The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index; 7 of the 35 sources its claims cite were checked for support.
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Open accessAbstract
The accumulation and extrusion of Ca 2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca 2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
7 of 35 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In chemical synapses, the dynamical balance of the Ca 2+ concentration is shaped by both influx via voltage-sensitive calcium channels (VSCC) and N-methyl-D-aspartate receptors (NMDAR) 42,43 , and extrusion via the plasma membrane Ca 2+ -ATPase (PMCA) and Na + /Ca 2+ exchanger (NCE) 43,44 that restores [Ca 2+ ]i to the basal concentration.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [42] Burgoyne, R. D. Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat. Rev. Neurosci. 8, 182–193 (2007). sourceExperimental — accuracy not yet measured
The “Changes in Ca2+ concentration” section says neuronal calcium can enter through voltage-gated calcium channels or NMDA receptors and identifies a resting intracellular concentration. It does not identify PMCA or the Na+/Ca2+ exchanger as the means of extrusion or say that they restore calcium to baseline. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [43] Clapham, D. E. Calcium signaling. Cell 131, 1047–1058 (2007). source
- Partly supportedcites [44] Catterall, W. A. & Few, A. P. Calcium channel regulation and presynaptic plasticity. Neuron 59, 882–901 (2008). sourceExperimental — accuracy not yet measured
The Introduction states that presynaptic voltage-gated calcium channels admit Ca2+ and that residual Ca2+ is cleared by a Ca2+-ATPase and a Na+/Ca2+ exchanger. It does not identify NMDARs as an influx route in this account or explicitly say these mechanisms restore intracellular calcium to its basal concentration. (LLM, conf=0.87)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The micro-devices represented by Fig. 1a have an area of 10µm×10µm and nano-devices with an area of 100nm×100nm exhibited similar switching behaviours (Supplementary Fig. S4).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“their operating characteristics were experimentally verified by demonstrating both short-and long-term plasticity, including mechanisms that have not been unambiguously demonstrated previously.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here we report such an emulator, which is a memristor based on metal atom diffusion and spontaneous nanoparticle formation, as determined by in situ high-resolution transmission electron microscopy (HRTEM) and nanoparticle dynamics simulations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An applied voltage above an apparent threshold abruptly switched the device to a conductance state limited by an external compliance current (Fig. 1b).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Repeatable and symmetric switching was demonstrated using wider bipolar voltage pulses with over a million switching cycles in Fig. 1d.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To demonstrate that the device spontaneously relaxed back to an insulating configuration upon removing the bias (without applying an opposite polarity voltage)”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The simulations for Ag in dielectric agreed well with the experimental HRTEM observations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under an applied pulse, the device exhibited threshold switching to a low resistance state after an incubation period τd, as shown in Fig. 3a.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 59
- Probably not a substantial claim. 9
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The calcium inside the postsynaptic membrane also plays important regulating roles in long-term potentiation and depression where Ca 2+ accumulation is necessary to regulate enzymes, i.e.Ca 2+ /calmodulindependent protein kinase II (CaMKII), calcineurin, and protein phosphatase 1 42,43,46,47 , which in turn triggers rapid and persistent modification of synaptic strengths by changing the number and”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [42] Burgoyne, R. D. Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat. Rev. Neurosci. 8, 182–193 (2007). sourceExperimental — accuracy not yet measured
The 'Different signals, diverse outcomes' section says postsynaptic Ca2+ signals can trigger LTP or LTD, and 'Ca2+ sensor proteins: calmodulin' links Ca2+/calmodulin to CaMKII and identifies calcineurin as a calmodulin target (page numbers unavailable). The paper does not establish the claimed pathway through protein phosphatase 1 or say that these enzymes persistently change synaptic strength by altering AMPAR number or conductance. Its References discuss possible AMPA-receptor endocytosis in LTD through hippocalcin, a different mechanism. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [43] Clapham, D. E. Calcium signaling. Cell 131, 1047–1058 (2007). source
- Partly supportedcites [46] Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004). sourceExperimental — accuracy not yet measured
In the passages headed “LTD Expression and Maintenance” (page unavailable), the review links LTP and LTD to AMPAR phosphorylation and identifies CaMKII as acting on an AMPAR phosphorylation site. Other passages describe postsynaptic Ca²⁺ as a trigger for LTD and report roles for calcineurin and protein phosphatase 1. The retrieved passages support changes in AMPAR surface expression, but do not establish the claim that these enzymes persistently change synaptic strength through altered AMPAR conductance. (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [47] Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Pinning can occur through interactions of the nanoparticles with impurities, the substrate and/or the ionic lattice 41 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Could not verifycites [41] Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016). sourceExperimental — accuracy not yet measured
The Noise spectra section discusses fluctuating atomic-scale contacts in memristors, while Methods mentions a substrate only as part of the device construction. Searches for nanoparticle pinning and interactions with impurities, substrates, or ionic lattices found no passage addressing the claim; our copy therefore does not verify it. The retrieved passages have no page numbers. (LLM, conf=0.76)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Moreover, a qualitatively similar result (although requiring considerably more computational resources) has been obtained when we modelled the interfacial energy as an interaction between particles 41 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Could not verifycites [41] Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) describes a Molecular Dynamics–Langevin simulation that reproduces a crossover in memristor noise spectra. Searches for interfacial energy, particle–particle interactions, and the claimed modelling comparison found no passage in our copy addressing them. (LLM, conf=0.75)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The material systems exhibiting a substantial relaxation were those with large wetting contact angles 32 , such as MgOx:Ag, SiOxNy:Ag, and HfOx:Ag in Fig. 1b, consistent with the reported psudoelasticity of silver nanoparticles 33 and the hypothesis that interfacial energy facilitates filament rupture in volatile switching [34][35][36] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [32] Stoneham, A. M. Systematics of metal–insulator interfacial energies: a new rule for wetting and strong catalyst-support interactions. Appl. Surf. Sci. 14, 249–259 (1983). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked — no full text of the sourcecites [33] Sun, J. et al. Liquid-like pseudoelasticity of sub-10-nm crystalline silver particles. Nat. Mater. 13, 1007–1012 (2014). source
