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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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.
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- 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.
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- 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
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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
“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.
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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 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
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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 701–800 of 2,351
Yao, Y., Huang, X., Peng, S., Zhang, D., Shi, J., Yu, G., Liu, Q., & Jin, Z. H. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800887 · Cited by 46
Highly transparent solid-state artificial synapse based on oxide memristor
Singh, R., Kumar, M., Iqbal, S., Kang, H., Park, J., & Seo, H. (2020) · Applied Surface Science · doi:10.1016/j.apsusc.2020.147738 · Cited by 46
Spring, J., Sediva, E., Hood, Z. D., Gonzalez‐Rosillo, J. C., O’Leary, W., Kim, K. J., Carrillo, A. J., & Rupp, J. L. M. (2020) · Small · doi:10.1002/smll.202003224 · Cited by 45
150 nm × 200 nm Cross‐Point Hexagonal Boron Nitride‐Based Memristors
Yuan, B., Liang, X., Zhong, L., Shi, Y., Palumbo, F., Chen, S., Hui, F., Jing, X., Villena, M. A., Jiang, L., et al. (2020) · Advanced Electronic Materials · doi:10.1002/aelm.201900115 · Cited by 45
Solid‐State Iontronic Devices: Mechanisms and Applications
Li, T., & Xiao, K. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202200205 · Cited by 45
Ye, F., Kiani, F., Huang, Y., & Xia, Q. (2022) · Advanced Materials · doi:10.1002/adma.202204778 · Cited by 44
Self-citationResearch Progress of Neural Synapses Based on Memristors
Li, Y., Su, K., Chen, H., Zou, X., Wang, C., Man, H., Liu, K., Xi, X., & Li, T. (2023) · Electronics · doi:10.3390/electronics12153298 · Cited by 45
“The second-order memristor models based on different materials have been discovered successively, such as TiO 2 :Ag [56], WO 3−x [57], SrTiO 3 [58], and InGaZnO [59].”
Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations
Mohanan, K. U. (2024) · Nanomaterials · doi:10.3390/nano14060527 · Cited by 48
“Z. Wang et al. [98] fabricated a new class of memristors for synaptic emulation based on the diffusion dynamics of Ag nanoparticles embedded inside the switching layer (SiO x N y ).”
BackgroundJi, Y., Wang, L., Long, Y., Wang, J., Zheng, H., Yu, Z. G., Zhang, Y., & Ang, K. (2025) · Nature Communications · doi:10.1038/s41467-025-62306-8 · Cited by 51
“The subsequent current decay observed under the applied read voltage re fl ects a passive relaxation process, attributed to the spon-taneous dissolution of thin Ag conductive fi laments 41 , and thus the energy dissipated during this period does not contribute to switching.”
Resistive switching and transport characteristics of an all-carbon memristor
Raeber, T. J., Zhao, Z. C., Murdoch, B. J., McKenzie, D. R., McCulloch, D. G., & Partridge, J. G. (2018) · Carbon · doi:10.1016/j.carbon.2018.04.045 · Cited by 43
Milo, V., Pedretti, G., Carboni, R., Calderoni, A., Ramaswamy, N., Ambrogio, S., & Ielmini, D. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2818978 · Cited by 44
“The hybrid CMOS/RRAM structure of our synapse has some key advantages compared to the previous approaches in which SRDP was demonstrated by specific RRAM materials, such as Ag2S [28], Ag/AgInSbTe/Ag [29], Pt/FeOx /Pt [30], Al/TiO2−x /AlOx /Al [31], and Ag/SiON [32].”
BackgroundMethodSelective UV‐Gating Organic Memtransistors with Modulable Levels of Synaptic Plasticity
Zhong, Y., Gao, X., Xu, J., Sirringhaus, H., & Wang, S. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900955 · Cited by 44
Electric Field- and Current-Induced Electroforming Modes in NbO x
Nandi, S. K., Nath, S. K., El-Helou, A. E., et al. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b20252 · Cited by 44
Nanomaterials and their applications on bio-inspired wearable electronics
Li, J., Xin, M., Ma, Z., Shi, Y., & Pan, L. (2021) · Nanotechnology · doi:10.1088/1361-6528/abe6c7 · Cited by 43
“Such devices include second-order memristors, which rely on an additional state variable such as temperature or ion mobility to encode relative timing information [ 216, 217 ] , and volatile diffusive memristors whose conductive fi lament ruptures spontaneously [ 218 ] .”
