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.
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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
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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
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- 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
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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
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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
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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
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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.
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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
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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
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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.
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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.
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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
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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
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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.
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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.
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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
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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.
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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.
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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
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Training and operation of an integrated neuromorphic network based on metal-oxide memristors
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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).
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Items 1,001–1,100 of 2,351
Qin, L., Guan, P., Shao, J., Xiao, Y., Yu, Y., Su, J., Zhang, C., Li, Y., Liu, S., Li, P., et al. (2025) · Nature Nanotechnology · doi:10.1038/s41565-025-02013-z · Cited by 26
Self-citationNeuromorphic iontronic devices based on soft ionic conductors
Wang, L., Jiao, Y., Zhang, H., Liu, Y., Zhang, Y., Wu, P., & Xiao, K. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00580a · Cited by 30
Organic Electrochemical Transistors for Neuromorphic Devices and Applications
Xiang, K., Song, J., Liu, H., Chen, J., & Yan, F. (2026) · Advanced Materials · doi:10.1002/adma.202515532 · Cited by 41
Dananjaya, P. A., Loy, D. J., Chow, S. C. W., & Lew, W. S. (2019) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00446 · Cited by 27
In-materio neuromimetic devices: dynamics, information processing and pattern recognition
Przyczyna, D., Zawal, P., Mazur, T., Strzelecki, M., Gentili, P. L., & Szacilowski, K. T. (2020) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ab82b0 · Cited by 26
Hylton, T. L. (2020) · Entropy · doi:10.3390/e22030256 · Cited by 28
Silicon-based optoelectronic synaptic devices*
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Multiterminal Ionic Synaptic Transistor With Artificial Blink Reflex Function
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“In recent years, tremendous efforts have been paid to studies of neuromorphic devices, including synaptic transistors [7]–[9] and memristors [10]–[12].”
Designing artificial sodium ion reservoirs to emulate biological synapses
Kim, D., & Lee, J. (2020) · NPG Asia Materials · doi:10.1038/s41427-020-00243-2 · Cited by 26
Zeng, T., Zou, X., Wang, Z., Yu, G., Yang, Z. C., Rong, H., Zhang, C., Xu, H., Lin, Y., Zhao, X., et al. (2021) · Small · doi:10.1002/smll.202006662 · Cited by 25
Self-citationCrossbar Array of Artificial Synapses Based on Ferroelectric Diodes
Seufert, L., Amiri, M. H., Gkoupidenis, P., & Asadi, K. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100558 · Cited by 27
Kim, J., Song, S., Kim, H., Yoo, G., Cho, S. S., Kim, J., Park, S. K., & Kim, Y. (2022) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2022.163873 · Cited by 25
Brain-Inspired Spiking Neural Network Using Superconducting Devices
Zhang, H., Chen, G., Xu, C., Gong, G., & Lu, H. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3089328 · Cited by 25
“Because of the simple structure, superconducting devices, such as magnetic Josephson junctions (MJJs) and quantum phase-slip junctions (QPSJs), have higher spiking speeds and lower power consumption than memristors [9], [10], [11].”
BackgroundIn-materio reservoir working at low frequencies in a Ag 2 S-island network