- Not matchedcites [34][35][36]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Symmetric hysteresis loops were observed with the opposite polarity bias (Supplementary Fig. S3), showing that the threshold switching is unipolar in nature and significantly different from non-volatile drift-type memristors, especially in the OFF-switching process.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“These devices are similar to electrochemical metallization memory (ECM) [21][22][23][24][25][26] cells in terms of utilizing mobile species of noble metals, but they differ substantially in terms of the structural symmetry and operating voltage polarities, metal concentration and profile, and transient switching behaviour.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [21][22][23][24][25][26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“X-ray photoelectron spectroscopy (XPS) revealed that the Ag was metallic, which was further confirmed by HRTEM micrographs showing Ag nanocrystals in SiOxNy:Ag”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization,”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The resistance ratio between the conducting and insulating states was 5-orders of magnitude in SiOxNy:Ag and over 10-orders in HfOx:Ag devices, the highest reported in threshold switching devices so far [27][28][29][30] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [27][28][29][30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“The volatile switching had sharp turn-on slopes of ~10mV/decade in MgOx:Ag and SiOxNy:Ag, and an extraordinary ~1mV/decade in HfOx:Ag, the sharpest demonstrated to date [27][28][29][30] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [27][28][29][30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reliable threshold switching was observed under a 100nA compliance in ambient conditions with a typical relaxation time constant of ~11ms (Supplementary Fig. S5b-c).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“If a subsequent pulse arrives before particles are re-absorbed, i.e., if the time between pulses is shorter than the diffusion relaxation time, more particles are pushed into the gap between terminals resulting in a gradual increase in device conductance, similar to the paired-pulse facilitation (PPF) phenomenon in bio-synapses.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in an inflection of the device conductance due to excessive stimulation, capturing another synaptic behaviour, i.e., PPF followed by PPD (paired pulse depression).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Eventually, most of the nanoparticles have merged into larger clusters to minimize interfacial energy, and the high resistance state is re-established along with the original particle distribution almost restored (compare Fig. 4a1 and Fig. 4a4), leading to the observed volatility in Fig. 2.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
In the paper
“The above PPF and PPD demonstrations were realized with diffusive memristors only, resembling short-term plasticity 45 in synapses because any conductance change from the OFF state of diffusive memristors will vanish over time.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [45] Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Third, sequential high voltage pulses with a long enough interval (low frequency) may form a conducting bridge first, but before the next pulse arrives the bridge breaks and the particles are re-absorbed at the terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 3b, τr decreased as the ambient temperature increased, consistent with a diffusion activation energy of 0.27eV (inset of Fig. 3b)”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As soon as the power is turned off, the temperature drops, and the nanoparticles start to coalesce (Fig. 4a3), i.e. particles slowly diffuse to their minimum energy positions near the device terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When a voltage pulse is applied (Fig. 4a), the local temperature increases due to Joule heating and the potential is tilted by electric forces acting on particles with induced charge, both of which cause larger clusters to break up.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the nanoparticles become more uniformly distributed in the gap, the resistance drops, the current and temperature increase, and a positive feedback results in the formation of a conductive channel (Fig. 4a2).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A smaller ∆t corresponds to a smaller diffusive memristor resistance and results in a greater resistance change in the drift memristor and vice versa (Fig. 6d).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This important feature of bio-synapses, which has not been clearly demonstrated previously on two terminal devices, was predicted in Fig. 4b and observed experimentally in Fig. 5c.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The elongated cluster of nanoparticles that had likely formed the conductive bridge rapidly contracted from a length of 14.8nm to a circular profile with a diameter of 7.6nm by 5.7s, indicating Ostwald ripening 31 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [31] Voorhees, P. W. The theory of Ostwald ripening. J. Stat. Phys. 38, 231–252 (1985). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The device in its steady state shows PPD upon low frequency (196Hz) stimulation and experiences an increase in current (facilitation) once the stimulation frequency is raised (5000Hz).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Similar to Fig. 5b, a shorter tzero resulted in a greater increase in the conductance of the diffusive memristor and thus a larger voltage drop across the drift memristor, which thereby switched due to the voltage divider effect.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At 4.6s, the cluster reached a diameter of ~4.2nm.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A longer tzero resulted in a smaller increase in the diffusive memristor conductance and thus a smaller voltage drop across the drift memristor, leading to a smaller or non-detectable resistance change in the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Upon channel formation, the current jumped abruptly by several orders of magnitude, and then slowly increased further under bias as the channel thickened.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The drift memristor is not switched by the first spike, because the majority of the voltage drops across the diffusive memristor and turns it ON first.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, when the initial voltage pulse is applied, electric field-assisted diffusion pumps some of the Ag-particles out of the 'left' cluster and they start to bridge terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the pre-spike appears before the postspike, the drift memristor conductance increases (potentiation).If the pre-spike follows the post-spike, depression occurs.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the combined element, the resistance of the diffusive memristor in its OFF state is much larger than that of the drift memristor, while the resistance of its ON state is much smaller than that of the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observed a delay time of ~2s during which the measured current was <5nA and Ag nanoparticles formed in the gap region”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The microscopic nature of both the threshold switching and relaxation of the diffusive memristor is revealed for the first time by in situ HRTEM and explained by nanoparticle dynamics simulation.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“curve) induced by a voltage pulse (red curve) shows a delay in the response and a gradual relaxation towards the low conducting state, consistent with the experimental threshold switching property in Fig. 3a.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When the post-spike precedes the pre-spike, the device is reset (depressed), and when the prespike precedes the post-spike, the device is set (potentiated).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“As one can see from Fig. 4, the relaxation occurs on time scales of the order 10-20 1/κ, thus, the simulated system is clearly governed by diffusion rather than the temperature relaxation 19 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“from Fig 4b we conclude that the conductance reached 25% of the maximum after 4 pulses and about 75% after 7 pulses.