Bipolar Resistive Switching in Junctions of Gallium Oxide and p-type Silicon
Almadhoun, M. N., Speckbacher, M., Olsen, B. C., Luber, E. J., Sayed, S. Y., Tornow, M., & Buriak, J. M. (2021) · Nano Letters · doi:10.1021/acs.nanolett.1c00539 · Cited by 45
Thickness-dependent monochalcogenide GeSe-based CBRAM for memory and artificial electronic synapses
Ali, A., Abbas, H., Hussain, M., Jaffery, S. H. A., Hussain, S., Choi, C., & Jung, J. (2021) · Nano Reseach · doi:10.1007/s12274-021-3793-1 · Cited by 32
Chromatic Plasmonic Polarizer-Based Synapse for All-Optical Convolutional Neural Network
Guo, J., Liu, Y., Lin, L., Li, S., Cai, J., Chen, J., Huang, W., Lin, Y., & Xu, J. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c02194 · Cited by 44
Wu, E., Wang, Y., Huo, S., Xu, J., Sheng, M. W., Liu, H., Zhong, L. S., Junfeng, G., Xie, Y., & Pan, C. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202518764 · Cited by 49
Characteristics and transport mechanisms of triple switching regimes of TaOx memristor
Lian, X., Wang, M., Rao, M., Yan, P., Yang, J. J., & Miao, F. (2017) · Applied Physics Letters · doi:10.1063/1.4982593 · Cited by 43
Self-citationOrganic electronic synapses with pinched hystereses based on graphene quantum-dot nanocomposites
Choi, H. Y., Wu, C., Bok, C. H., & Kim, T. W. (2017) · NPG Asia Materials · doi:10.1038/am.2017.133 · Cited by 43
Developing near-infrared quantum-dot light-emitting diodes to mimic synaptic plasticity
Zhao, S., Wang, Y., Huang, W., Jin, H., Huang, P., Wang, H., Wang, K., Li, D., Xu, M., Yang, D., et al. (2019) · Science China Materials · doi:10.1007/s40843-019-9437-9 · Cited by 44
Synaptic behaviors in flexible Au/WO /Pt/mica memristor for neuromorphic computing system
Zhang, L., Tang, Z., Yao, D., Fan, Z.-Y., Hu, S., Sun, Q., Tang, X., Jiang, Y., Guo, X., Huang, M., et al. (2022) · Materials Today Physics · doi:10.1016/j.mtphys.2022.100650 · Cited by 44
Memristive tonotopic mapping with volatile resistive switching memory devices
Milozzi, A., Ricci, S., & Ielmini, D. (2024) · Nature Communications · doi:10.1038/s41467-024-47228-1 · Cited by 44
“The main features of biological mechanisms such as spike-timing dependent plasticity (STDP) and Hebbian learning have been demonstrated through these approaches 42 – 44 .”
Improvement of Device Reliability by Introducing a BEOL-Compatible TiN Barrier Layer in CBRAM
Cao, R., Liu, S., Liu, Q., Zhao, X.-L., Wang, W., Zhang, X.-M., Wu, F., Wu, Q., Wang, Y., Lv, H., et al. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2746738 · Cited by 32
Self-citationElectrically-generated memristor based on inkjet printed silver nanoparticles
Yoon, K. J., Han, J., Moon, D., Seol, M., Meyyappan, M., Kim, H., & Hwang, C. S. (2019) · Nanoscale Advances · doi:10.1039/c9na00329k · Cited by 43
Redox transistors for neuromorphic computing
Fuller, E. J., Li, Y., Bennet, C., Keene, S. T., Melianas, A., Agarwal, S., Marinella, M. J., Salleo, A., & Talin, A. A. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942285 · Cited by 44
Fabricating Molybdenum Disulfide Memristors
Qiao, Y., Hirtz, T., Wu, F., Deng, G., Li, X., Zhi, Y., Tian, H., Yang, Y., & Ren, T. (2019) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00655 · Cited by 42
Kook, G., Jeong, S., Kim, M. K., Lee, S., Choi, N., & Lee, H. J. (2020) · Advanced Materials Technologies · doi:10.1002/admt.201900991 · Cited by 44
Memristive Devices for Neuromorphic Applications: Comparative Analysis
Erokhin, V. V. (2020) · BioNanoScience · doi:10.1007/s12668-020-00795-1 · Cited by 43
Adda, C., Lee, M., Kalcheim, Y., et al. (2022) · Physical Review X · doi:10.1103/physrevx.12.011025 · Cited by 42
Volatile and Nonvolatile Memory Operations Implemented in a Pt/HfO₂/Ti Memristor
Wu, L., Liu, H., Lin, J., & Wang, S. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3061033 · Cited by 42
“STP is achieved in a diffusive Ag-in-oxide memristor [17] with a temporal response during and after stimulation similar to that of the synaptic Ca2+ dynamics, but the relaxation time of STP is shorter than 5 μs, and STDP is realized by combining with other nonvolatile memristors.”