Nakajima, M., Minegishi, K., Shimizu, Y., Usami, Y., Tanaka, H., & Hasegawa, T. (2022) · Nanoscale · doi:10.1039/d2nr01439d · Cited by 27
Electroforming-Free Y2O3Memristive Crossbar Array with Low Variability
Kumar, S., Das, M., Htay, M. T., Sriram, S., & Mukherjee, S. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.2c00472 · Cited by 26
TWO DISCRETE MEMRISTIVE CHAOTIC MAPS AND ITS DSP IMPLEMENTATION
Han, X., Mou, J., Lu, J., et al. (2023) · Fractals · doi:10.1142/s0218348x23401047 · Cited by 25
Khemnani, M., Tripathi, B., Thakkar, P., et al. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c01038 · Cited by 25
Atomically Thin CBRAM Enabled by 2-D Materials: Scaling Behaviors and Performance Limits
Dong, Z., Zhao, H., DiMarzio, D., Han, M., Zhang, L., Tice, J. B., Wang, H., & Guo, J. H. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2830328 · Cited by 25
Silicon‐Based Hybrid Optoelectronic Devices with Synaptic Plasticity and Stateful Photoresponse
Chen, Y., Wei, Q., Yin, J., Xia, Y., & Liu, Z. G. (2018) · Advanced Electronic Materials · doi:10.1002/aelm.201800242 · Cited by 26
Controlled Nonvolatile Transition in Polyoxometalates‐Graphene Oxide Hybrid Memristive Devices
Chen, X., Zhu, X. B., Zhang, S., et al. (2018) · Advanced Materials Technologies · doi:10.1002/admt.201800551 · Cited by 25
Duan, N., Li, Y., Chiang, H., Huang, S.-P., Yin, K., Chen, J., Yang, C.-I., Chang, T., & Miao, X. (2018) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.8b00060 · Cited by 26
Engineering Oxygen Migration for Homogeneous Volume Resistive Switching in 3‐Terminal Devices
Gonzalez‐Rosillo, J. C., Ortega‐Hernandez, R., Arndt, B., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800629 · Cited by 25
Wang, W., Bricalli, A., Laudato, M., Ambrosi, E., Covi, E., & Ielmini, D. (2018) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2018.8614556 · Cited by 26
Conductive metallic filaments dominate in hybrid perovskite-based memory devices
Huang, Y., Zhao, Z., Wang, C., et al. (2019) · Science China Materials · doi:10.1007/s40843-019-9433-4 · Cited by 25
Park, H., & Kim, J. (2020) · Advanced Electronic Materials · doi:10.1002/aelm.201901410 · Cited by 25
Park, Y., Kim, M., & Lee, J. (2020) · Carbon · doi:10.1016/j.carbon.2020.04.096 · Cited by 25
Ng, S. E., Vishwanath, S. K., Yang, J., Periyal, S. S., Nirmal, A., Jamaludin, N. F., John, R. A., & Mathews, N. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202300540 · Cited by 25
Self-citationJeon, Y., Akinwande, D., & Choi, C. (2024) · Nanoscale Horizons · doi:10.1039/d3nh00571b · Cited by 26
Ultra-low power IGZO optoelectronic synaptic transistors for neuromorphic computing
Zhu, L. Q., Li, S., Lin, J., Zhao, Y., Wan, X., Sun, H., Yan, S., Xu, Y., Yu, Z., Tan, C. L., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-3966-8 · Cited by 25
Nano device fabrication for in-memory and in-sensor reservoir computing
Lin, Y., Chen, X., Zhang, Q., You, J., Xu, R., Wang, Z., & Sun, L. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad88bb · Cited by 29
“In 2019, a study expanded on previous work by employing a different type of memristor known as the diffusive memristor [97] for RC [96].”
Self-citationSun, R., Hou, Z., Chen, Q., Zhu, B., Zhu, C., Huang, P., Hu, Z., Zhen, L., Zhou, F., Xu, C., et al. (2024) · Advanced Materials · doi:10.1002/adma.202409017 · Cited by 24
Organic memristive devices for perceptron applications
Battistoni, S., Erokhin, V. V., & Iannotta, S. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aac98f · Cited by 24
Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
Rahmani, M. K., Kim, M., Hussain, F. A., Abbas, Y., Ismail, M., Hong, K., Mahata, C., Choi, C., Park, B., & Kim, S. (2020) · Nanomaterials · doi:10.3390/nano10050994 · Cited by 24
“In the nervous sys em, a sy apse makes a neuron to pass an electrical or chemical si nal to an ther n uron [16,17] by using neurotransmitters.”
BackgroundOptical and oxide modification of CsFAMAPbIBr memristor achieving low power consumption
Guan, H., Sha, J., Zhang, Z., Xiong, Y., Dong, X., Bao, H., Sun, K., Wang, S., & Wang, Y. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.162096 · Cited by 24
Liu, P., Jia, C., & Zhang, W. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.1c01163 · Cited by 24
Kim, D.-W., Kim, H.-J., Lee, W., Kim, K., Lee, S., Bae, J., Kang, I. M., Kim, K., & Jang, J. (2022) · Materials · doi:10.3390/ma15051943 · Cited by 24
Halide perovskite based synaptic devices for neuromorphic systems
Beom, K., Fan, Z., Li, D., & Newman, N. (2022) · Materials Today Physics · doi:10.1016/j.mtphys.2022.100667 · Cited by 25
Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device
Mishchenko, M. A., Bolshakov, D. I., Lukoyanov, V., Korolev, D. S., Belov, A. I., Guseinov, D. V., Matrosov, V., Kazantsev, V., & Mikhaylov, A. N. (2022) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac79de · Cited by 24
“Recently, the high-frequency portion of SRDP was implemented using the in-series connected diffusive and bipolar (drift) memristors [25].”