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Device current response (blue) to multiple dependent plasticity (SRDP and true STDP) behaviour of a combined device consisting of a diffusive and a drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The long low voltage pulse in each spike turns the diffusive memristor ON, and the short high voltage pulse switches the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The device did not fail during the measurement, demonstrating high robustness.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Highly non-linear, fast, and repeatable threshold switching behaviours of diffusive memristors”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The device requires a finite delay time to turn ON and has a finite relaxation time before it goes to the high resistance state after the switching pulse is removed.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For long tzero, the change in the conductance of the diffusive memristor is lower (see Fig. 5b), resulting in a lower weight change of the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the external biasing is removed at 5.0s, the filament starts to deform, shrinking to a round spherical nanocluster implying an interfacial energy driven diffusion mechanism.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this range the characteristic single hopping time scale 𝑒𝑒 𝑤𝑤 𝑝𝑝 /𝑘𝑘 𝐵𝐵 𝑇𝑇 changed from about 2/κ to 6/κ and the diffusion relaxation time (estimated as a characteristic jump time multiplied by the number of jumps to reach the interfacial minimum near the device terminal) was about 20/κ to 60/κ.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“CMOS circuits have been employed to mimic synaptic Ca 2+ dynamics, but three-terminal devices bear limited resemblance to bio-counterparts at the mechanism level and require significant numbers and complex circuits to simulate synaptic behavior [1][2][3] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [1][2][3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Various types of memristors based on ionic drift (drift-type memristor) [4][5][6][7][8] have recently been utilized for this purpose in neuromorphic architectures [9][10][11][12][13][14][15] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [4][5][6][7][8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [9][10][11][12][13][14][15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Recently, Lu's group adopted second-order drift memristors to approximate the Ca 2+ dynamics of chemical synapses by utilizing thermal dissipation 19 or mobility decay 20 , which successfully demonstrated STDP with non-overlapping spikes and other synaptic functions”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This approach features repeatability and simplicity, but the significant differences of the dynamical response from actual synapses limit the fidelity and variety of desired synaptic functions.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Similar issues also exist in MOS-based memristor emulators [16][17][18] , although they are capable of simulating a variety of synaptic functions including spike-timing-dependent plasticity (STDP).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [16][17][18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although qualitative synaptic functionality has been demonstrated, the fast switching and non-volatility of drift memristors optimized for memory applications do not faithfully replicate the nature of plasticity.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Ca 2+ dynamics is responsible for initiating both short-and long-term plasticity of synapses, forming the basis of memory and learning [42][43][44] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [42][43][44]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The particular shape of neither the interfacial potential nor the pinning energy profile are essential; the only property that matters is that the potential 𝑈𝑈 has two different energy scales and the weaker pinning potential has many wells (for example it can be random with fluctuating 𝑤𝑤 𝑝𝑝 and 𝑖𝑖 𝑝𝑝 ).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the dynamics of the diffusive memristor provides an intrinsic timing mechanism for the combined element, the spike-ratedependent plasticity and STDP do not require complex pulse engineering or spike overlapping.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Ag dynamics of the diffusive memristors functionally resemble the synaptic Ca 2+ behavior in chemical synapses and lead to a direct and natural emulation of multiple synaptic functions for both short-term and long-term plasticity, such as PPF, PPD, PPD following PPF, SRDP and STDP.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to providing a synapse emulator, the diffusive memristor can also serve as a selector with a large transient nonlinearity that is critical for the operation of a large crossbar array as a neural network.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The electrical bias together with diffusion is responsible for switching to a high conducting state and depletion of nanoparticles at one of the device terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This substantially reduces the complexity of both circuit and algorithm design and enables lowenergy operations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The stronger fluctuations support larger Joule heating and vice versa, breaking up the large Ag clusters to form a string of smaller nanoparticles and thus a conductive path between the terminals, as observed experimentally in the HRTEM studies.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“diffusion into the gap region between Ag nanoclusters with field assistance and clearance of Ag filament from the gap region by the interfacial energy or mechanical stress once the voltage signal is removed, which is a close emulation of Ca 2+ dynamics.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Equations ( 4) and ( 5) are sufficient to describe the memristor dynamics subject to the current through the device, which is controlled by the electric circuit in which it is embedded.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“programming is at the instrument noise level (<100nA) for the current measurement range used.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These quantities can be easily obtained from both simulations (averaged over 30 realizations) and experimental I-V hysteresis loops, thus directly linking the units in the simulations and the experiments.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The first one depends on temperature, potential profile, and electric drift, thus, this time scale changes depending on voltage pulse intensity and temperature.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
“For the state-dependent resistance we assume sequential electron tunnelling: first from the input terminal to the nearest metallic nanoparticle, and then from this nanoparticle to the next one and so on with the last tunnelling event to the output terminal.”
The authors state this as an assumption.
The tunnelling resistance amplitude is taken to be identical for every island.
“R 𝑡𝑡 the tunneling resistance amplitude (assumed the same for all islands)”
The authors state this as an assumption.
Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
“we employed an overdamped Langevin equation for each mobile metallic nanoparticle trapped by a potential 𝑈𝑈 and subject to a random force 𝜉𝜉 𝑖𝑖 , the magnitude of which is determined by the device local temperature 𝑇𝑇,”
The authors state this as an assumption.
The simulation model omits redox reactions.
“The model results here did not include redox reactions, although they can be added in order to more closely resemble the electrochemical models proposed previously [21][22][23][24][25][26] .”
The authors state this as an assumption.
The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
“The potential 𝑈𝑈 = 𝑈𝑈 𝐼𝐼 + 𝑈𝑈 𝑝𝑝 has two energy scales -the interfacial energy 𝑈𝑈 𝐼𝐼 responsible for formation of large metallic clusters near the device terminals and a weaker nanoparticlepinning energy 𝑈𝑈 𝑝𝑝 with many smaller wells between the electrodes.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The decay of the conductance is considered to arise from the surface diffusion of Ag caused by the Gibbs -Thomson effect 41 -45 .”