Tang, Z., Zhu, R., Hu, R., Chen, Y., Wu, E. Q., Wang, H., He, J. L., Huang, Q., & Chang, S. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3003377 · Cited by 41
High Performance Full-Inorganic Flexible Memristor with Combined Resistance-Switching
Zhu, Y., Liang, J. K., Mathayan, V., Nyberg, T., Primetzhofer, D., Shi, X., & Zhang, Z. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c02264 · Cited by 41
Dias, C., Castro, D., Aroso, M., Ventura, J. O., & Aguiar, P. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.2c00198 · Cited by 45
Zhang, L., Tang, Z., Fang, J., Jiang, X., Jiang, Y., Sun, Q., Fan, J., Tang, X., & Zhong, G. (2022) · Applied Surface Science · doi:10.1016/j.apsusc.2022.154718 · Cited by 39
Oh, J., Kim, S., Lee, C., Cha, J., Yang, S. Y., Im, S. G., Park, C., Jang, B. C., & Choi, S. (2023) · Advanced Materials · doi:10.1002/adma.202300023 · Cited by 40
Lai, X.-C., Tang, Z., Fang, J., Feng, L., Yao, D., Zhang, L., Jiang, Y., Liu, Q., Tang, X., Zhou, Y., et al. (2024) · Materials Horizons · doi:10.1039/d4mh00064a · Cited by 42
Hu, J., Li, H., Zhang, Y., Zhou, J., Zhao, Y., Xu, Y., & Yu, B. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c02658 · Cited by 46
Graphene/h-BN Heterostructures for Vertical Architecture of RRAM Design
Huang, Y., & Lee, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-08939-2 · Cited by 41
A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing
Zhuo, Y., Midya, R., Song, W., Wang, Z., Asapu, S., Rao, M., Lin, P., Jiang, H., Xia, Q., Williams, R. S., et al. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100696 · Cited by 41
Self-citationYe, W., Lin, J., Zhang, X., Lian, Q., Liu, Y., Wang, H., Wu, S., Chen, H., & Guo, T. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107525 · Cited by 45
Polyoxometalate Accelerated Cationic Migration for Reservoir Computing
Zhang, G., Xiong, Z., Gong, Y., Zhu, Z., Lv, Z., Wang, Y., Yang, J., Xing, X., Wang, Z., Qin, J., et al. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202204721 · Cited by 44
Self-citationUltrasound: A new strategy for artificial synapses modulation
Yuan, J., Li, Y., Wang, M., Huang, X., Zhang, T., Xue, K., Yuan, J., Ou‐Yang, J., Yang, X., Miao, X., et al. (2024) · InfoMat · doi:10.1002/inf2.12528 · Cited by 40
Yoo, H., Lee, Y. H., Lee, M., & Sun, J. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00245 · Cited by 51
Battery-like artificial synapses
Yang, J. J., & Xia, Q. (2017) · Nature Materials · doi:10.1038/nmat4870 · Cited by 40
Self-citationComputing of temporal information in spiking neural networks with ReRAM synapses
Wang, W. J., Pedretti, G., Milo, V., Carboni, R., Calderoni, A., Ramaswamy, N., Spinelli, A. S., & Ielmini, D. (2018) · Faraday Discussions · doi:10.1039/c8fd00097b · Cited by 39
Frequency driven organic memristive devices for neuromorphic short term and long term plasticity
Battistoni, S., Erokhin, V. V., & Iannotta, S. (2018) · Organic Electronics · doi:10.1016/j.orgel.2018.11.033 · Cited by 40
Wu, Z., Zhao, X., Yang, Y., Wang, W., Zhang, X., Wang, R., Cao, R., Liu, Q., & Banerjee, W. (2019) · Nanoscale Advances · doi:10.1039/c9na00409b · Cited by 40
MXenes for memristive and tactile sensory systems
Ding, G., Yang, B., Chen, R., Zhou, K., Han, S., & Zhou, Y. (2021) · Applied Physics Reviews · doi:10.1063/5.0026093 · Cited by 40
Lu, Y., Li, X., Yan, B., Yan, L., Zhang, T., Song, Z., Huang, R., & Yang, Y. (2021) · Advanced Materials · doi:10.1002/adma.202107811 · Cited by 41
Flexible Ta2O5/WO3-Based Memristor Synapse for Wearable and Neuromorphic Applications