Ali, A., Abbas, H., Hussain, M., Jaffery, S. H. A., Hussain, S., Choi, C., & Jung, J. (2022) · Applied Materials Today · doi:10.1016/j.apmt.2022.101554 · Cited by 24
A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules
Florini, D., Gandolfi, D., Mapelli, J., Benatti, L., Pavan, P., & Puglisi, F. M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3202501 · Cited by 25
“Several works have shown the possibility to implement STDP with either analog [8], [18], [19], [20] or binary [21] memristive devices and few others tackled the problem of other long-term plasticity rules [22], [23], [24], [25], [26], [27].”
BackgroundMethodInfluentialPolarization-Dominated Internal Timing Mechanism in a Ferroelectric Second-Order Memristor
Li, W., Fan, Z. H., Huang, Q., Rao, J., Cui, B., Chen, Z., Lin, Z., Yan, X., Tian, G., Tao, R., et al. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.19.014054 · Cited by 24
Research Process of Carbon Dots in Memristors
Hao, H., Yan, L., Wang, M., Cao, Y., He, J., & Yang, Y. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202201195 · Cited by 25
In-materio reservoir computing based on nanowire networks: fundamental, progress, and perspective
Fang, R.-R., Zhang, W.-Y., Ren, K., Zhang, P.-W., Xu, X., Wang, Z., & Shang, D.-S. (2023) · Materials Futures · doi:10.1088/2752-5724/accd87 · Cited by 19
“When the applied voltage is removed, the Ag ions on the cathode side with much higher concentration than the anode side will spontaneously diffuse to the anode to reach the equilibrium state under the driving of the nanobattery effect including theminimization of interfacial energy and internal electro-dynamic potenti…”
BackgroundSelf-citationVoltage-controlled reverse filament growth boosts resistive switching memory
Belmonte, A., Celano, U., Chen, Z., Radhaskrishnan, J., Redolfi, A., Clima, S., Richard, O., Bender, H., Kar, G. S., Vandervorst, W., et al. (2018) · Nano Research · doi:10.1007/s12274-018-1983-2 · Cited by 25
“O. R. and H. B. performed TEM and S. C. has contributed with ab-initio simulations.”
BackgroundImproved Learning Experience Memristor Model and Application as Neural Network Synapse
Zhang, X., & Long, K. (2019) · IEEE Access · doi:10.1109/access.2019.2894634 · Cited by 24
“of real memristors with excellent performance have been designed, such as memristors with diffusive dynamics as synaptic emulators [18].”
Ta2O5-y-based ReRAM device with annealing-free Ag:ZrNx-based bilayer selector device
Kim, D., Park, J. H., Jeon, D. S., et al. (2020) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2020.157261 · Cited by 24
Electrolyte-Dependent Modification of Resistive Switching in Anodic Hafnia
Zrinski, I., Mardare, C. C., Jinga, L.-I., et al. (2021) · Nanomaterials · doi:10.3390/nano11030666 · Cited by 24
“Additionally, memristors are used as building blocks for neuromorphic architectures [4] such as artificial synapses [5] and logic circuits due to the possibility of multilevel switching to implement a fuzzy behavior [6–8].”
MethodRoom‐Temperature‐Integrated Flexible Diffusive Oxide Memristors for Artificial Nociceptive Systems
Li, J. C., Liu, Z. C., Xia, Y. H., Liu, X., Yang, H. X., Ma, Y. X., & Wang, Y. L. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416635 · Cited by 26
Bou, A., Gonzales, C., Boix, P. P., Vaynzof, Y., Guerrero, A., & Bisquert, J. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c03132 · Cited by 26
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Tunable Resistive Switching Enabled by Malleable Redox Reaction in the Nano-Vacuum Gap
Ji, X., Wang, C., Lim, K. G., Tan, C. C., Chong, T. C., & Zhao, R. J. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b02498 · Cited by 23
2D Resistive Switching Based on Amorphous Zinc–Tin Oxide Schottky Diodes
Branca, N. C., Deuermeier, J., Martins, J., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900958 · Cited by 23
On synapse intelligence emulated in a self-formed artificial synaptic network
Bannur, B., & Kulkarni, G. U. (2020) · Materials Horizons · doi:10.1039/d0mh01037e · Cited by 23
Coexistence of Long‐Term Memory and Short‐Term Memory in an SiNx‐Based Memristor
Choi, J., & Kim, S. (2020) · physica status solidi (RRL) - Rapid Research Letters · doi:10.1002/pssr.202000357 · Cited by 23
Liang, A., Zhang, J., Wang, F., Jiang, Y., Hu, K., Shan, X., Liu, Q., Song, Z., & Zhang, K. (2020) · Nanotechnology · doi:10.1088/1361-6528/abd3c7 · Cited by 24
“The conductance change ( Δ ω ) is de fi ned as [ 43 ] ( equation ( 2 )) : where the G 0 is the conductance of cells before applying sweeps and G t is the conductance after the pluses [ 44, 45 ] .”