From Enhanced analog synaptic behavior of SiNx/a-Si bilayer memristors through Ge implantation (2020) · cites this paper as [42]
Experimental — accuracy not yet measuredIn the citing paper
“In contrast to ref. 34 we have also taken into account the memristor self-capacitance, which is critically important to describe the delay time distributions.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [34]
Citations
Cites
Calcium's Role in Mechanotransduction during Muscle Development
Damm, T. B., & Egli, M. (2014) · Cellular Physiology and Biochemistry · doi:10.1159/000356667 · Cited by 14,552
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
Clapham, D. E. (2007) · Cell · doi:10.1016/j.cell.2007.11.028 · Cited by 4,175
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Malenka, R. C., & Bear, M. F. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.012 · Cited by 3,968
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
Kwon, D.-H., Kim, K. M., Jang, J. H., Jeon, J. M., Lee, M. H., Kim, G. H., Li, X.-S., Park, G.-S., Lee, B., Han, S., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2009.456 · Cited by 2,092
Flagged first, then by citations
Research works printed but not linked: 2
- Jo, S. H., Kumar, T., Narayanan, S., Lu, W. D. & Nazarian, H. Electron Devices Meeting (IEDM) 6.7.1–6.7.4 (IEEE International, 2014).
- Luo, Q. et al. Electron Devices Meeting (IEDM) 10.14.11–10.14.14 (IEEE International, 2015).
Cited by
Sorted by citations
Items 201–300 of 2,351
Reconfigurable Logic‐in‐Memory and Multilingual Artificial Synapses Based on 2D Heterostructures
Xiong, X., Kang, J., Hu, Q., Gu, C., Gao, T., Li, X., & Wu, Y. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201909645 · Cited by 158
Van der Waals heteroepitaxial AZO/NiO/AZO/muscovite (ANA/muscovite) transparent flexible memristor
Le, V. Q., Do, T., Retamal, J. R. D., Shao, P., Lai, Y.-H., Wu, W., He, J., Chueh, Y., & Chu, Y. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.10.042 · Cited by 158
Lead-free monocrystalline perovskite resistive switching device for temporal information processing
Mao, J., Zheng, Z. P., Xiong, Z., Huang, P., Ding, G., Wang, R., Wang, Z., Yang, J., Zhou, Y., Zhai, T., et al. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.104616 · Cited by 159
Self-citationNonstationary distributions and relaxation times in a stochastic model of memristor
Викторович, А. Н., Safonov, A. V., Krichigin, A. V., Kharcheva, A. A., Dubkov, A. A., Valenti, D., Guseinov, D. V., Belov, A. I., Mikhaylov, A. N., Carollo, A. C. M., et al. (2020) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ab684a · Cited by 159
A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing
Deng, X., Wang, S., Liu, Y., Zhong, N., He, Y., Peng, H., Xiang, P., & Duan, C. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202101099 · Cited by 158
2D Metal–Organic Framework Nanosheets with Time‐Dependent and Multilevel Memristive Switching
Ding, G., Wang, Y., Zhang, G., Zhou, K., Zeng, K., Li, Z.-X., Zhou, Y., Zhang, C., Chen, X., & Han, S. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201806637 · Cited by 152
Self-citationUltrathin Conformable Organic Artificial Synapse for Wearable Intelligent Device Applications
Jang, S., Jang, S., Lee, E.-H., Kang, M., Wang, G., & Kim, T. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b12092 · Cited by 159
Wang, C., Tang, D., Lin, H., Yu, F., & Sun, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122513 · Cited by 156
“The memristor is considered to be an ideal component for modeling neural synapses due to its memory, non-volatile and low power consumption (Chua, 2015; Wang et al., 2017; Yang et al., 2023c).”
Emerging Opportunities for 2D Semiconductor/Ferroelectric Transistor‐Structure Devices
Luo, Z., Yang, M., Liu, Y., & Alexe, M. (2021) · Advanced Materials · doi:10.1002/adma.202005620 · Cited by 154
Memristor Crossbars with 4.5 Terabits-per-Inch-Square Density and Two Nanometer DimensionPreprint
Pi, S., Li, C., Jiang, H., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09848 · Cited by 154
Self-citationTransparent and Flexible Inorganic Perovskite Photonic Artificial Synapses with Dual‐Mode Operation
Yang, L., Singh, M., Shen, S., Chih, K., Liu, S., Wu, C., Chu, C., & Lin, H. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202008259 · Cited by 155
Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires
Milano, G., Porro, S., Valov, I., & Ricciardi, C. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800909 · Cited by 150
ABO 3 multiferroic perovskite materials for memristive memory and neuromorphic computing
Sun, B. T., Zhou, G., Sun, L., Zhao, H., Chen, Y., Yang, F., Zhao, Y. X., & Song, Q. (2021) · Nanoscale Horizons · doi:10.1039/d1nh00292a · Cited by 154
Electrolyte‐Gated Synaptic Transistor with Oxygen Ions
Huang, H., Ge, C., Zhang, Q., Liu, C., Du, J., Li, J., Wang, C., Gu, L., Yang, G., & Jin, K. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201902702 · Cited by 149
Nonlinearity in memristors for neuromorphic dynamic systems
Yang, K., Joshua Yang, J., Huang, R., & Yang, Y. (2022) · Small Science · doi:10.1002/smsc.202100049 · Cited by 151
Self-citationDastgeer, G., Nisar, S., Rasheed, A., Akbar, K., Chavan, V. D., Kim, D., Wabaidur, S. M., Zulfiqar, M. W., & Eom, J. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.109106 · Cited by 154
Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device
Tian, H., Cao, X., Xie, Y., Yan, X., Kostelec, A., DiMarzio, D., Chang, C., Zhao, L., Wu, W., Tice, J. B., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b03033 · Cited by 152
Memristor devices for neural networks
Jeong, H., & Shi, L. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aae223 · Cited by 151
“Among emerging non-volatile memory devices, some devices such as MRAM, PRAM, RRAM, and FeRAM emerged as memristor devices later [15, 54 – 59].”
