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.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The first one depends on temperature, potential profile, and electric drift, thus, this time scale changes depending on voltage pulse intensity and temperature.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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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- Luo, Q. et al. Electron Devices Meeting (IEDM) 10.14.11–10.14.14 (IEEE International, 2015).
Cited by
Sorted by citations
Items 1,401–1,500 of 2,351
Köymen, I., Gokturk, P. A., Kocabas, C., & Süzer, Ş. (2019) · Faraday discussions · doi:10.1039/c8fd00129d · Cited by 9
Light Driven Active Transition of Switching Modes in Homogeneous Oxides/Graphene Heterostructure
Chen, X., Zeng, K., Zhu, X. B., Ding, G., Zou, T., Zhang, C., Zhou, K., Zhou, Y., & Han, S. (2019) · Advanced Science · doi:10.1002/advs.201900213 · Cited by 12
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RetractedNeuromorphic computing systems based on flexible organic electronics
Keene, S. T., Gkoupidenis, P., & van de Burgt, Y. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818890-3.00018-7 · Cited by 12
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E-Synapse Based on Lead-Free Organic Halide Perovskite (CH3NH3)3Sb2Cl9 for Neuromorphic Computing
Ni, Z., Zhu, Y., Ju, S., Xu, Z., Tian, F., Hu, H., Guo, T., & Li, F. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3095434 · Cited by 12
MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection
Jiang, J., Tian, F., Liang, J., Shen, Z., Liu, Y., Zheng, J., Wu, H., Zhang, Z., Fang, C., Zhao, Y., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.761127 · Cited by 11
“Due to the energy-efficient features of memristors, attempts utilizing memristors to build synapses (Wang et al., 2017) and neurons (Wang et al.”
MethodLeng, K., Zhu, X., Ma, Z., et al. (2022) · Nanomaterials · doi:10.3390/nano12030311 · Cited by 12
“However, the realization of resistive switching from TS to MS by tuning the Si dangling bond conductive pathway in Si-based RRAM devices is less reported [25–30].”
BackgroundYang, Q., Huang, J., Chen, Q., Chen, C., Chen, H., & Guo, T. (2022) · Journal of Materials Chemistry C · doi:10.1039/d2tc00211f · Cited by 12
Qin, J., Zhu, B., Wang, C., Zhu, C., Sun, R., Zhen, L., Chai, Y., & Xu, C. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200721 · Cited by 12
Improved Environment Stability of Y2O3 RRAM Devices with Au Passivated Ag Top Electrodes
Kim, H.-I., Lee, T., Lee, W., et al. (2022) · Materials · doi:10.3390/ma15196859 · Cited by 12
Wang, J., Pan, X., Luo, W., Shuai, Y., Xie, Q., Xu, J., Song, Z., Wu, C., & Zhang, W. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202201064 · Cited by 12
Kim, K., Kim, H.-I., Lee, T., et al. (2023) · Semiconductor Science and Technology · doi:10.1088/1361-6641/acbb99 · Cited by 12
“To solve this issue, resistive random-access memory (RRAM) has recently been considered as a novel data-storage device [4–6].”
Electric-Field-Induced Metal Filament Formation in Cobalt-Based CBRAM Observed by TEM
Choi, Y.-J., Bang, S., Kim, T., Hong, K., Kim, S., Kim, S., Park, B., & Choi, W. Y. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00034 · Cited by 13
Lee, J., Kwon, O., Jeon, B., & Kim, S. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3317003 · Cited by 12
“Importantly, the retention loss of our device can be perceived as analogous to the STM characteristic in biological synapses [18], [34], [35].”
BackgroundFlexible Meso Electronics and Photonics Based on Cocoon Silk and Applications
Lu, C., Wang, X., & Liu, X. (2024) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.4c00254 · Cited by 13
Wu, C., Zhang, Y., Zhou, X., Li, D., Park, J. H., An, H., Sung, S., Lin, J., Guo, T., Li, F., et al. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c02145 · Cited by 12
Wang, H., Gao, C., Sun, K., Shi, C., & Yan, X. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.3040208 · Cited by 12
Carstens, N., Vahl, A., Gronenberg, O., Strunskus, T., Kienle, L., Faupel, F., & Hassanien, A. (2021) · Nanomaterials · doi:10.3390/nano11020265 · Cited by 14
“Diffusive memristive switching has been adapted to emulate short-term plasticity in neural systems [15], for the implementation of random number generators [16], or for selector devices [17].”