Rajasekaran, S., Simanjuntak, F. M., Chandrasekaran, S., Panda, D., Saleem, A., & Tseng, T. (2021) · IEEE Electron Device Letters · doi:10.1109/led.2021.3127489 · Cited by 42
Qian, C., Choi, Y., Kim, S., Kim, S., Choi, Y. J., Roe, D. G., Lee, J. H., Kang, M. S., Lee, W. H., & Cho, J. H. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202112490 · Cited by 40
CMOS back-end compatible memristors for in situ digital and neuromorphic computing applications
He, Z., Wang, T., Meng, J.-L., Zhu, H., Li, J., Sun, Q., Chen, L., & Zhang, D. W. (2021) · Materials Horizons · doi:10.1039/d1mh01257f · Cited by 39
Carstens, N., Adejube, B., Strunskus, T., Faupel, F., Brown, S. A., & Vahl, A. (2022) · Nanoscale Advances · doi:10.1039/d2na00121g · Cited by 40
Yang, Y., Wu, Y., He, W., Tien, H., Yang, W., Michinobu, T., Chen, W., Lee, W., & Chueh, C. (2022) · Advanced Science · doi:10.1002/advs.202203025 · Cited by 41
In materia implementation strategies of physical reservoir computing with memristive nanonetworks
Milano, G., Montano, K., & Ricciardi, C. (2023) · Journal of Physics D: Applied Physics · doi:10.1088/1361-6463/acb7ff · Cited by 35
Leakage Mechanism and Cycling Behavior of Ferroelectric Al0.7Sc0.3N
Chen, L., Wang, Q., Liu, C., Li, M., Song, W., Wang, W., Loke, D. K., & Zhu, Y. H. (2024) · Materials · doi:10.3390/ma17020397 · Cited by 40
Ling, S., Lin, S., Wu, Y., & Li, Y. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.150100 · Cited by 42
Chen, Y., Xia, J., Qu, Y., Zhang, H., Mei, T., Zhu, X., Xu, G., Li, D., Wang, L., Liu, Q., et al. (2025) · Advanced Materials · doi:10.1002/adma.202419013 · Cited by 40
Oxygen vacancy migration/diffusion induced synaptic plasticity in a single titanate nanobelt
Xiao, M. W., Shen, D., Musselman, K. P., Duley, W. W., & Zhou, Y. (2018) · Nanoscale · doi:10.1039/c7nr09335g · Cited by 41
Wang, S., Chen, X., Huang, X., Zhang, D. W., & Zhou, P. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000124 · Cited by 40
Iqbal, S., Duy, L. T., Kang, H., Singh, R., Kumar, M., Park, J., & Seo, H. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202102567 · Cited by 39
Timing Selector: Using Transient Switching Dynamics to Solve the Sneak Path Issue of Crossbar Arrays
Rao, M., Song, W., Kiani, F., Asapu, S., Zhuo, Y., Midya, R., Upadhyay, N. K., Wu, Q., Barnell, M. D., Lin, P., et al. (2021) · Small Science · doi:10.1002/smsc.202100072 · Cited by 41
Self-citationTerabe, K., Tsuchiya, T., & Tsuruoka, T. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100645 · Cited by 39
A review on ferroelectric systems for next generation photovoltaic applications
Pal, S., Sarath, N. V., Priya, K. S., et al. (2022) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac52f4 · Cited by 39
Sensory Adaptation in Biomolecular Memristors Improves Reservoir Computing Performance
Maraj, J. J., Haughn, K. P. T., Inman, D. J., & Sarles, S. A. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300049 · Cited by 40
Bio-inspired artificial synapses: Neuromorphic computing chip engineering with soft biomaterials
Ahmed, T. (2023) · Memories - Materials Devices Circuits and Systems · doi:10.1016/j.memori.2023.100088 · Cited by 38
Xie, Z., Zhang, D., Cheng, L., Li, C., Elia, J., Wu, J., Tian, J., Chen, L., Loi, M. A., Osvet, A., et al. (2024) · ACS Energy Letters · doi:10.1021/acsenergylett.3c02767 · Cited by 40
Emerging Optoelectronic Devices for Brain‐Inspired Computing
Hu, L., Zhuge, X., Wang, J., Wei, X., Zhang, L., Chai, Y., Xue, X., Ye, Z., & Zhuge, F. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400482 · Cited by 43
Comprehensive modeling of electrochemical metallization memory cells