TSSM: Three-State Switchable Memristor Model Based on Ag/TiOx Nanobelt/Ti Configuration
Ji, X., Qi, D., Dong, Z., et al. (2021) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127421300202 · Cited by 23
Maraj, J. J., Najem, J. S., Ringley, J. D., Weiss, R., Rose, G. S., & Sarles, S. A. (2021) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.1c00610 · Cited by 27
Site‐Specific Regulated Memristors via Electron‐Beam‐Induced Functionalization of HfO2
Kumar, M., Ahn, Y. H., Iqbal, S., Kim, U., & Seo, H. (2021) · Small · doi:10.1002/smll.202105585 · Cited by 23
Xiong, X., Wang, X., Hu, Q., Li, X., & Wu, Y. (2022) · iScience · doi:10.1016/j.isci.2022.103947 · Cited by 23
“…of the brain in combining computation and storage with energy efficiency into a compact space focus on new computing architectures that distinguish from traditional von Neumann architectures (Boybat et al., 2018; van de Burgt et al., 2017; von Neumann, 2012; Wang et al., 2017; Zidan et al., 2018).”
BackgroundIsaac, S. D., Njitacke, Z., Tsafack, N., Tchapga, C. T., & Kengne, J. (2022) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-022-00472-2 · Cited by 21
Intrinsic variation effect in memristive neural network with weight quantization
Park, J., Song, M. S., Youn, S., Kim, T., Kim, S., Hong, K., & Kim, H. (2022) · Nanotechnology · doi:10.1088/1361-6528/ac7651 · Cited by 23
“In order to ease this issue, neuromorphic computing systems constructing a hardware-based neural network have been proposed by utilizing memory technologies [ 9 – 11 ] .”
CMOS-compatible self-aligned 3D memristive elements for reservoir computing systems
Koroleva, A. A., Kuzmichev, D. S., Kozodaev, M. G., Zabrosaev, I. V., Korostylev, E. V., & Markeev, A. M. (2023) · Applied Physics Letters · doi:10.1063/5.0138218 · Cited by 23
Tian, Q., Chen, X., Zhao, X., Wang, Z., Lin, Y., Tao, Y., Xu, H., & Liu, Y. (2023) · Applied Physics Letters · doi:10.1063/5.0142742 · Cited by 24
Self-citationAqueous chemimemristor based on proton-permeable graphene membranes
Wang, Y., Seki, T., Gkoupidenis, P., Chen, Y., Nagata, Y., & Bonn, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314347121 · Cited by 27
“Memristive devices, whose resistance/conductance states depend on the history of applied electrical signals states, can display such synaptic functionality and have been important candidates for neuromorphic computing (4, 5, 11–15).”
