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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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.
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
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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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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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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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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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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 801–900 of 2,351
A Photoelectric-Stimulated MoS 2 Transistor for Neuromorphic Engineering
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Water-Mediated Ionic Migration in Memristive Nanowires with a Tunable Resistive Switching Mechanism
Milano, G., Raffone, F., Lübben, M., Boarino, L., Cicero, G., Valov, I., & Ricciardi, C. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c13020 · Cited by 37
Memory-centric neuromorphic computing for unstructured data processing
Sung, S. H., Kim, T., Shin, H., Namkung, H., Im, T. H., Wang, H. S., & Lee, K. J. (2021) · Nano Research · doi:10.1007/s12274-021-3452-6 · Cited by 36
Hui, F., Liu, P., Hodge, S. A., Carey, T., Wen, C., Torrisi, F., Galhena, D. T. L., Tomarchio, F., Lin, Y., Moreno, E., et al. (2021) · Small · doi:10.1002/smll.202101100 · Cited by 36
Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors
Wang, Y., Chen, X., Shen, D., Zhang, M., Chen, X., Chen, X., Shao, W., Gu, H., Xu, J., Hu, E., et al. (2021) · Nanomaterials · doi:10.3390/nano11112860 · Cited by 37
“However, when the applied voltage was not strong enough, filaments were spontaneously ruptured resulting from the Joule heat effect and minimum energy positions [45,46].”
BackgroundCao, Y., Zhao, T., Zhao, C., Liu, Y., Song, P., Gao, H., & Zhao, C. (2022) · Journal of Materials Chemistry C · doi:10.1039/d1tc04827a · Cited by 36
Y2O3-Based Crossbar Array for Analog and Neuromorphic Computation
Kumar, S., Kumbhar, D. D., Park, J. H., Kamat, R. K., Dongale, T. D., & Mukherjee, S. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3227890 · Cited by 37
“The current research trend suggests that memristors are promising candidates for neurobiological synapses, which can effectively perform synaptic functionalities in a single device [9], [10], [11], [12], [13].”
BackgroundShin, S. M., Kang, D. C., Kim, K., Jeong, Y., Kim, J., Lee, S., Kwak, J. Y., Park, J., Hwang, G. W., Lee, K.-S., et al. (2022) · Materials Advances · doi:10.1039/d1ma01078f · Cited by 35
Resistive Switching and Synaptic Characteristics in ZnO/TaON-Based RRAM for Neuromorphic System
Oh, I., Pyo, J., & Kim, S. (2022) · Nanomaterials · doi:10.3390/nano12132185 · Cited by 32
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Self-citationHuang, X., Jiang, K., Niu, Y., REN-ZHI, W., Zheng, D., Dong, A., Dong, X., Mei, C., Lu, J., Liu, S., et al. (2018) · Applied Physics Letters · doi:10.1063/1.5047925 · Cited by 35
Processing Halide Perovskite Materials with Semiconductor Technology
Cheng, C., Zhu, C., Huang, B., et al. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201800729 · Cited by 35
Ren, Y., Chang, C., Ting, L., Zhou, L., Mao, J., Zhang, S., Chou, H., Yang, J., Zhou, Y., & Han, S. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900008 · Cited by 35
Self-citationMultistability in a physical memristor-based modified Chua’s circuit
Guo, M. L., Yang, W., Xue, Y., et al. (2019) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5089293 · Cited by 35
Memristor crossbar array for binarized neural networks
Kim, Y. S., Jeong, W. H., Tran, S. B., Woo, H. C., Kim, J., Hwang, C. S., Min, K., & Choi, B. J. (2019) · AIP Advances · doi:10.1063/1.5092177 · Cited by 33
A high performance electroformed single-crystallite VO 2 threshold switch
Zhou, X., Gu, D., Li, Y., Qin, H., Jiang, Y., & Xu, J. M. (2019) · Nanoscale · doi:10.1039/c9nr08364b · Cited by 36
Li, W., Song, X., Zhao, X., Zhang, X., Chen, R., Zhang, X., Jiang, C., He, J., & Xiao, X. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.104213 · Cited by 35
Wlaźlak, E., Zawal, P., & Szacilowski, K. T. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00750 · Cited by 34
Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables
Xu, S., & Wu, W. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000117 · Cited by 37
Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al2O3/TiN Stack
Ryu, H., & Kim, S. (2020) · Nanomaterials · doi:10.3390/nano10102055 · Cited by 35
“It is worthwhile to note that the synaptic dynamics such as those of CBRAM type or diffusive memristor devices using Ag and Cu can be realized in ur Pt/Al2O3/TiN device [51].”