BackgroundMethodA Threshold Switching Selector Based on Highly Ordered Ag Nanodots for X‐Point Memory Applications
Hua, Q., Wu, H., Gao, B., Zhao, M., Li, Y., Li, X., Hou, X., Chang, M., Zhou, P., & Qian, H. (2019) · Advanced Science · doi:10.1002/advs.201900024 · Cited by 148
Memristor modeling: challenges in theories, simulations, and device variability
Gao, L., Ren, Q., Sun, J., Han, S., & Zhou, Y. (2021) · Journal of Materials Chemistry C · doi:10.1039/d1tc04201g · Cited by 156
Technology and Integration Roadmap for Optoelectronic Memristor
Wang, J., Ilyas, N., Ren, Y., Ji, Y., Li, S., Li, C., Liu, F., Gu, D., & Ang, K. (2023) · Advanced Materials · doi:10.1002/adma.202307393 · Cited by 158
Stochastic Memory Devices for Security and Computing
Carboni, R., & Ielmini, D. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900198 · Cited by 150
“soon after the set transition with a retention time ranging from few μs to few ms [133], [134],”
BackgroundAbbas, H., Abbas, Y., Hassan, G., Sokolov, A. S., Jeon, Y.-R., Ku, B., Kang, C. J., & Choi, C. (2020) · Nanoscale · doi:10.1039/d0nr02335c · Cited by 144
Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse
Sung, S. H., Kim, T. J., Shin, H., Im, T. H., & Lee, K. J. (2022) · Nature Communications · doi:10.1038/s41467-022-30432-2 · Cited by 146
“The volatile TS layer is fabricated by co-sputtering of Ag and SiO 2 targets after the deposition of an inert Au electrode to suppress the evolution of nonvolatile fi laments 30 .”
MethodPCMO RRAM for Integrate-and-Fire Neuron in Spiking Neural Networks
Lashkare, S., Chouhan, S., Chavan, T., Bhat, A., Kumbhare, P., & Ganguly, U. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2805822 · Cited by 145
Competing memristors for brain-inspired computing
Kim, S. J., Kim, S., & Jang, H. W. (2020) · iScience · doi:10.1016/j.isci.2020.101889 · Cited by 150
Zhang, F., Li, C., Li, Z., Dong, L., & Zhao, J. (2023) · Microsystems & Nanoengineering · doi:10.1038/s41378-023-00487-2 · Cited by 155
Self-citationEmergent dynamics of neuromorphic nanowire networks
Álvarez, A. D., Higuchi, R., Sanz‐Leon, P., Marcus, I., Shingaya, Y., Stieg, A. Z., Gimzewski, J. K., Kuncic, Z., & Nakayama, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-51330-6 · Cited by 141
“The physical mechanism whereby Ag filaments decay could be related to spontaneous clustering of the Ag filament whenever voltage difference is absent, in order to minimize surface energy of the filaments 47 .”
BackgroundPanda, P., Aketi, S. A., & Roy, K. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00653 · Cited by 149
“…for real-time hardware implementation on several emerging and non-von Neumann architectures (Pérez-Carrasco et al., 2010; Linares-Barranco et al., 2011; Ankit et al., 2017; Sengupta and Roy, 2017; van de Burgt et al., 2017; Wang et al., 2017), also suffers from accuracy/scalability deficiencies.”
Chen, L., Ren, M., Zhou, J., Zhou, X., Liu, F., Di, J., Xue, P., Li, C., Li, Q., Li, Y., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2407971121 · Cited by 143
A Survey on Neuromorphic Computing: Models and Hardware
Shrestha, A., Fang, H., Mei, Z., Rider, D. P., Wu, Q., & Qiu, Q. (2022) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2022.3166331 · Cited by 152
Self-citationEssential Characteristics of Memristors for Neuromorphic Computing
Chen, W., Song, L., Wang, S., Zhang, Z., Wang, G., Hu, G., & Gao, S. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200833 · Cited by 148
Memristive and CMOS Devices for Neuromorphic Computing
Milo, V., Malavena, G., Compagnoni, C. M., & Ielmini, D. (2020) · Materials · doi:10.3390/ma13010166 · Cited by 145
“Among these local learning rules, spike-timing-dependent plasticity (STDP) and spike-rate-dependent plasticity (SRDP) have received intense investigation for hardware implementation of brain-inspired SNNs.”