Menzel, S. (2017) · Journal of Computational Electronics · doi:10.1007/s10825-017-1051-2 · Cited by 38
Electrochemically prepared oxides for resistive switching memories
Zaffora, A., Quarto, F. D., Habazaki, H., Valov, I., & Santamaria, M. (2018) · Faraday Discussions · doi:10.1039/c8fd00112j · Cited by 38
Artificial Shape Perception Retina Network Based on Tunable Memristive Neurons
Bao, L., Kang, J., Fang, Y., Yu, Z., Wang, Z., Yang, Y., Cai, Y., & Huang, R. (2018) · Scientific Reports · doi:10.1038/s41598-018-31958-6 · Cited by 36
Self-citationDang, B., Sun, J. W., Zhang, T., Wang, S., Zhao, M., Liu, K., Xu, L., Zhu, J., Cheng, C., Bao, L., et al. (2019) · IEEE Electron Device Letters · doi:10.1109/led.2019.2919914 · Cited by 37
“This behavior can be explained by formation of a thin Ag filament in MgO due to the small thickness of Ag in the electrode (4 nm), and the fragile filament can spontaneously be ruptured after removing the voltage so as to minimize the interfacial energy between the conductive filament and the dielectric [17]–[19].”
BackgroundEnergy-Efficient Artificial Synapses Based on Oxide Tunnel Junctions
Li, J., Ge, C., Lu, H., Guo, H., Guo, E., He, M., Wang, C., Yang, G., & Jin, K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b13434 · Cited by 37
Basnet, P., Pahinkar, D. G., West, M. P., Perini, C. J., Graham, S., & Vogel, E. M. (2020) · Journal of Materials Chemistry C · doi:10.1039/c9tc06736a · Cited by 39
Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
Lee, T., Kim, S., & Seong, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-62642-3 · Cited by 40
Huang, H., Yu, X. Y., Yang, R., Yu, Y., He, H., Wang, Z., & Guo, X. (2020) · Advanced Science · doi:10.1002/advs.202001842 · Cited by 36
Self-citationJeon, Y.-R., Choi, J.-M., Kwon, J., et al. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c18784 · Cited by 38
Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs
Du, N., Zhao, X., Chen, Z., Choubey, B., Ventra, M. D., Skorupa, I., Bürger, D., & Schmidt, H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.660894 · Cited by 38
Feali, M. S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.132 · Cited by 38
Soft Biomaterials Based Flexible Artificial Synapse for Neuromorphic Computing
Guo, T., Ge, J., Sun, B. T., Pan, K., Pan, Z., Wei, L., Yan, Y., Zhou, Y., & Wu, Y. A. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200449 · Cited by 38
Understanding of the Volatile and Nonvolatile Switching in Ag-Based Memristors
Ding, X., Huang, P. C., Zhao, Y., et al. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3144373 · Cited by 37
Basnet, P., Anderson, E. C., Athena, F. F., Chakrabarti, B., West, M. P., & Vogel, E. M. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00079 · Cited by 38
Spintronics intelligent devices
Cai, W., Huang, Y., Zhang, X., Wang, S., Pan, Y., Yin, J., Shi, K., & Zhao, W. S. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-022-2012-2 · Cited by 37
Tan, H., Majumdar, S., Qin, Q., Lahtinen, J., & van Dijken, S. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900036 · Cited by 37
Zhang, H., Ju, X., Yew, K. S., & Ang, D. S. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b17026 · Cited by 37
Miranda, E. A., Milano, G., & Ricciardi, C. (2020) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2020.3009734 · Cited by 37
“In order to model STP effects in ZnO-NW including potentiation (current increase), depression and relaxation (current decrease) caused by external electrical stimulation, a memristive model comprising one equation for the electron transport and one equation for the ion displacement is proposed.”
