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
53 Own result · 32 Own interpretation · 18 No basis given · 10 Sources not found · 8 No full text of the sources · 3 Partly supported · 2 Couldn’t confirm · 2 Own method · 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)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
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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
plain- Asserted: passes — The authors put it forward as true.
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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
boosteddirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
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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
boosteddirect- Asserted: passes — The authors put it forward as true.
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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 simulations for Ag in dielectric agreed well with the experimental HRTEM observations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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
plaindirect- Asserted: passes — The authors put it forward as true.
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It 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)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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)
plain- Asserted: passes — The authors put it forward as true.
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Probably 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It 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
boosteddirect- Asserted: passes — The authors put it forward as true.
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It 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It 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
plain- Asserted: passes — The authors put it forward as true.
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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
“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
boosteddirect- Asserted: passes — The authors put it forward as true.
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It 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.
plain- Asserted: passes — The authors put it forward as true.
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- 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.
boosted- Asserted: passes — The authors put it forward as true.
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It 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
plain- Asserted: passes — The authors put it forward as true.
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It 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
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It 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It 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
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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
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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
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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
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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
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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
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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
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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
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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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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The microscopic nature of both the threshold switching and relaxation of the diffusive memristor is revealed for the first time by in situ HRTEM and explained by nanoparticle dynamics simulation.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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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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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When the post-spike precedes the pre-spike, the device is reset (depressed), and when the prespike precedes the post-spike, the device is set (potentiated).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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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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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Device current response (blue) to multiple dependent plasticity (SRDP and true STDP) behaviour of a combined device consisting of a diffusive and a drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The long low voltage pulse in each spike turns the diffusive memristor ON, and the short high voltage pulse switches the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The device did not fail during the measurement, demonstrating high robustness.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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Probably 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.
plain- Asserted: passes — The authors put it forward as true.
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- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Various types of memristors based on ionic drift (drift-type memristor) [4][5][6][7][8] have recently been utilized for this purpose in neuromorphic architectures [9][10][11][12][13][14][15] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [4][5][6][7][8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [9][10][11][12][13][14][15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Recently, Lu's group adopted second-order drift memristors to approximate the Ca 2+ dynamics of chemical synapses by utilizing thermal dissipation 19 or mobility decay 20 , which successfully demonstrated STDP with non-overlapping spikes and other synaptic functions”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This approach features repeatability and simplicity, but the significant differences of the dynamical response from actual synapses limit the fidelity and variety of desired synaptic functions.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
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In the paper
“Similar issues also exist in MOS-based memristor emulators [16][17][18] , although they are capable of simulating a variety of synaptic functions including spike-timing-dependent plasticity (STDP).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [16][17][18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- 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.
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
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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 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.
plain- Asserted: passes — The authors put it forward as true.
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It 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaindirect- Asserted: passes — The authors put it forward as true.
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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.
plaindirect- Asserted: passes — The authors put it forward as true.
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It 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
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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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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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Cited by
Sorted by citations
Items 1,501–1,600 of 2,351
Riddled Attraction Basin and Multistability in Three-Element-Based Memristive Circuit
Xu, Q., Tan, X., Zhang, Y., Bao, H., Hu, Y., Bao, B., & Chen, M. (2020) · Complexity · doi:10.1155/2020/4624792 · Cited by 9
Improved Stability and Controllability in ZrN-Based Resistive Memory Device by Inserting TiO2 Layer
Choi, J., & Kim, S. (2020) · Micromachines · doi:10.3390/mi11100905 · Cited by 10
“Recently, Ag-based programmable metallization cell (PMC) has attracted interest as a neuromorphic device due to its versatile memory properties, including volatile and non-volatile switching with low power consumption [17].”
BackgroundNon-spike timing-dependent plasticity learning mechanism for memristive neural networks
Tang, Z., Chen, Y., Wang, Z., Hu, R., & Wu, E. Q. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01985-w · Cited by 10
“Recently, J. J. Yang from UMass proposed some breakthrough in memristor based neural network [44] and novel diffusion memristor device [32].”