BackgroundMethodInfluentialPhase-change memtransistive synapses for mixed-plasticity neural computations
Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P., & Sebastian, A. (2022) · Nature Nanotechnology · doi:10.1038/s41565-022-01095-3 · Cited by 140
Memristor-based neural networks: a bridge from device to artificial intelligence
Cao, Z., Sun, B. T., Zhou, G., Mao, S., Zhu, S., Zhang, J., Ke, C., Zhao, Y. X., & Shao, J. (2023) · Nanoscale Horizons · doi:10.1039/d2nh00536k · Cited by 138
Ferroic tunnel junctions and their application in neuromorphic networks
Guo, R., Lin, W., Yan, X., Venkatesan, T., & Chen, J. (2020) · Applied Physics Reviews · doi:10.1063/1.5120565 · Cited by 138
Chekol, S. A., Menzel, S., Ahmad, R. W., Waser, R. M., & Hoffmann‐Eifert, S. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202111242 · Cited by 146
Artificial Neural Pathway Based on a Memristor Synapse for Optically Mediated Motion Learning
He, K., Liu, Y., Yu, J., Guo, X., Wang, M., Zhang, L., Wan, C., Wang, T., Zhou, C., & Chen, X. (2022) · ACS Nano · doi:10.1021/acsnano.2c03100 · Cited by 136
Seok, H., Lee, D., Son, S., Choi, H., Kim, G., & Kim, T. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202300839 · Cited by 153
Novel Electronics for Flexible and Neuromorphic Computing
Lee, H. E., Park, J. H., Kim, T., Im, D., Shin, J. H., Kim, D. H., Mohammad, B. S., Kang, I., & Lee, K. J. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201801690 · Cited by 137
Flexible artificial nociceptor using a biopolymer-based forming-free memristor
Ge, J., Zhang, S., Liu, Z., Xie, Z., & Pan, S. (2019) · Nanoscale · doi:10.1039/c8nr08721k · Cited by 133
Midya, R., Wang, Z., Asapu, S., Joshi, S., Li, Y., Zhuo, Y., Song, W., Jiang, H., Upadhyay, N. K., Rao, M., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900060 · Cited by 135
Self-citationFerroelectric Second-Order Memristor
Mikheev, V., Chouprik, A. A., Lebedinskiǐ, Y. Y., Zarubin, S., Matveyev, Y., Kondratyuk, E., Kozodaev, M. G., Markeev, A. M., Zenkevich, A. V., & Negrov, D. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b08189 · Cited by 135
Band‐tailored van der Waals heterostructure for multilevel memory and artificial synapse
Wang, Y., Zheng, Y., Gao, J. L., Jin, T., Li, E., Lian, X., Pan, X., Han, C., Chen, H., & Chen, W. (2021) · InfoMat · doi:10.1002/inf2.12230 · Cited by 139
Two‐dimensional In 2 Se 3 : A rising advanced material for ferroelectric data storage
Huang, Y., Chen, N., Li, Z., Wang, X., Sun, H., Zhang, S., & Li, X. (2022) · InfoMat · doi:10.1002/inf2.12341 · Cited by 135
Self-citationRoadmap to neuromorphic computing with emerging technologies
Mehonić, A., Ielmini, D., Roy, K., Mutlu, O., Kvatinsky, S., Serrano‐Gotarredona, T., Linares-Barranco, B., Spiga, S., Savel'ev, S. E., Balanov, A. G., et al. (2024) · APL Materials · doi:10.1063/5.0179424 · Cited by 163
Self-citationGuo, R., Zhou, Y., Wu, L., Wang, Z., Lim, Z., Yan, X., Lin, W., Wang, H., Yoong, H. Y., Chen, S., et al. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b01469 · Cited by 131
Self-citationIsmail, M., Chand, U., Mahata, C., Nebhen, J., & Kim, S. (2021) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2021.04.025 · Cited by 131
Progress of Materials and Devices for Neuromorphic Vision Sensors
Cho, S. W., Jo, C., Kim, Y., & Park, S. K. (2022) · Nano-Micro Letters · doi:10.1007/s40820-022-00945-y · Cited by 133
Kamaei, S., Liu, X., Saeidi, A., Wei, Y., Gastaldi, C., Brügger, J., & Ionescu, A. M. (2023) · Nature Electronics · doi:10.1038/s41928-023-01018-7 · Cited by 133
Li, B., Liu, Y., Wan, C., Liu, Z., Wang, M., Qi, D., Yu, J., Cai, P., Meng, X., Zeng, Y., et al. (2018) · Advanced Materials · doi:10.1002/adma.201706395 · Cited by 131
Zhang, Z., Li, T., Wu, Y., Jia, Y., Tan, C., Xu, X., Wang, G., Lv, J., Zhang, W., He, Y., et al. (2018) · Advanced Materials · doi:10.1002/adma.201805769 · Cited by 133
Self-citationChoi, S., Jang, S., Moon, J., Kim, J. C., Jeong, H. Y., Jang, P.-H., Lee, K., & Wang, G. (2018) · NPG Asia Materials · doi:10.1038/s41427-018-0101-y · Cited by 130
Gaussian synapses for probabilistic neural networks
Sebastian, A., Pannone, A., Radhakrishnan, S. S., & Das, S. (2019) · Nature Communications · doi:10.1038/s41467-019-12035-6 · Cited by 128
“Recent years have, therefore, witnessed innovation in analog devices such as the memristors 12 – 14 , coupled oscillators 15 , and various targeted components 16 – 18 , which can emulate neural spiking, neural transmission, and neural plasticity and hence can be used as computational primitives in…”
Xue, F., He, X., Wang, Z., Retamal, J. R. D., Chai, Z., Jing, L., Zhang, C., Fang, H., Chai, Y., Jiang, T., et al. (2021) · Advanced Materials · doi:10.1002/adma.202008709 · Cited by 130
Self-citationAdvanced Data Encryption using 2D Materials
Wen, C., Li, X., Zanotti, T., Puglisi, F. M., Shi, Y., Saiz, F., Antidormi, A., Roche, S., Zheng, W., Liang, X., et al. (2021) · Advanced Materials · doi:10.1002/adma.202100185 · Cited by 131
Analog‐Type Resistive Switching Devices for Neuromorphic Computing
Zhang, W., Gao, B., Tang, J., Li, X., Wu, W., Qian, H., & Wu, H. (2019) · physica status solidi (RRL) - Rapid Research Letters · doi:10.1002/pssr.201900204 · Cited by 131
Short-term synaptic plasticity in emerging devices for neuromorphic computing
Li, C., Zhang, X., Chen, P., Zhou, K., Yu, J., Wu, G., Xiang, D., Jiang, H., Wang, M., & Liu, Q. (2023) · iScience · doi:10.1016/j.isci.2023.106315 · Cited by 143
“77 In detail, Ag ions can be driven by the electric field and interfacial energy, which is similar to the Ca 2+ movement influenced by concentration gradients and exchangers.”