BackgroundKim, H. S., Sahota, A., Mohan, J., Lucero, A., Jung, Y.-C., Kim, M., Lee, J.-S., Choi, R., Kim, S., & Kim, J. (2021) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.1c00197 · Cited by 8
An Optimized MOS-Based High Frequency Charge-Controlled Memcapacitor Emulator
Ananda, Y. R., Satyanarayan, G. S., & Trivedi, G. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3221314 · Cited by 12
“Memelements find themselves as a suitable building block in the realization of several applications, which include chaotic circuits [5], neuromorphic computing [6], programmable analog circuits [7], and bio-inspired applications [8], [9].”
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“Introducing continuous or stepwise changing migration barriers through defect engineering can regulate the smooth movement of defects [154, 155].”
Quantum Memristors with Quantum Computers
Guo, Y., Albarrán-Arriagada, F., Alaeian, H., Solano, E., & Barrios, G. A. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.024082 · Cited by 11
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Nanograin network memory with reconfigurable percolation paths for synaptic interactions
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Gamma radiation-induced nanodefects in diffusive memristors and artificial neurons
Pattnaik, D. P., Andrews, C., Cropper, M. D., Gabbitas, A., Balanov, A. G., Savel'ev, S. E., & Borisov, P. (2023) · Nanoscale · doi:10.1039/d3nr01853a · Cited by 13
Self-citationA high-speed true random number generator based on Ag/SiNx/n-Si memristor
Yan, X., Zhang, Z., Guan, Z., Fang, Z., Zhang, Y., Zhao, J., Sun, J., Han, X., Niu, J., Wang, L., et al. (2023) · Frontiers of Physics · doi:10.1007/s11467-023-1331-1 · Cited by 11
Recent advances of carbon dot-based memristors: Mechanisms, devices, and applications
Cao, Y., Hao, H., Chen, L., et al. (2023) · Applied Materials Today · doi:10.1016/j.apmt.2023.102032 · Cited by 11
Sun, Y., Li, Z., Li, Q., Wang, Y., Meng, X., & Yuan, Q. (2023) · ACS Materials Letters · doi:10.1021/acsmaterialslett.3c01271 · Cited by 11
Self-citationGriffin, K., & Redmond, G. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c13735 · Cited by 14
Zhu, J., Liao, J., Feng, J., Jiang, Y., Tang, X., Guo, X., Li, W., Tang, Z., & Zhou, Y. (2024) · International Journal of Applied Ceramic Technology · doi:10.1111/ijac.14693 · Cited by 11
Dynamic memristor for physical reservoir computing
Zhang, Q., Ouyang, W.-L., Wang, X., Yang, F., Chen, J., Wen, Z., Liu, J.-X., Wang, G., Liu, Q., & Liu, F. (2024) · Nanoscale · doi:10.1039/d4nr01445f · Cited by 12
Li, Y., Xiong, Y., Zhai, B., Yin, L., Yu, Y., Wang, H., & He, J. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c00212 · Cited by 11
Spiking Reservoir Computing Based on Stochastic Diffusive Memristors
Ma, Z., Ge, J., & Pan, S. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400469 · Cited by 12
Integration of Ag-based threshold switching devices in silicon microchips
Alharbi, O., Pazos, S. M., Zhu, K., Aguirre, F. L., Yuan, Y. J., Li, X., Wu, H., & Lanza, M. (2024) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2024.100837 · Cited by 12
2D materials-based crossbar array for neuromorphic computing hardware
Lee, H. J., Park, S., Kim, J., Park, M. H., Kim, J., Lim, J. A., & Jang, H. W. (2024) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ad7755 · Cited by 11
Modelling of memristor networks and the effective memristor
Huijzer, A.-M., van der Schaft, A. J., & Besselink, B. (2024) · Automatica · doi:10.1016/j.automatica.2024.111922 · Cited by 12