BackgroundSelf-citationTowards Sustainable Crossbar Artificial Synapses with Zinc-Tin Oxide
Silva, C., Martins, J., Deuermeier, J., et al. (2021) · Electronic Materials · doi:10.3390/electronicmat2020009 · Cited by 10
“This interest is in great part not only due to the analog switching behavior that is shown in area-dependent mechanisms, but also because filament formation and destruction is not needed for switching to occur, leading to better device reliability [7].”
BackgroundFrontier applications of perovskites beyond photovoltaics
Mei, L., Mu, H., Zhu, H. L., et al. (2022) · Journal of Semiconductors · doi:10.1088/1674-4926/43/4/040203 · Cited by 10
Iqbal, S., Kumar, M., Sial, Q. A., Duy, L. T., & Seo, H. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.2c01167 · Cited by 10
Flexibly Photo-Regulated Brain-Inspired Functions in Flexible Neuromorphic Transistors
Wang, L., Zhang, T., Shen, J., Huang, J., Li, W., Shi, W. Y., Huang, W., & Yi, M. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c22754 · Cited by 10
Physically Transient Artificial Neuron Based on Mg/Magnesium Oxide Threshold Switching Memristor
Cao, Y., Wang, S., Wang, R., Sun, J. W., Yang, M., Ma, X., Wang, H., & Hao, Y. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3249137 · Cited by 10
“Due to simple structure and low power consumption, memristors are frequently employed as artificial neurons and synapses [12], [13], [14], [15], [16], [17].”
BackgroundStudy of a Stretchable Polymer for Adjustable Flexible Organic Memristor
Ding, Q., Jiao, W., Wang, H., Zhang, X., & Gao, S. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3274509 · Cited by 10
“which is similar to previous reports [32], [33].”
ResultSchmitt, N., Ascoli, A., Messaris, I., Demirkol, A. Ş., Menzel, S., Rana, V., Tetzlaff, R., & Chua, L. O. (2024) · Frontiers in Nanotechnology · doi:10.3389/fnano.2024.1301320 · Cited by 10
CMOS compatible low-power volatile atomic switch for steep-slope FET devices
Lim, S., Yoo, J., Song, J., Woo, J., Park, J.-H., & Hwang, H. (2018) · Applied Physics Letters · doi:10.1063/1.5039898 · Cited by 10
Wang, G., Yan, X., Chen, J., & Ren, D. (2019) · physica status solidi (RRL) - Rapid Research Letters · doi:10.1002/pssr.201900539 · Cited by 10
Zhang, Y.-Z., Ping, Y., Zhang, Z.-L., & Zhao, G.-Z. (2021) · Complex · doi:10.1155/2021/2747174 · Cited by 6
Chen, Q., Han, T., Tang, M., Zhang, Z., Zheng, X., & Liu, G. (2020) · Micromachines · doi:10.3390/mi11040427 · Cited by 9
A memristive computational neural network model for time-series processing
Pistolesi, V., Ceni, A., Milano, G., Ricciardi, C., & Gallicchio, C. (2025) · APL Machine Learning · doi:10.1063/5.0255168 · Cited by 9
Kim, K.-H., Lim, J. G., Hu, S. M., Jeong, Y., Kim, J., Lee, S., Kwak, J. Y., Park, J., Hwang, G. W., Lee, K.-S., et al. (2023) · NPG Asia Materials · doi:10.1038/s41427-023-00495-8 · Cited by 9
Low-power perovskite-based threshold switching memristor for artificial nociceptor
Li, Y., Li, J., Ni, J., Zhang, J., & Cai, H. (2024) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2024.175121 · Cited by 9
Xu, H., Liu, T., Li, H., et al. (2024) · Materials Horizons · doi:10.1039/d4mh00716f · Cited by 9
Reconfigurable neuromorphic computing by a microdroplet
Ma, Y., Niu, Y., Pei, R.-C., Wang, W., Wei, B., & Xie, Y.-B. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102202 · Cited by 10
A True Random Number Generator Based on High-Speed Ag/a-Si/Pt Memristor
Guo, Z., Fang, Z., Niu, J., Wang, H., Yan, L., Tong, L., Zhao, J., Yin, S., Sun, S., Feng, L., et al. (2024) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3454588 · Cited by 10
“2 shows a schematic of the formation and rupture process of Ag conductive filaments (CF) [19], [20].”