BackgroundWang, Y., Guo, D., Jiang, J., Wang, H., Shang, Y., Zheng, J., Huang, R., Li, W., & Wang, S. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c18053 · Cited by 24
On the Limits of Scalpel AFM for the 3D Electrical Characterization of Nanomaterials
Chen, S., Jiang, L., Buckwell, M., Jing, X., Ji, Y., Grustan‐Gutierrez, E., Hui, F., Shi, Y., Rommel, M., Paskaleva, A., et al. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201802266 · Cited by 23
3D Convolutional Neural Network based on memristor for video recognition
Liu, J., Li, Z., Tang, Y., Hu, W., & Wu, J. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.12.005 · Cited by 25
Self-citationWu, L., Wang, Z., Wang, B., Chen, Q., Bao, L., Yu, Z., Yang, Y., Ling, Y., Qin, Y., Tang, K., et al. (2020) · Nanoscale · doi:10.1039/d0nr08012h · Cited by 23
Self-citationLow-power memristors based on layered 2D SnSe/graphene materials
Wang, H., Yu, T., Zhao, J., et al. (2021) · Science China Materials · doi:10.1007/s40843-020-1586-x · Cited by 23
Manipulation of the electrical behaviors of Cu/MXene/SiO2/W memristor
Wang, Y., Liu, X., Chen, Y., Xu, W., Liang, D., Gao, F., Zhang, M., Samanta, S., Gong, X., Lian, X., et al. (2019) · Applied Physics Express · doi:10.7567/1882-0786/ab4233 · Cited by 23
Hu, J., Bao, H., Xu, Q., Chen, M., & Bao, B. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114993 · Cited by 23
Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems
Zhang, X., Wang, C., Pi, X., Li, B., Ding, Y., Yu, H., Sun, J., Wang, P., Chen, Y., Wang, Q., et al. (2025) · Advanced Materials · doi:10.1002/adma.202418108 · Cited by 23
Self-citationEdge of Chaos Theory Unveils the First and Simplest Ever Reported Hodgkin–Huxley Neuristor
Ascoli, A., Demirkol, A. Ş., Messaris, I., Ntinas, V. G., Prousalis, D. A., Slesazeck, S., Mikolajick, T., Corinto, F., Bonnin, M., Gilli, M., et al. (2025) · Advanced Electronic Materials · doi:10.1002/aelm.202400789 · Cited by 24
Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation
Kim, S., Zhang, H., Rivera‐Sierra, G., Fenollosa, R., Rubio‐Magnieto, J., & Bisquert, J. (2025) · Journal of Applied Physics · doi:10.1063/5.0257462 · Cited by 23
Self-organizing neuromorphic nanowire networks as stochastic dynamical systems
Milano, G., Michieletti, F., Pilati, D., Ricciardi, C., & Miranda, E. (2025) · Nature Communications · doi:10.1038/s41467-025-58741-2 · Cited by 25
“In our case, the switching mechanism is volatile-type since Ag conductive fi laments can spontaneously break down after formation with a characteristic lifetime that depends on the experienced electrical excitation, as discussed in previous works 35 – 37 .”
An energy-efficient and high-throughput bitwise CNN on sneak-path-free digital ReRAM crossbar
Ni, L., Liu, Z., Song, W., Yang, J. J., Yu, H., Wang, K., & Wang, Y. (2017) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped.2017.8009177 · Cited by 22
Self-citationDynamic structure-properties characterization and manipulation in advanced nanodevices
Xu, H., Wu, X., Tian, X., Li, J., Chu, J., & Sun, L. (2019) · Materials Today Nano · doi:10.1016/j.mtnano.2019.100042 · Cited by 22
Low power non-volatile memory switching in monolayer-rich 2D WS2 and MoS2 devices
Mihai, C., Sava, F., Galca, A. C., et al. (2020) · AIP Advances · doi:10.1063/1.5140717 · Cited by 22
Wang, Y., Shen, D., Liang, Y., Zhao, Y., Chen, X., Zhou, L., Zhang, M., Xu, J., Liu, X., Hu, E., et al. (2021) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2021.106123 · Cited by 22
Koroleva, A. A., Kozodaev, M. G., Lebedinskiǐ, Y. Y., & Markeev, A. M. (2021) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac26f6 · Cited by 23
Spray pyrolysis deposited iron tungstate memristive device for artificial synapse application
Patil, A. R., Dongale, T. D., Kamat, R. K., & Rajpure, K. Y. (2021) · Materials Today Communications · doi:10.1016/j.mtcomm.2021.102900 · Cited by 22
Memristive System Based Image Processing Technology: A Review and Perspective
Ji, X., Dong, Z., Zhou, G., Lai, C. S., Yan, Y., & Qi, D. (2021) · Electronics · doi:10.3390/electronics10243176 · Cited by 25