Boolean Computation in Single‐Transistor Neuron
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Ganeriwala, M. D., Toral-López, A., Calaforra-Ayuso, E., et al. (2024) · ACS Applied Nano Materials · doi:10.1021/acsanm.4c04769 · Cited by 11
Integrated artificial neurons from metal halide perovskites
de Boer, J. J., & Ehrler, B. (2025) · Materials Horizons · doi:10.1039/d4mh01729c · Cited by 11
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Light-activated electroforming in ITO/ZnO/p-Si resistive switching devices
Blázquez, O., Frieiro, J. L., López-Vidrier, J., Guillaume, C., Portier, X., Labbé, C., Hernández, S., & Garrido, B. (2019) · Applied Physics Letters · doi:10.1063/1.5125844 · Cited by 11
Photonic Kernel Machine Learning for Ultrafast Spectral Analysis
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Thermal Effects on Initial Volatile Response and Relaxation Dynamics of Resistive RAM Devices
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Stochastic Resonance in Organic Electronic Devices
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Shin, H. S., Song, H., Kim, D. H., Martinez, A., Cheong, W. H., & Kim, K. M. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.2c00320 · Cited by 12
Resistive Switching of Self‐Assembled Silver Nanowire Networks Governed by Environmental Conditions
Schneider, J. I. D., Angelomé, P. C., Granja, L. P., et al. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200631 · Cited by 11
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Emulating the Signal Transmission in a Neural System Using Polymer Membranes
Kim, D., & Lee, J. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c12166 · Cited by 11
Hasina, D., Mandal, A., Basu, N., Srivastava, S. K., Mitra, A., & Som, T. (2022) · Applied Surface Science · doi:10.1016/j.apsusc.2022.155563 · Cited by 11
Threshold Switching in Forming-Free Anodic Memristors Grown on Hf–Nb Combinatorial Thin-Film Alloys
Zrinski, I., Zavašnik, J., Duchoslav, J., Hassel, A. W., & Mardare, A. I. (2022) · Nanomaterials · doi:10.3390/nano12223944 · Cited by 12
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AgBiS 2 quantum dot based multilevel resistive switching for low power electronics
Sharma, H., Saini, N., Kumar, A., & Srivastava, R. (2023) · Journal of Materials Chemistry C · doi:10.1039/d3tc01050c · Cited by 12
Roncador, A., Jiménez-Garduño, A. M., Pasquardini, L., et al. (2017) · Biophysical Chemistry · doi:10.1016/j.bpc.2017.04.010 · Cited by 11
Kim, S., Lee, Y., Kim, H., & Choi, S. (2019) · Nanoscale · doi:10.1039/c9nr06715a · Cited by 11
Acevedo, W. R., van den Bosch, C. A. M., Aguirre, M. H., et al. (2020) · Applied Physics Letters · doi:10.1063/1.5131854 · Cited by 11
Reinforcement learning in synthetic gene circuits
Racovita, A., & Jaramillo, A. (2020) · Biochemical Society Transactions · doi:10.1042/bst20200008 · Cited by 11
Zhang, J., Yang, T., Wang, J., Zhao, J., & Yan, X. (2020) · Science China Materials · doi:10.1007/s40843-020-1367-x · Cited by 11
Memristors With Controllable Data Volatility by Loading Metal Ion-Added Ionic Liquids
Sato, H., Shima, H., Nokami, T., Itoh, T., Honma, Y., Naitoh, Y., Akinaga, H., & Kinoshita, K. (2021) · Frontiers in Nanotechnology · doi:10.3389/fnano.2021.660563 · Cited by 11
“Various materials including solid oxide and sulfides exhibiting voltage resistance change are intensively investigated in order to realize artificial synaptic devices which mimic the information processing function of biological synapse (Hasegawa et al., 2012; Wang et al., 2016; Sun J. et al., 2019; Yan et al., 2019b).”