BackgroundInfluentialJiang, D., Li, J., Fu, W., Chen, Q., Yang, Y., Zhou, Y.-H., & Zhang, J. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00474 · Cited by 37
Memristor-based Hopfield network circuit for recognition and sequencing application
Sun, J., Xiao, X., Yang, Q., Liu, P., & Wang, Y. (2021) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2021.153698 · Cited by 37
Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning
Yang, C., Wang, X., Chen, Z., Zhang, S., & Zeng, Z. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3204742 · Cited by 38
“and volatile [22] models, have also been proposed based on their physical electrical properties or experimental data.”
BackgroundMXenes: promising 2D memristor materials for neuromorphic computing components
Patel, M., Ramesh, H. N., Gosai, J., Mohili, R., Solanki, A., Roy, M., Fang, B., & Chaudhari, N. K. (2022) · Trends in Chemistry · doi:10.1016/j.trechm.2022.06.004 · Cited by 36
Song, B., Xu, H. F., Liu, S., Liu, H., & Li, Q. (2018) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2018.2836400 · Cited by 36
“generation nonvolatile memory and neuromorphic computing applications due to the scalability, 3D stacking potential and analogy to characteristics of synapse [1]–[3].”
BackgroundInkjet-printed h-BN memristors for hardware security
Zhu, K., Vescio, G., González‐Torres, S., López-Vidrier, J., Frieiro, J. L., Pazos, S. M., Jing, X., Gao, X., Wang, S., Ascorbe-Muruzábal, J., et al. (2023) · Nanoscale · doi:10.1039/d3nr00030c · Cited by 36
In-memory and in-sensor reservoir computing with memristive devices
Lin, N., Chen, J., Zhao, R., He, Y., Wong, K., Qiu, Q., Wang, Z., & Yang, J. J. (2024) · APL Machine Learning · doi:10.1063/5.0174863 · Cited by 38
Self-citationKhot, A. C., Nirmal, K. A., Dongale, T. D., et al. (2024) · Small · doi:10.1002/smll.202400791 · Cited by 36
So, H., Ji, H., Kim, S., & Kim, S. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202405544 · Cited by 38
Bioinspired ion-shuttling memristor with both neuromorphic functions and ion selectivity
Xie, B., Xiong, T., Guo, G., Pan, C., Ma, W., & Yu, P. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2417040122 · Cited by 37
Niu, G., Schubert, M. A., Sharath, S. U., Zaumseil, P., Vogel, S., Wenger, C., Hildebrandt, E., Bhupathi, S., Pérez, E., Alff, L., et al. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa6cd9 · Cited by 36
Dual-Layer Selector With Excellent Performance for Cross-Point Memory Applications
Lin, Q., Li, Y., Xu, M., Cheng, Q., Qian, H., Feng, J., Tong, H., & Miao, X. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2808465 · Cited by 36
“This type of TS device with diffusive dynamics has also been reported for their important applications in neuromorphic computing recently [20], [21].”
BackgroundA Fluorographene‐Based Synaptic Transistor
Liu, B., Hong, M., Sahoo, M., Ong, B. L., Tok, E. S., Di, M., Ho, Y., Liang, H., Bow, J. S., Liu, Z., et al. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201900422 · Cited by 36
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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