BackgroundGradient Descent on Multilevel Spin–Orbit Synapses with Tunable Variations
Lan, X.-K., Cao, Y., Liu, X., Xu, K.-J., Liu, C., Zheng, H., & Wang, K.-Y. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202000182 · Cited by 31
Guo, J., Liu, Y., Zhou, F., Li, F., Li, Y., & Huang, F. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202102015 · Cited by 36
Sangwan, V. K., Rangnekar, S. V., Kang, J., Shen, J., Lee, H., Lam, D. K., Shen, J., Liu, X., de Moraes, A. C. M., Kuo, L., et al. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202107385 · Cited by 36
Review of applications of 2D materials in memristive neuromorphic circuits
Wang, L., Shen, X., Gao, Z., Fu, J., Yao, S., Cheng, L., & Lian, X. (2022) · Journal of Materials Science · doi:10.1007/s10853-022-06954-x · Cited by 35
“201704862 [15] Wang ZR et al (2017) Memristors with diffusive dynamics”
BackgroundElectrochemically prepared oxides for resistive switching devices
Zaffora, A., Macaluso, R., Habazaki, H., Valov, I., & Santamaria, M. (2018) · Electrochimica Acta · doi:10.1016/j.electacta.2018.04.087 · Cited by 35
Nikam, R. D., & Hwang, H. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202201749 · Cited by 34
An ab initio study on resistance switching in hexagonal boron nitride
Ducry, F., Waldhoer, D., Knobloch, T., Csontos, M., Olalla, N. J., Leuthold, J., Grasser, T., & Luisier, M. (2022) · npj 2D Materials and Applications · doi:10.1038/s41699-022-00340-6 · Cited by 35
From memristive devices to neuromorphic systems
Huang, Y., Kiani, F., Ye, F., & Xia, Q. (2023) · Applied Physics Letters · doi:10.1063/5.0133044 · Cited by 36
Self-citationElectrochemical Analysis of Ion Effects on Electrolyte-Gated Synaptic Transistor Characteristics
Lee, H., Cho, J., Jin, M., Lee, J., Lee, C. Y., Kim, J., Lee, J., Shin, J. C., Yoo, J., Lee, E., et al. (2024) · ACS Nano · doi:10.1021/acsnano.3c10082 · Cited by 35
Perovskite Thin Single Crystal for a High Performance and Long Endurance Memristor
Fernández-Guillen, I., Aranda, C., Betancur, P. F., Vallés‐Pelarda, M., Momblona, C., Ripollés, T. S., Abargues, R., & Boix, P. P. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202300475 · Cited by 40
Neuromorphic ionic computing in droplet interface synapses
Li, Z., Myers, S. K., Xiao, J., Li, Y., Noy, N. F., Leuski, A., & Noy, A. (2025) · Science Advances · doi:10.1126/sciadv.adv6603 · Cited by 40
Self-citationA Compact Model for Drift and Diffusion Memristor Applied in Neuron Circuits Design
Zhao, Y., Fang, C., Zhang, X., Xu, X., Gong, T., Luo, Q., Chen, C., Liu, Q., Lv, H., Li, Q., et al. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2865225 · Cited by 33
“For the diffusion memristor, according to [1], [20], the interfacial energy for particles diffusion is included and the model is shown as:”
BackgroundMethodInfluentialSynaptic dynamics in complex self-assembled nanoparticle networks
Bose, S. K., Shirai, S., Mallinson, J. B., & Brown, S. A. (2018) · Faraday Discussions · doi:10.1039/c8fd00109j · Cited by 34
Liquid-based memory and artificial synapse
Kim, D., & Lee, J. (2019) · Nanoscale · doi:10.1039/c9nr02767j · Cited by 33
Wu, Q., Lu, N., Geng, D., Wang, H., Li, L., Liu, M., Yang, G., Lu, C., Xu, G., Wang, J., et al. (2019) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2019.2928792 · Cited by 34
Self-citationImprovement in conductance modulation linearity of artificial synapses based on NaNbO3 memristor
Woo, J., Hwang, H.-G., Park, S.-M., Lee, T., & Nahm, S. (2020) · Applied Materials Today · doi:10.1016/j.apmt.2020.100582 · Cited by 34
Electroforming in Metal-Oxide Memristive Synapses
Wang, T., Shi, Y., Puglisi, F. M., Chen, S., Zhu, K., Zuo, Y., Li, X., Jing, X., Han, T., Guo, B., et al. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b19362 · Cited by 34
Oxide-based filamentary RRAM for deep learning
Zhang, Y., Huang, P. C., Gao, B., Kang, J., & Wu, H. (2020) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/abc5e7 · Cited by 35
“The long-and short-term plasticity, the basic functions of biological synapses, can be emulated using RRAM devices with nonvolatile and volatile characteristics [24].”