BackgroundThreshold Switching of Ag-Ga2Te3 Selector with High Endurance for Applications to Cross-Point Arrays
Kim, J., Lee, J., Kang, M., & Sohn, H. (2021) · Nanoscale Research Letters · doi:10.1186/s11671-021-03585-0 · Cited by 11
Sebastianella, G. C., Lauro, M. D., Murgia, M., et al. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100755 · Cited by 11
Building resistive switching memory having super-steep switching slope with in-plane boron nitride
Wang, Y., Huang, Z., Chen, X., et al. (2021) · Nanotechnology · doi:10.1088/1361-6528/ac4289 · Cited by 11
Aspects of the a-TiOx memristor active medium technology
Палагушкин, А. Н., Рощупкин, Д. В., Yudkin, F. A., et al. (2018) · Journal of Applied Physics · doi:10.1063/1.5038919 · Cited by 11
The mise en scéne of memristive networks: effective memory, dynamics and learning
Caravelli, F. (2017) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2017.1320796 · Cited by 12
“Although here we describe only the theoretical properties of the dynamics, this type of memristors can be experimentally realised using atomic switches, which are based on silver ions filaments [27,28].”
BackgroundLee, J.-H., Cho, I., Beak, C., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202515933 · Cited by 10
Zhu, L. Q., Zhang, F., Chen, P., et al. (2026) · Nano Letters · doi:10.1021/acs.nanolett.5c05934 · Cited by 10
Tunable synaptic working memory with volatile memristive devices
Ricci, S., Kappel, D., Tetzlaff, C., Ielmini, D., & Covi, E. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ad01d6 · Cited by 10
“The switching to the LRS occurs because, as the voltage exceeds a given threshold voltage V T , Ag ions migrate across the oxide layer and form the CF, thus bringing the device in an LRS ( set operation , figure 2(A) [27, 31, 32].”
BackgroundReview of solid-state proton devices for neuromorphic information processing
Pati, S. P., & Yajima, T. (2024) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ad297b · Cited by 10
Multilevel Logic Operation and Artificial Synaptic Plasticity Based on Pectin Transient Memristor
Liu, M., Yuan, Q., Li, B., & Sun, Y. (2024) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c01611 · Cited by 11
Nanolayered NbO2-Based Dynamic Memristor for Leaky Integrate and Fire Neuron
Wang, Y., Wang, W., Xu, H., Liu, S., Cao, R., Sun, Y. K., Tong, P., Song, B., & Li, Q. (2024) · ACS Applied Nano Materials · doi:10.1021/acsanm.4c01087 · Cited by 11
Srikimkaew, O., Azhari, S., Banerjee, D., Usami, Y., & Tanaka, H. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400360 · Cited by 13
Deepthi, M. S., Shashidhara, H. R., Raghu, J., & Rudraswamy, S. B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128758 · Cited by 10
Memristor-based in-situ convolutional strategy for accurate braille recognition
Zhang, X., Qin, C., Peng, W., Qin, N., Cheng, E., Wu, J., Fan, Y., Yang, Q., & Chen, H. (2024) · Science China Materials · doi:10.1007/s40843-024-3122-7 · Cited by 10
“S1) [29,30].”