BackgroundResistive switching and artificial synapses performance of co-evaporated Cs3Cu2I5 films
Zhang, Y., Mao, X., Xi, C., Xie, J., Niu, Y., Gong, S., & Qian, M. (2024) · Applied Physics Letters · doi:10.1063/5.0235918 · Cited by 9
Zhu, L. Q., Li, S., Zhang, F., et al. (2024) · Small Methods · doi:10.1002/smtd.202401543 · Cited by 9
Taming Prolonged Ionic Drift–Diffusion Dynamics for Brain‐Inspired Computation
Inoue, H., Tamura, H., Kitoh, A., Chen, X., Byambadorj, Z., Yajima, T., Hotta, Y., Iizuka, T., Tanaka, G., & Inoue, I. H. (2024) · Advanced Materials · doi:10.1002/adma.202407326 · Cited by 9
Zou, J., Feng, Z., Qian, Z., Wu, Z., Zheng, J., Gao, L., Yang, R., Guo, W., Lin, Z., Yang, H., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202423267 · Cited by 13
Ion Intercalation‐Mediated MoS2 Conductance Switching for Highly Energy‐Efficient Memristor Synapse
Zhao, B., Zhao, X., Xun, X., Gao, F., Li, Q., Sun, J., Ouyang, T., Liao, Q., & Zhang, Y. (2025) · Advanced Electronic Materials · doi:10.1002/aelm.202400633 · Cited by 10
Moon, S., Park, C., Jung, Y. K., Min, K., Ko, H., & Yoon, T. (2025) · Journal of Materials Chemistry C · doi:10.1039/d4tc05041j · Cited by 9
Artificial transneurons emulate neuronal activity in different areas of brain cortex
Midya, R., Pawar, A. S., Pattnaik, D. P., Mooshagian, E., Borisov, P., Albright, T. D., Snyder, L. H., Williams, R. S., Yang, J. J., Balanov, A. G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62151-9 · Cited by 14
Self-citationMulti-timescale temporal processing for ion-motion memristor-based physical reservoir computing
Hwang, S. I., Yang, D. Y., Cho, H. Y., & Yoon, J. H. (2025) · Device · doi:10.1016/j.device.2025.100907 · Cited by 11
Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes
Moraitis, T., Sebastian, A., Boybat, I., Gallo, M. L., Tůma, T., & Eleftheriou, E. S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966072 · Cited by 10
“Even though in this particular demonstration, we used the PCM devices mostly for synaptic weight storage and weight updates, one could foresee the development of memristive devices in which also the short-term synaptic dynamics could be realized using the device physics [14].”