Near-infrared photonic artificial synapses based on organic heterojunction phototransistors
Sha, X., Cao, Y., Meng, L., Yao, Z., Gao, Y., Zhou, N., Zhang, Y., Chu, P. K.-H., & Li, J. (2022) · Applied Physics Letters · doi:10.1063/5.0083925 · Cited by 22
Sahu, D. P., Park, K., Han, J., & Yoon, T. (2022) · APL Materials · doi:10.1063/5.0090425 · Cited by 22
Tan, Z., de Rojas, J., Martins, S., Lopeandía, A. F., Quintana, A., Cialone, M., Herrero‐Martín, J., Meersschaut, J., Vantomme, A., Costa-Krämer, J. L., et al. (2022) · Materials Horizons · doi:10.1039/d2mh01087a · Cited by 22
A true random number generator based on double threshold-switching memristors for image encryption
Bian, J., Tao, Y., Wang, Z., Dong, Y., Li, Z., Zhao, X., Lin, Y., Xu, H., & Liu, Y. (2023) · Applied Physics Letters · doi:10.1063/5.0145875 · Cited by 23
Self-citationAnisotropic mass transport enables distinct synaptic behaviors on 2D material surface
Yang, Z., Yang, Z., Liu, L., Li, X., Li, J., Xiong, C., Mai, X., Tong, H., Li, Y., Xue, K., et al. (2023) · Materials Today Electronics · doi:10.1016/j.mtelec.2023.100047 · Cited by 22
A LIF Neuron With Adaptive Firing Frequency Based on the GaSe Memristor
Wang, H., Xu, Y., Yang, R., & Miao, X. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3288508 · Cited by 21
Hard and soft excitation of oscillations in memristor-based oscillators with a line of equilibria
Александрович, К. И., Вадивасова, Т. Е., & Семенов, В. В. (2017) · Nonlinear Dynamics · doi:10.1007/s11071-017-3628-5 · Cited by 22
“Novel papers provide an encouraging pathway toward neuromorphic computing [1,51,96,111], particularly in fuzzy logic systems [118].”
BackgroundShen, W., Kumari, N., Gibson, G. A., Jeon, Y., Henze, D., Silverthorn, S., Bash, C. E., & Kumar, S. (2018) · Journal of Applied Physics · doi:10.1063/1.5009439 · Cited by 21
Zhang, B., Wang, C., Wang, L., & Chen, Y. (2018) · Journal of Materials Chemistry C · doi:10.1039/c8tc00524a · Cited by 22
Yin, J., Zeng, F., Wan, Q., Sun, Y., Hu, Y., Liu, J., Li, G., & Pan, F. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b09793 · Cited by 21
Self-citationPhysically Transient Memristive Synapse With Short-Term Plasticity Based on Magnesium Oxide
Sun, J. W., Wang, H., Wang, Z., Song, F., Zhu, Q., Dang, B., Gao, H., Yang, M., Ma, X., & Hao, Y. (2019) · IEEE Electron Device Letters · doi:10.1109/led.2019.2904752 · Cited by 23
“Such an interesting phenomenon can be explained as follows [3]: when the interval time between pluses is shorter than the relaxation time of Ag atoms in the device, Ag atoms will be gradually Fig.”
BackgroundMethodSelf-citationFeng, Y., Gao, X., Zhong, Y., Wu, J.-L., Xu, J., & Wang, S. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900022 · Cited by 22
Ultralow operation voltages of a transparent memristor based on bilayer ITO
Wang, Y., Hu, L., Wei, X., & Zhuge, F. (2020) · Applied Physics Letters · doi:10.1063/5.0008416 · Cited by 22
Sleep-Dependent Memory Consolidation in a Neuromorphic Nanowire Network
Li, Q., Álvarez, A. D., Tang, D., Higuchi, R., Shingaya, Y., & Nakayama, T. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c11157 · Cited by 23
Bifunctional Silver-Doped ZnO for Reliable and Stable Organic–Inorganic Hybrid Perovskite Memory
Park, Y., & Lee, J. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c18038 · Cited by 22
Jeong, H., Han, S., Park, S., Kim, T. R., Bae, J., Jang, T., Cho, Y., Seo, S., Jeong, H.-J., Park, S., et al. (2025) · Nature Electronics · doi:10.1038/s41928-024-01318-6 · Cited by 23
“The memristor device also emulates various biological synaptic plasticity, including short-term potentiation and/or short-term depression 38 , paired-pulse facilitation and/or paired-pulse depression 39 , spike-voltage-dependent plasticity and spike-rate-dependent plasticity 40 (Supplementary Fig.”
Synaptic Function in Memristor Devices for Neuromorphic Circuit Applications
Bisquert, J., Shim, W., Kim, S., & Linares-Barranco, B. (2025) · Advanced Electronic Materials · doi:10.1002/aelm.202400903 · Cited by 23
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 ›
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