BackgroundSelf-citationAbbas, Y., Jeon, Y., Sokolov, A. S., Kim, S., Ku, B., & Choi, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-19575-9 · Cited by 127
MXene Ti3C2 memristor for neuromorphic behavior and decimal arithmetic operation applications
Wang, K., Chen, J., & Yan, X. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105453 · Cited by 129
Bao, H., Liu, W., & Chen, M. (2019) · Nonlinear Dynamics · doi:10.1007/s11071-019-04890-1 · Cited by 125
Ultrafast and Low-Power 2D Bi 2 O 2 Se Memristors for Neuromorphic Computing Applications
Dong, Z., Hua, Q., Xi, J., Shi, Y., Huang, T., Dai, X., Niu, J., Wang, B., Wang, Z. L., & Hu, W. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c00322 · Cited by 130
Self-citationSarkar, D., Tao, J., Wang, W., Lin, Q., Yeung, M., Ren, C., & Kapadia, R. (2018) · ACS Nano · doi:10.1021/acsnano.7b08272 · Cited by 126
Wang, Y., Yang, J., Wang, Z., Chen, J., Yang, Q., Lv, Z., Zhou, Y., Zhai, Y., Li, Z.-X., & Han, S. (2019) · Small · doi:10.1002/smll.201805431 · Cited by 126
Self-citationImperfection-enabled memristive switching in van der Waals materials
Li, M., Liu, H., Zhao, R., Yang, F., Chen, M., Zhuo, Y., Zhou, C., Wang, H., Lin, Y., & Yang, J. J. (2023) · Nature Electronics · doi:10.1038/s41928-023-00984-2 · Cited by 131
Self-citationTransporting an ionic-liquid/water mixture in a conical nanochannel: a nanofluidic memristor
Sheng, Q., Xie, Y., Li, J., Wang, X., & Xue, J. (2017) · Chemical Communications · doi:10.1039/c7cc01047h · Cited by 128
Lee, T., Hwang, H.-G., Woo, J., Kim, D., Kim, T., & Nahm, S. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b04550 · Cited by 126
Yan, X., Li, X., Zhou, Z., Zhao, J., Wang, H., Wang, J., Zhang, L., Ren, D., Zhang, X., Chen, J., et al. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b04443 · Cited by 125
2D Layered Materials for Memristive and Neuromorphic Applications
Wang, C., Wang, C., Meng, F., Wang, P., Wang, S., Liang, S., & Miao, F. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201901107 · Cited by 129
Li, S., Li, B., Feng, X.-W., Chen, L., Li, Y., Huang, L., Fong, X.-Y., & Ang, K. (2021) · npj 2D Materials and Applications · doi:10.1038/s41699-020-00190-0 · Cited by 95
Mou, X., Tang, J., Lyu, Y., Zhang, Q., Yang, S., Xu, F., Liu, W., Xu, M., Zhou, Y. M., Sun, W. B., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh0648 · Cited by 127
Neuromorphic Computing with Memristor Crossbar
Zhang, X., Huang, A., Hu, Q., Xiao, Z., & Chu, P. K.-H. (2018) · physica status solidi (a) · doi:10.1002/pssa.201700875 · Cited by 126
Organic small molecule-based RRAM for data storage and neuromorphic computing
Mu, B., Hsu, H., Kuo, C., Han, S., & Zhou, Y. (2020) · Journal of Materials Chemistry C · doi:10.1039/d0tc02116d · Cited by 123
Advances in Memristor-Based Neural Networks
Xu, W., Wang, J., & Yan, X. (2021) · Frontiers in Nanotechnology · doi:10.3389/fnano.2021.645995 · Cited by 132
“Due to scalability, low power operation, non-volatile features, and small on-chip area, memristors are good candidates for artificial synaptic devices to mimicking the LTP, LTD, and STDP behaviors (Jo et al., 2010; Ohno et al., 2011; Kim et al., 2015; Wang et al., 2017; Yan et al., 2017).”
BackgroundFull imitation of synaptic metaplasticity based on memristor devices
Wu, Q., Wang, H., Luo, Q., Banerjee, W., Cao, J., Zhang, X., Wu, F., Liu, Q., Li, L., & Liu, M. (2018) · Nanoscale · doi:10.1039/c8nr00222c · Cited by 124
Self-citationRecent Developments of Optoelectronic Synaptic Devices Based on Metal Halide Perovskites
Hao, D., Yang, Z., Huang, J. D., & Shan, F. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202211467 · Cited by 127
John, R. A., Tiwari, N., Patdillah, M. I. B., Kulkarni, M. R., Tiwari, N. G., Basu, J., Bose, S. K., Ankit, A., Yu, C. J., Nirmal, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17870-6 · Cited by 128
Organic synaptic devices for neuromorphic systems
Sun, J., Fu, Y., & Wan, Q. C. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aacd99 · Cited by 124
Avalanches and criticality in self-organized nanoscale networks
Mallinson, J. B., Shirai, S., Acharya, S. K., Bose, S. K., Galli, E., & Brown, S. A. (2019) · Science Advances · doi:10.1126/sciadv.aaw8438 · Cited by 126
Xu, X., Cho, E. J., Bekker, L., Talin, A. A., Lee, E., Pascall, A. J., Worsley, M. A., Zhou, J. J., Cook, C. C., Kuntz, J. D., et al. (2022) · Advanced Science · doi:10.1002/advs.202200629 · Cited by 120
2D MoS 2 -Based Threshold Switching Memristor for Artificial Neuron
Dev, D., Krishnaprasad, A., Shawkat, M. S., et al. (2020) · IEEE Electron Device Letters · doi:10.1109/led.2020.2988247 · Cited by 120
“Upon removal of the electric field, the Ag filament spontaneously turns into a sphere for minimizing the interfacial energy [15].”