MethodIonically Mediated Mechanical Deformation Associated with Memristive Switching
Sriboriboon, P., Qiao, H., Kang, S., et al. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202103145 · Cited by 9
Mambretti, F., Mirigliano, M., Tentori, E., Pedrani, N., Martini, G., Milani, P., & Galli, D. E. (2022) · Scientific Reports · doi:10.1038/s41598-022-15996-9 · Cited by 9
Liu, C., Wang, Y., Zhang, T., Yuan, R., & Yang, Y. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202201060 · Cited by 9
Asnaz, O. H., Drewes, J., Elis, M., Strunskus, T., Greiner, F., Polonskyi, O., Faupel, F., Kienle, L., Vahl, A., & Benedikt, J. (2023) · Nanoscale Advances · doi:10.1039/d2na00806h · Cited by 9
Recent progress in bio-voltage memristors working with ultralow voltage of biological amplitude
Fu, T., Fu, S., & Yao, J. (2023) · Nanoscale · doi:10.1039/d2nr06773k · Cited by 9
Femtosecond Laser-Induced Nano-Joining of Volatile Tellurium Nanotube Memristor
Yu, Y., Joshi, P. C., Bridges, D., Fieser, D., & Hu, A. (2023) · Nanomaterials · doi:10.3390/nano13050789 · Cited by 9
“The device also exhibits a symmetrical behavior for negative sweeps, which indicates that TeNT has a volatile memristor feature at both polarities [52].”
BackgroundZhang, Z., Zhan, G., Gan, W., Cheng, Y., Zhang, X., Peng, Y., Tang, J., Zhang, F., Huo, J., Xu, G., et al. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300275 · Cited by 9
A Self‐Oscillated Organic Synapse for In‐Memory Two‐Factor Authentication
Liu, S., Zhong, X., Li, Y., Guo, B., He, Z., Wu, Z., Liu, S., Guo, Y., Shi, X., Chen, W., et al. (2024) · Advanced Science · doi:10.1002/advs.202401080 · Cited by 8
Chen, X., Gong, B., Li, Y., Wang, W., Huang, X., Huang, Y., Xiao, H., & Zhu, L. Q. (2024) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3397233 · Cited by 9
“In recent years, fundamental synaptic plasticity behaviors have been simulated on both two-terminal memristors [13], [14], [15] and three-terminal transistors [16], [17], [18].”
BackgroundA case for multiple and parallel RRAMs as synaptic model for training SNNs
Shukla, A., Prasad, S., Lashkare, S., & Ganguly, U. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489429 · Cited by 10
Wan, X., Gao, F., Lian, X., Ji, X., Hu, E., He, L., Tong, Y., & Guo, Y. (2018) · Japanese Journal of Applied Physics · doi:10.7567/jjap.57.060303 · Cited by 9
Nanoscale Ring-Shaped Conduction Channels with Memristive Behavior in BiFeO3 Nanodots
Li, Z., Fan, Z. H., & Zhou, G. (2018) · Nanomaterials · doi:10.3390/nano8121031 · Cited by 9
Self-citationArtificial synaptic device based on a multiferroic heterostructure
Lu, P., Shen, J., Shang, D., et al. (2019) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab3b33 · Cited by 9
Graphene-Based Artificial Synapses with Tunable Plasticity
Wang, H., Laurenciu, N. C., Jiang, Y., et al. (2021) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3447778 · Cited by 9
Mimicking Melanopsin Behavior with Integrated Photoregulated Memory
Kumar, M., & Seo, H. (2021) · Advanced Optical Materials · doi:10.1002/adom.202101372 · Cited by 9
Materials challenges and opportunities for brain-inspired computing
Zhao, Y., Kang, J., & Ielmini, D. (2021) · MRS Bulletin · doi:10.1557/s43577-021-00205-1 · Cited by 9
Akbari, M. K., & Zhuiykov, S. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c17584 · Cited by 10
‘Stateful’ threshold switching for neuromorphic learning
Zhong, Z., Jiang, Z., Huang, J., Gao, F., Hu, W. H., Zhang, Y., & Chen, X. (2022) · Nanoscale · doi:10.1039/d1nr05502j · Cited by 9
Cha, J., Jang, B. C., Oh, J., Lee, C., Yang, S. Y., Park, H., Im, S. G., & Choi, S. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200018 · Cited by 10
Abd, H., & Konig, A. S. (2022) · Journal of sensors and sensor systems · doi:10.5194/jsss-11-233-2022 · Cited by 8
Electronegative metal dopants improve switching variability in Al2O3 resistive switching devices
Tan, Z. J., Somjit, V., Toparlı, Ç., Yildiz, B., & Fang, N. X. (2022) · Physical Review Materials · doi:10.1103/physrevmaterials.6.105002 · Cited by 11
Investigating Series and Parallel Oxide Memtransistors for Tunable Weight Update Properties
Kang, S., Yang, S., Lee, D., Kim, S., Suh, J., & Lee, H. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00325 · Cited by 9
Unconventional computing with diffusive memristors
Wang, Z., Midya, R., Joshi, S., Jiang, H., Li, C., Lin, P., Song, W., Rao, M., Li, Y., Barnell, M. D., et al. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351882 · Cited by 12
“drift memristors provides a direct functional emulation of both short-term and long-term plasticity of synapses for power efficient learning required for neuromorphic computing [6].”