BackgroundA Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors
Mao, H., He, Y., Chen, C., Zhu, L. Q., Zhu, Y., Zhu, Y., Ke, S., Wang, X., Wan, C., & Wan, Q. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100918 · Cited by 35
Ahn, Y., Shin, H. W., Lee, T. H., et al. (2018) · Nanoscale · doi:10.1039/c8nr02986e · Cited by 34
Phase-change nanoclusters embedded in a memristor for simulating synaptic learning
Wan, Q., Zeng, F., Yin, J., Sun, Y., Hu, Y., Liu, J., Wang, Y., Li, G., Guo, D., & Pan, F. (2019) · Nanoscale · doi:10.1039/c8nr09765h · Cited by 35
Self-citationLi, Y., Xiong, Y., Zhai, B., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk9474 · Cited by 33
Wang, J., Ren, Y., Yang, Z., Lv, Q., Zhang, Y., Zhang, M., Zhao, T., Gu, D., Liu, F., Tang, B., et al. (2024) · Advanced Science · doi:10.1002/advs.202309538 · Cited by 36
Zhang, S., Tao, Y., Qin, S., et al. (2024) · npj 2D Materials and Applications · doi:10.1038/s41699-024-00519-z · Cited by 33
“In the actual preparation process, it may be contaminated by polymers, resulting in unstable resistance 84 – 86 .”
Yuan, L., Zhao, T., Dai, J., Xue, L., Zhang, X., Peng, C., Wen, P., Liu, H., Hu, H., Chen, L., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202418052 · Cited by 34
Self-citationHuang, P. C., Li, Z., Dong, Z., Han, R., Zhou, Z., Zhu, D., Liu, L., Liu, X., & Kang, J. (2019) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00011 · Cited by 34
Self-citationHalogen-containing semiconductors: From artificial photosynthesis to unconventional computing
Klejna, S., Mazur, T., Wlaźlak, E., Zawal, P., Soo, H. S., & Szacilowski, K. T. (2020) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213316 · Cited by 33
Electromechanical coupling effects for data storage and synaptic devices
Guo, L., Han, S., & Zhou, Y. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105156 · Cited by 35
Ali, M., Sokolov, A. S., Ko, M. J., & Choi, C. (2020) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2020.157514 · Cited by 35
Lee, S., Park, H., Kim, M., Kim, M., Park, B., & Lee, S.-D. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c15519 · Cited by 34
Bousoulas, P., Panagopoulou, M., Boukos, N. K., & Tsoukalas, D. (2021) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/abea3b · Cited by 33
“A high/low frequency leads to an increase/decrease of the current response from the initial state and as a result to clear demonstration of bio-synaptic properties (figure S9— supporting information) [34].”