BackgroundKafraj, M. S., Parastesh, F., & Jafari, S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109782 · Cited by 119
Memristor‐Based Intelligent Human‐Like Neural Computing
Wang, S., Song, L., Chen, W., Wang, G., Hao, E., Li, C., Hu, Y., Pan, Y., Nathan, A., Hu, G., et al. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200877 · Cited by 123
Sokolov, A. S., Ali, M., Riaz, R., Abbas, Y., Ko, M. J., & Choi, C. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201807504 · Cited by 118
Advancements in 2D layered material memristors: unleashing their potential beyond memory
Nirmal, K. A., Kumbhar, D. D., Kesavan, A. V., Dongale, T. D., & Kim, T. G. (2024) · npj 2D Materials and Applications · doi:10.1038/s41699-024-00522-4 · Cited by 129
Nanoscale resistive switching devices for memory and computing applications
Lee, S. H., Zhu, X., & Lu, W. D. (2020) · Nano Research · doi:10.1007/s12274-020-2616-0 · Cited by 119
Electroluminescent synaptic devices with logic functions
Zhao, S., Ni, Z., Tan, H., Wang, Y., Jin, H., Nie, T., Xu, M., Pi, X., & Yang, D. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.10.018 · Cited by 118
Highly Compact Artificial Memristive Neuron with Low Energy Consumption
Zhang, Y., He, W., Wu, Y., Huang, K., Shen, Y., Su, J., Wang, Y., Zhang, Z., Ji, X., Li, G., et al. (2018) · Small · doi:10.1002/smll.201802188 · Cited by 117
Self-citationFlexible 3D memristor array for binary storage and multi‐states neuromorphic computing applications
Wang, T., Meng, J., Chen, L., Zhu, H., Sun, Q., Ding, S., Bao, W., & Zhang, D. W. (2021) · InfoMat · doi:10.1002/inf2.12158 · Cited by 118
Stretchable and conformable synapse memristors for wearable and implantable electronics
Yang, M., Zhao, X., Tang, Q., Cui, N., Wang, Z., Tong, Y., & Liu, Y. (2018) · Nanoscale · doi:10.1039/c8nr05336g · Cited by 118
Self-citationVariability and Yield in h‐BN‐Based Memristive Circuits: The Role of Each Type of Defect
Shen, Y., Zheng, W., Zhu, K., Xiao, Y., Wen, C., Liu, Y., Jing, X., & Lanza, M. (2021) · Advanced Materials · doi:10.1002/adma.202103656 · Cited by 115
Bian, J., Cao, Z., & Zhou, P. (2021) · Applied Physics Reviews · doi:10.1063/5.0067352 · Cited by 119
Versatile memristor for memory and neuromorphic computing
Guo, T., Pan, K., Jiao, Y., et al. (2022) · Nanoscale Horizons · doi:10.1039/d1nh00481f · Cited by 115
Functional Materials for Memristor‐Based Reservoir Computing: Dynamics and Applications
Zhang, G., Qin, J., Zhang, Y., Gong, G., Xiong, Z., Ma, X., Lv, Z., Zhou, Y., & Han, S. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202302929 · Cited by 120
Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device
Ilyas, N., Li, D., Li, C., Jiang, X., Jiang, Y., & Li, W. (2020) · Nanoscale Research Letters · doi:10.1186/s11671-020-3249-7 · Cited by 114
“The results show that when high-frequency pulses are applied, which pumps more Ag atoms in the SiO x layer until a conducting bridge is formed across the TE and BE, achieving a saturation level [22].”
BackgroundResultInfluentialCharge disproportionate molecular redox for discrete memristive and memcapacitive switching
Goswami, S., Rath, S. P., Thompson, D., Hedström, S., Annamalai, M., Pramanick, R., Ilic, B., Sarkar, S., Hooda, S., Nijhuis, C. A., et al. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0653-1 · Cited by 113
Self-citationEmerging Iontronic Neural Devices for Neuromorphic Sensory Computing
Dai, S., Liu, X., Liu, Y., Xu, Y., Zhang, J., Wu, Y., Cheng, P., Xiong, L., & Huang, J. D. (2023) · Advanced Materials · doi:10.1002/adma.202300329 · Cited by 115
Cascade-heterogated biphasic gel iontronics for electronic-to-multi-ionic signal transmission
Chen, W., Zhai, L., Zhang, S., Zhao, Z., Hu, Y., Xiang, Y., Liu, H., Xu, Z., Jiang, L., & Wen, L. (2023) · Science · doi:10.1126/science.adg0059 · Cited by 114
Yoon, C., Lee, J. H., Lee, S., Jeon, J. H., Jang, J. T., Kim, D. H., Kim, Y. H., & Park, B. H. (2017) · Nano Letters · doi:10.1021/acs.nanolett.6b05308 · Cited by 113
First-Principles Prediction of Potentials and Space-Charge Layers in All-Solid-State Batteries
Swift, M. W., & Qi, Y. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.167701 · Cited by 113
Metadata
- Authors
- Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., Wu, Q., Barnell, M., Li, G.-L., Xin, H. L., Williams, R. S., Xia, Q., & Yang, J. J.
- Affiliations
- University of Massachusetts Amherst (US)
- Hewlett-Packard (United States) (US)
- U.S. Air Force Research Laboratory Information Directorate (US)
- Brookhaven National Laboratory (US)
- Corresponding author
- Wang, Zhongrui
- Joshi, Saumil
- Yang, J. Joshua
- ORCID
- Venue
- Nature Materials
- Published
- 2016-09-26
- Volume
- 16
- Issue
- 1
- Pages
- 101-108
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W2526646482
- Cited by
- 2,358
- References
- 52
- Citations by year
- 2026: 182 · 2025: 264 · 2024: 259 · 2023: 260 · 2022: 273 · 2021: 274 · 2020: 297 · 2019: 246 · 2018: 210 · 2017: 89 · 2016: 2
- FWCI
- 83.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (1253073, DE-SC0012704)
- U.S. Department of Energy (DE‐SC0012704, SC0012704.)
- Center for Hierarchical Manufacturing, National Science Foundation
- Office of Science (DE-SC-0012704, SC0012704)
- U.S. Air Force (FA9550, FA9550-12-1)
- University of Massachusetts Amherst
- Intelligence Advanced Research Projects Activity (2014‐14080800008)
- Division of Electrical, Communications and Cyber Systems
- Air Force Office of Scientific Research (FA9550-, -15-2-, FA9550-12, FA9550‐12‐1‐0038, FA9550‐12‐1)
- Brookhaven National Laboratory (SC0012704, DE- SC0012704)
- Air Force Research Laboratory (DE-SC0012704, FA8750-15-2- 0044, FA9550)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- memristors, silver diffusion, synaptic plasticity, calcium dynamics, neuromorphic computing, short-term plasticity, long-term plasticity, synaptic emulation, interfacial energy minimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