BackgroundSelf-citationHardware implementation of classical conditioning with iron-oxide-based memristors
Wan, X., Liang, D., Gao, F., Lian, X., & Tong, Y. (2018) · Applied Physics Express · doi:10.7567/apex.11.114601 · Cited by 9
Koizumi, R., Aiba, A., Kaneko, S., Fujii, S., Nishino, T., & Kiguchi, M. (2019) · Nanotechnology · doi:10.1088/1361-6528/aafc79 · Cited by 9
A TaOx-Based Electronic Synapse With High Precision for Neuromorphic Computing
Liu, S., Li, K., Sun, Y. K., Zhu, X. J., Li, Z., Song, B., Liu, H., & Li, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2961166 · Cited by 9
“Neuromorphic computing, learning from the information processing mechanism of biological neural network, is considered as one of the most promising candidates for next generation high-efficient computing systems [5], [6].”
BackgroundSelf-citationMemristive brain-like computing
Wen, X.-Y., Wang, Y.-S., He, Y., Miao, X., & Hubei Yangtze Memory Laboratories, W. 4. (2022) · Acta Physica Sinica · doi:10.7498/aps.71.20220666 · Cited by 8
Sun, J. W., Wang, Z., Wang, S., Cao, Y., Gao, H., Wang, H., Ma, X., & Hao, Y. (2023) · Applied Physics Letters · doi:10.1063/5.0146341 · Cited by 8
Self-citationTea helps neuromorphic computing: flexible memristors from tea polyphenols
Zhang, W., Li, X., Zhang, Z., Sun, W., & Zhang, H. (2024) · Journal of Materials Chemistry C · doi:10.1039/d4tc00440j · Cited by 9
Wang, K., Ren, S., Jia, Y., & Yan, X. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.109765 · Cited by 9
Light-Controlled Threshold Switching Memristive Neuron Devices for Color Photoreceptor
Shan, X., Liu, Z., Tao, Y., et al. (2024) · IEEE Electron Device Letters · doi:10.1109/led.2024.3435437 · Cited by 8
“When the voltage lower than the V hold , the CFs rupture at the most vulnerable location and diffuses into dielectric film [25], [28], [29], then the TSM spontaneously returns to the HRS.”