Wang, H., Yan, X., Wang, S., & Lu, N. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c01076 · Cited by 33
High-performance resistive switching memory with embedded molybdenum disulfide quantum dots
Yu, X., Chang, K., Dong, A., Gan, Z., Jiang, K., Ling, Y., Niu, Y., Zheng, D., Dong, X., REN-ZHI, W., et al. (2021) · Applied Physics Letters · doi:10.1063/5.0039654 · Cited by 33
Shamsi, J., Avedillo, M. J., Linares-Barranco, B., & Serrano‐Gotarredona, T. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.674567 · Cited by 34
Song, Y., Feng, G., Wu, L., et al. (2021) · Journal of Materials Chemistry C · doi:10.1039/d1tc04248c · Cited by 33
Single Crystal Halide Perovskite Film for Nonlinear Resistive Memory with Ultrahigh Switching Ratio
Li, L., Chen, Y., Cai, C., Ma, P., Ji, H., & Zou, G. (2021) · Small · doi:10.1002/smll.202103881 · Cited by 33
Mixed Ionic‐Electronic Charge Transport in Layered Black‐Phosphorus for Low‐Power Memory
Ahmed, T., Kuriakose, S., Tawfik, S. A., Mayes, E. L. H., Mazumder, A., Balendhran, S., Spencer, M. J. S., Akinwande, D., Bhaskaran, M. C., Sriram, S., et al. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202107068 · Cited by 34
Yang, J., Lee, J., Jung, Y., Kim, S., Kim, J. H., Kim, S., Kim, J., Seo, S., Park, D.-A., Lee, J., et al. (2022) · Advanced Science · doi:10.1002/advs.202200168 · Cited by 32
Self-citationRobust and Low-Power-Consumption Black Phosphorus–Graphene Artificial Synaptic Devices
Yuan, S., Qiu, B., Amina, K., Li, L., Zhai, P., Su, Y., Xue, T., Jiang, T., Ding, L., & Wei, G. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c03667 · Cited by 33
Ajayan, J., Nirmal, D., I.V, B. K. J., & Sreejith, S. (2022) · Microelectronics Journal · doi:10.1016/j.mejo.2022.105634 · Cited by 33
A spiking artificial neuron based on one diffusive memristor, one transistor and one resistor
Zhao, R., Wang, T., Moon, T., Xu, Y., Zhao, J., Sud, P., Kim, S. J., Liao, H.-T., Zhuo, Y., Midya, R., et al. (2025) · Nature Electronics · doi:10.1038/s41928-025-01488-x · Cited by 45
“A diffusive memristor is a volatile memristor with intrinsic dynamic behaviour driven by ion migration 21 , closely resembling the electrolyte-ion diffusion mechanisms of biological neurons (Fig.”
Self-citationWang, Z., Wang, W., Liu, P., Liu, G., Li, J., Zhao, J., Zhou, Z., Wang, J., Pei, Y., Zhao, Z., et al. (2022) · Research · doi:10.34133/2022/9754876 · Cited by 32
Self-citationOscillatory Neural Network-Based Ising Machine Using 2D Memristors
Chen, X., Yang, D., Hwang, G., Dong, Y., Cui, B., Wang, D., Chen, H., Lin, N., Zhang, W., Li, H., et al. (2024) · ACS Nano · doi:10.1021/acsnano.3c10559 · Cited by 37
Self-citationRyu, J., Park, K., Sahu, D. P., et al. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c04874 · Cited by 32
Mei, T., Chen, F., Huang, T., Feng, Z., Wan, T., Han, Z., Li, Z., Hu, L., Lin, C., Lu, Y., et al. (2025) · ACS Nano · doi:10.1021/acsnano.5c02397 · Cited by 36
Self-citationMetal oxide-based resistive switching memristors for neuromorphic computing
Tian, Q., Shan, X., Bian, J., Cheng, Y., Zheng, J., Wang, Z., Zhao, X., Xu, H., & Liu, Y. (2025) · Journal of Materials Chemistry C · doi:10.1039/d5tc01467k · Cited by 37
Self-citationThreshold switching synaptic device with tactile memory function
Wang, D., Wang, L., Ran, W., Zhao, S., Yin, R., Yan, Y., Jiang, K., Lou, Z., & Shen, G. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105109 · Cited by 31
Zeng, T., Yang, Z. C., Liang, J., Lin, Y., Cheng, Y., Hu, X., Zhao, X., Wang, Z., Xu, H., & Liu, Y. (2021) · Nanoscale Advances · doi:10.1039/d1na00152c · Cited by 31
Self-citationWang, J., Zhuge, X., & Zhuge, F. (2021) · Science and Technology of Advanced Materials · doi:10.1080/14686996.2021.1911277 · Cited by 34
“Besides, Ag-doped Ta 2 O 5 [146], Ag-doped HfO x [147], Ag-doped MgO x [147], Ag-doped SiO x N y [147], Cu-Al-codoped SiO 2 and Cu-Ga-codoped SiO 2 [148] have been employed to fabricate memristive devices based on metal ion migration.”