Stress-induced artificial neuron spiking in diffusive memristors
Pattnaik, D. P., Sharma, Y., Savel'ev, S. E., Borisov, P., Akhter, A., Balanov, A. G., & Ferreira, P. (2024) · Communications Engineering · doi:10.1038/s44172-024-00315-z · Cited by 14
Self-citationA Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons
Wang, Y., Zhang, Y., Wang, Y., Wang, X., Zhang, H., Xu, R., & Tong, Y. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400421 · Cited by 10
Ion-modulation optoelectronic neuromorphic devices: mechanisms, characteristics, and applications
Meng, X., Gao, R., Zhu, X., & Li, R. (2025) · Journal of Semiconductors · doi:10.1088/1674-4926/24100025 · Cited by 13
On-receptor computing utilizing ZnO-based flexible memristor for wearable electronics
Hasina, D., & Mukherjee, D. (2025) · Applied Materials Today · doi:10.1016/j.apmt.2025.102664 · Cited by 10
Lin, Y.-H., Pan, J., Zhuang, X., Guo, Q., & Li, Y. (2025) · Nano Research · doi:10.26599/nr.2025.94907660 · Cited by 10
Hardware Implementation of On‐Chip Hebbian Learning Through Integrated Neuromorphic Architecture
Kim, S., Im, S., Kwak, I. C., Lee, J., Roe, D. G., Ju, H., & Cho, J. H. (2025) · Advanced Materials · doi:10.1002/adma.202506920 · Cited by 12
Embracing the era of neuromorphic computing
Wang, Y., Yang, Y., Hao, Y., & Huang, R. (2021) · Journal of Semiconductors · doi:10.1088/1674-4926/42/1/010301 · Cited by 10
Pavlovian conditioning achieved via one-transistor/one-resistor memristive synapse
Cheng, Y., Lin, Y., Zeng, T., Shan, X., Wang, Z., Zhao, X., Ielmini, D., Xu, H., & Liu, Y. (2022) · Applied Physics Letters · doi:10.1063/5.0086867 · Cited by 9
Self-citationModeling of a diffusive memristor based on the DT-FNT mechanism transition
Dai, Y., Zou, J., Feng, Z., et al. (2022) · Semiconductor Science and Technology · doi:10.1088/1361-6641/ac78ff · Cited by 10
“Diffusive memristors, based on active metals (Ag or Cu), have internal dynamics that are functionally similar to ionic dynamics in bio-neurons and synapses [9].”
InfluentialZhao, X., Chang, K., Liu, B., Jiang, K., Hu, C., Wang, Y., & Wang, H. (2022) · Applied Physics Letters · doi:10.1063/5.0098750 · Cited by 10
Emulating synaptic plasticity in ionic liquid-gated zinc tin oxide neuromorphic transistor
Shaji, M., Jose, F. P., Saji, K. J., Antony, A., & Jayaraj, M. K. (2022) · Journal of Materials Science Materials in Electronics · doi:10.1007/s10854-022-08817-2 · Cited by 10
Mott Memristors and Neuronal Ion Channels: A Qualitative Analysis
Nabil, A., Kumar, T. N., & Almurib, H. A. F. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3221735 · Cited by 11
“A new category of memristive devices that are volatile [20] can also be used to implement short-term synaptic plasticity [17], [21].”
BackgroundMethodLin, Y., Liu, W., & Hang, C. (2023) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2022.113006 · Cited by 11
Threshold Switching Memristor-Based Voltage Regulative Circuit
Wu, Z., Zhang, C., Zou, J., Peng, C., & Wu, X. (2022) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2022.3221140 · Cited by 11
Simulating the filament morphology in electrochemical metallization cells
Buttberg, M., Valov, I., & Menzel, S. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/acdbe5 · Cited by 11
Liu, G., Wang, W., Guo, Z., Jia, X., Zhao, Z., Zhou, Z., Niu, J., Duan, G., & Yan, X. (2023) · Nanoscale · doi:10.1039/d3nr00510k · Cited by 10
Simultaneously high thermal stability and low power based on Cu-doped GeTe phase change material
Hu, Y., Lai, T., Zou, H., et al. (2018) · Materials Research Express · doi:10.1088/2053-1591/aaeeab · Cited by 10
Tin oxide artificial synapses for low power temporal information processing
Le, P. Y., Tran, H. N., Zhao, Z. C., McKenzie, D. R., McCulloch, D. G., Holland, A. S., Murdoch, B. J., & Partridge, J. G. (2019) · Nanotechnology · doi:10.1088/1361-6528/ab19c9 · Cited by 10
“devices typically operate based on the formation of metallic filaments [11, 12] or as a result of Joule heating at relatively high power [13, 14].”
BackgroundNeuromorphic computing with resistive switching memory devices
Ielmini, D., & Ambrogio, S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102584-0.00017-6 · Cited by 13
Ding, D., HuiLiu, Weng, Y., & Wang, N. (2019) · The European Physical Journal Plus · doi:10.1140/epjp/i2019-12688-8 · Cited by 10
Enhanced Spiking Neural Network with forgetting phenomenon based on electronic synaptic devices
Li, J., Xu, H. F., Sun, S., Liu, S., Li, N., Li, Q., Liu, H., & Li, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.030 · Cited by 10
Self-citation
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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