BackgroundMethodSelf-citationMehta, S. K., Mondal, I., Yadav, B., & Kulkarni, G. U. (2024) · Nanoscale · doi:10.1039/d4nr02748e · Cited by 8
High-speed Ta2O5-based threshold switching memristor for LIF neurons
Yao, L., Ma, C., He, Z., et al. (2024) · Journal of Applied Physics · doi:10.1063/5.0221088 · Cited by 8
Investigation of the TaOx unipolar switching memory on high efficiency computing
Du, C., Ji, X., Dong, Z., et al. (2024) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2024.177020 · Cited by 8
Volatile resistive switching characteristics of molecular beam epitaxy grown HfO2 thin films
Dehury, T., Kumar, S., Pütter, S., et al. (2024) · Applied Surface Science · doi:10.1016/j.apsusc.2024.162060 · Cited by 8
Lee, S., Lee, S., Cho, Y., et al. (2024) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2024.109241 · Cited by 8
Liu, L., Zhang, X., Tian, R., Zhang, Q., Zhang, M., Zhang, Y., & Gan, X. (2025) · Applied Physics Reviews · doi:10.1063/5.0235614 · Cited by 8
Ionically gated transistors based on two-dimensional materials for neuromorphic computing
Xu, K. W., & Fullerton‐Shirey, S. K. (2025) · 2D Materials · doi:10.1088/2053-1583/adb8c3 · Cited by 8
Simulations of biological associative learning behaviors using Cs2SnI6 perovskite memristors
Gao, J., Meng, L., Li, H., et al. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.163362 · Cited by 8
Revolutionizing neuromorphic computing with memristor-based artificial neurons
Chen, Y., Zhang, G., Liu, F., Wu, B., Deng, Y., Gao, D., & Zhang, Y. (2025) · Journal of Semiconductors · doi:10.1088/1674-4926/24110006 · Cited by 9
More-than-Moore Approaches Implemented Using van der Waals Heterostructures
Lee, S., Kim, Y. K., Noh, J., Jang, B. C., & Lee, S. (2025) · ACS Nano · doi:10.1021/acsnano.5c07260 · Cited by 9
Brain‐Inspired In‐Memory Data Pruning and Computing with TaO x Mem‐Selectors
Li, Y., Lai, J., Wang, S., Lin, N., Zheng, X., Sun, W., Dong, D., Xu, X., Ma, H., Zhang, F., et al. (2025) · Advanced Materials · doi:10.1002/adma.202502168 · Cited by 10
Self-citationLiu, Z., Fang, Y., Liu, Q., Tian, B., & Zhao, C. (2026) · Nano-Micro Letters · doi:10.1007/s40820-025-02028-0 · Cited by 14
“Specifically, existing synaptic plasticity research largely centers on basic transitions between STM and LTM states, modulation between excitatory and inhibitory synaptic activities, and optimization of linearity and symmetry in synaptic weight updating [15].”
Memristive cellular neural networks for fast in-pixel computing
Ravichandran, V., Huang, Y., Flannery, B., Molom-Ochir, T., Maurer, T. E., Asapu, S., Abdel-Maksoud, A., Heermance, N., Yoo, R., Tackie, J., et al. (2026) · Nature Electronics · doi:10.1038/s41928-025-01555-3 · Cited by 10
“The CMOS-based nonlinear cell dynamics could be realized using a simple diffusive memristor, whose intrinsic nonlinear behaviour can replicate the cell’s nonlinear dynamics and reduce the circuit complexity 47 .”
Self-citationVan der Waals materials for energy-efficient electronic devices
Hwang, E., Hong, H., Lee, Y., Joo, Y., Yer, S., Cho, S., & Yang, H. (2026) · Nature Reviews Materials · doi:10.1038/s41578-025-00886-z · Cited by 14
On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits
Ferri, A. C., Rapoport, A., Fierens, P. I., Patterson, G. A., Miranda, E. A., & Suñé, J. (2019) · Materials · doi:10.3390/ma12142260 · Cited by 8
Reservoir Computing-Based Design of ZnO Memristor-Type Digital Identification Circuits
Wang, L., Zhang, Y., Guo, Z., Wu, Z., Chen, X., & Du, S. Q. (2022) · Micromachines · doi:10.3390/mi13101700 · Cited by 9
“…are well-suited for Among them, the nonlinear dynamic response characteristics possessed by memristors are well-suited for RC system hardware [39,40], but this has certain limitations, such as the input signal duration and rate, which are strictly limited to a certain range based on the…”
Electret-Based Vertical Organic Synaptic Transistor With MXene for Neural Network-Based Computation
Zou, Y., Li, E., Yu, R., Gao, C., Yu, X., Zeng, B., Yang, Q., Guo, T., & Chen, H. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3211478 · Cited by 8
Artificial synapse based on 1,4-diphenylbutadiyne with femtojoule energy consumption
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Self-citationPseudo-transistors for emerging neuromorphic electronics
Fu, J., Wang, J., He, X., Ming, J., Wang, L., Wang, Y., Shao, H., Zheng, C., Xie, L., & Ling, H. (2023) · Science and Technology of Advanced Materials · doi:10.1080/14686996.2023.2180286 · Cited by 8
“Memristor works on the principle of electric field-driven resistance transitions, mainly including resistive random access memory (RRAM) [19,20] and phase-change memory (PCM) [21].”