MethodTransverse barrier formation by electrical triggering of a metal-to-insulator transition
Salev, P., Fratino, L., Sasaki, D., Berkoun, R., del Valle, J., Kalcheim, Y., Takamura, Y., Rozenberg, M. J., & Schuller, I. K. (2021) · Nature Communications · doi:10.1038/s41467-021-25802-1 · Cited by 33
MXene-Based Memristor for Artificial Optoelectronic Neuron
Zeng, B., Zhang, X., Gao, C., Zou, Y., Yu, X., Yang, Q., Guo, T., & Chen, H. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3234881 · Cited by 32
“behavior is caused by the formation and rupture of Ag CF [35].”
BackgroundA DISCRETE MEMRISTOR COUPLED TWO-DIMENSIONAL GENERALIZED SQUARE HYPERCHAOTIC MAPS
Ma, Y., Mou, J., Lu, J., et al. (2023) · Fractals · doi:10.1142/s0218348x23401369 · Cited by 32
Nie, F., Wang, J., Fang, H., Ma, S., Wu, F., Zhao, W., Wei, S., Wang, Y., Zhao, L., Yan, S., et al. (2023) · Materials Futures · doi:10.1088/2752-5724/ace3dc · Cited by 32
“In contrast, the diffusive memristors, which are generally built by embedding metallic clusters within a dielectric matrix [14], exhibit short-term volatility due to the spontaneous relaxation of the metallic ions, and are expected to participate in the dynamic behaviors of neural networks [15],…”
BackgroundSolution‐Processed Flexible Threshold Switch Devices
Park, Y., Han, U., Kim, M., & Lee, J. (2018) · Advanced Electronic Materials · doi:10.1002/aelm.201700521 · Cited by 32
Super Nonlinear Electrodeposition–Diffusion-Controlled Thin-Film Selector
Ji, X., Song, L., He, W., Huang, K., Yan, Z., Zhong, S., Zhang, Y., & Zhao, R. J. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b17235 · Cited by 32
Dynamics of a physical SBT memristor-based Wien-bridge circuit
Guo, M. L., Gao, Z., Xue, Y., Dou, G., & Li, Y. (2018) · Nonlinear Dynamics · doi:10.1007/s11071-018-4284-0 · Cited by 29
Artificial LIF Neuron With Bursting Behavior Based on Threshold Switching Device
Zhang, Z., Gao, S., Li, Z., et al. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3236906 · Cited by 31
“The interface energy minimization drives the spontaneous rupture of Ag filament tip when the voltage is removed [43], and the device returns to LRS.”
BackgroundSelf-citationMemlumor: A Luminescent Memory Device for Energy-Efficient Photonic Neuromorphic Computing
Marunchenko, A., Kumar, J., Kiligaridis, A., Tatarinov, D., Pushkarev, A. P., Vaynzof, Y., & Scheblykin, I. (2024) · ACS Energy Letters · doi:10.1021/acsenergylett.4c00691 · Cited by 31
Li, C., Xu, T., Pan, R. Y., Bao, S., Yin, K., Shen, J., Zhu, Y., Hou, S., & Sun, L. (2024) · ACS Nano · doi:10.1021/acsnano.4c11598 · Cited by 35
Recent Progress in Real‐Time Adaptable Digital Neuromorphic Hardware
Kornijčuk, V., & Jeong, D. S. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900030 · Cited by 31
Phase noise-induced coherence resonance in three dimension memristive Hindmarsh-Rose neuron model
Lu, L., Bao, C., Ge, M., Xu, Y., Yang, L., Zhan, X., & Jia, Y. (2019) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2019-900011-1 · Cited by 30
Stoliar, P., Yamada, H., Toyosaki, Y., & Sawa, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-54215-w · Cited by 31
Synaptic learning and memory functions in SiO 2 :Ag/TiO 2 based memristor devices
Li, D., Ilyas, N., Li, C., Jiang, X., Jiang, Y., & Li, W. (2020) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab70c9 · Cited by 32
Liu, Z., & Wang, L. (2020) · IEEE Access · doi:10.1109/access.2020.2990536 · Cited by 31
“This breakthrough brought about the prosperity of the mem-ristor technology in aspects of both the theoretical models [124], [125] and experimental fabrication [126], [127], leading to many technical publications and enormous US patents.”