BackgroundJiang, Q., Zhang, B., Yan, Q., Fan, F., Liu, G., & Chen, Y. (2023) · Journal of Materials Chemistry C · doi:10.1039/d3tc00120b · Cited by 8
Intermittent Defect Fluctuations in Oxide Heterostructures
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A memristor-based circuit approximation of the Hindmarsh–Rose model
Jenderny, S., Ochs, K., & Hövel, P. (2023) · The European Physical Journal B · doi:10.1140/epjb/s10051-023-00578-z · Cited by 8
“This is especially because they can be used for synapse realizations [27,28].”
BackgroundSun, Y., Yuan, Q., Wang, Y., Li, B., & Wen, D. (2023) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.3c05027 · Cited by 8
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“To resolve these matters, resistive random-access memory (RRAM) devices have been considered a promising candidate to replace conventional flash memory as a next-generation memory model [2–5].”
BackgroundA ternary gate-connected threshold switching thin-film transistor
Woo, K. S., Lee, Y.-H., Han, J., Park, T. W., Jang, Y. H., & Hwang, C. S. (2024) · Applied Physics Letters · doi:10.1063/5.0187155 · Cited by 8
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A memristive-photoconductive transduction methodology for accurately nondestructive memory readout
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“…have revealed the structural and element evolution of resistive memories during the set/reset process, convin-cingly displaying that metallic particles or oxygen species could diffuse to guide conductive fi lament formation and fuse, resulting in resistance variation of memristive layer 14 – 21 .”
BackgroundSelf-citationvan der Ham, A., Ahn, W., Oh, J., et al. (2025) · ACS Nano · doi:10.1021/acsnano.4c13613 · Cited by 8
Reset switching statistics of TaOx-based Memristor
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Self-citationA mean-field model of memristive circuit interaction
Caravelli, F., & Barucca, P. (2018) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/122/40008 · Cited by 8
Multifunctional Optoelectronic Device Based on Resistive Switching Effects
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A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems
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“More interestingly, the processes responsible for such behavior also resemble the physiological phenomena that create short-term synaptic plasticity in vivo, unlike other devices used to mimic short term plasticity [6] [7].”
BackgroundSolid-State Electrochemical Process and Performance Optimization of Memristive Materials and Devices
Xue, W., Xu, X., & Liu, G. (2019) · Chemistry · doi:10.3390/chemistry1010005 · Cited by 8
Directed Assembly of Nanoparticle Threshold‐Selector Arrays
Speckbacher, M., Rinderle, M., Kaiser, W., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900098 · Cited by 8
Selector devices for emerging memories
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A Stochastic Compact Model Describing Memristor Plasticity and Volatility
Malik, A., Papavassiliou, C., & Stathopoulos, S. (2021) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs53924.2021.9665591 · Cited by 8
Forming-Free Nonfilamentary Resistive Switching in W/WO3 – x/HFO2/Pd Structures
Koroleva, A. A., Kozodaev, M. G., Lebedinskiǐ, Y. Y., & Markeev, A. M. (2021) · Nanobiotechnology Reports · doi:10.1134/s2635167621060094 · Cited by 9
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
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