BackgroundReview of resistive switching mechanisms for memristive neuromorphic devices*
Yang, R. (2020) · Chinese Physics B · doi:10.1088/1674-1056/aba9c7 · Cited by 31
Yu, Y., Wang, C., Jiang, C., Wang, L., Wang, Z., Du, Z., Sun, J., Abrahams, I., & Huang, X. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.159194 · Cited by 31
Self-citationLanza, M., Palumbo, F., Shi, Y., Aguirre, F. L., Boyeras, S., Yuan, B., Yalon, E., Moreno, E., Wu, T., & Roldán, J. B. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100580 · Cited by 31
Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
Guo, T., Pan, K., Sun, B. T., Wei, L., Yan, Y., Zhou, Y., & Wu, Y. A. (2021) · Materials Today Advances · doi:10.1016/j.mtadv.2021.100192 · Cited by 32
Kim, H., Woo, D., Jin, S., Kwon, H., Kwon, K., Kim, D., Park, D., Kim, D., Jin, H., Choi, H., et al. (2022) · Advanced Materials · doi:10.1002/adma.202203643 · Cited by 32
Li, H., Hu, J., Chen, A., Wang, C., Chen, L., Tian, F., Zhou, J., Zhao, Y., Chen, J., Tong, Y., et al. (2022) · Advanced Materials · doi:10.1002/adma.202207371 · Cited by 31
Ahn, W., Jeong, H. B., Oh, J., Hong, W., Cha, J., Jeong, H. Y., & Choi, S. (2023) · Small · doi:10.1002/smll.202300223 · Cited by 32
Reservoir computing using networks of memristors: effects of topology and heterogeneity
Mallinson, J. B., Heywood, Z. E., Daniels, R. K., Arnold, M. D., Bones, P. J., & Brown, S. A. (2023) · Nanoscale · doi:10.1039/d2nr07275k · Cited by 33
Advances in pixel driving technology for micro-LED displays
Gao, H., Zou, M., Zhong, C., Zhuang, J., Lin, J., Lu, Z., Jiang, Z., Lü, Y., Chen, Z., & Guo, W. (2023) · Nanoscale · doi:10.1039/d3nr01649h · Cited by 32
Liu, M., Cao, Z., Wang, X., Mao, S., Qin, J., Yang, Y., Rao, Z., Zhao, Y. X., & Sun, B. T. (2023) · Journal of Materials Chemistry C · doi:10.1039/d3tc02309e · Cited by 30
Two-dimensional material-based memristive devices for alternative computing
Panisilvam, J., Lee, H. Y., Byun, S., Fan, D., & Kim, S. (2024) · Nano Convergence · doi:10.1186/s40580-024-00432-7 · Cited by 31
“Memristive devices offer several advantages including non-volatility, low power consumption, and parallelism, allowing for applications such as analog computing [2, 3], in-memory computing [4, 5] and most importantly for the current technological climate, neuromorphic computing [6, 7].”
BackgroundMechanical exfoliation of non-layered metal oxides into ultrathin flakes
Li, R., Yao, Z., Li, Z., et al. (2024) · Nature Synthesis · doi:10.1038/s44160-024-00657-8 · Cited by 30
“One well-known example is the insulating amorphous metal oxides (AMOs, such as SiO 2 and high dielectric constant (high-k ) HfO 2 ), which are critical industrial dielectrics in memory and logic devices 4–6 .”
InfluentialSelf-citationMondal, I., Attri, R., Rao, T. S., Yadav, B., & Kulkarni, G. U. (2024) · Applied Physics Reviews · doi:10.1063/5.0220628 · Cited by 30
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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