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.
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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Cited by
Sorted by citations
Items 1,101–1,200 of 2,351
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“A particularly promising class of memristors is the electrochemical metallization (ECM) type 8,9 , which uses oxide bulk materials 10 – 12 or two-dimensional (2D) materials 13 – 17 as the switching medium.”
Self-citationA solution processed metal–oxo cluster for rewritable resistive memory devices
Zhou, K., Ding, G., Zhang, C., et al. (2018) · Journal of Materials Chemistry C · doi:10.1039/c8tc05188g · Cited by 21
Wang, Y., Min, F., Cheng, Y., et al. (2021) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-021-00121-0 · Cited by 21
Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior
Zhao, Y., Pei, Y., Zhang, Z., Li, X., Wang, J., Yan, L., He, H. M., Zhou, Z., Zhao, J., Chen, J., et al. (2022) · Science China Materials · doi:10.1007/s40843-021-1925-x · Cited by 22
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A Memristor‐Based Bioinspired Multimodal Sensory Memory System for Sensory Adaptation of Robots
Pan, X., Wang, J., Deng, Z., Shuai, Y., Luo, W., Luo, W., Xie, Q., Xiao, Y., Tang, S. Y., Jiang, S., et al. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200031 · Cited by 21
Ultra-large dynamic range synaptic indium gallium zinc oxide transistors
Kwak, T., Kim, M.-J., Lee, W., Kim, E. C., Jang, J., Wang, G., Kim, T., Kim, Y. S., & Park, S. (2022) · Applied Materials Today · doi:10.1016/j.apmt.2022.101648 · Cited by 22
In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x -Based Neuristor Array
Zhang, H., Qiu, P., Lu, Y., Ju, X., Chi, D., Yew, K. S., Zhu, M., Wang, S. H., Wei, R., & Hu, W. (2023) · ACS Sensors · doi:10.1021/acssensors.3c01418 · Cited by 21
Mandal, R., Mandal, A., & Som, T. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102103 · Cited by 21
Lead-free perovskites-based photonic synaptic devices with zero electric energy consumption
Hao, D., Yang, D., Liang, H., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-3835-4 · Cited by 21
“Various artificial synaptic devices, such as memristors [6, 7], synaptic transistors [8], atomic-switch memory [9], have been developed to mimic fundamental synaptic behaviors by using a series of electrical pulses or light pulses.”
BackgroundWang, K., Liao, Y., Li, W., Li, J., Su, H., Chen, R., Park, J. H., Zhang, Y., Zhou, X., Wu, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47641-6 · Cited by 24
“Under the periodic increase and decrease of the applied voltage, radiative recombination does not occur at once but in batches, resulting in Mem-EL spikes with multi-peaks(Supplementary Figs.9, 10).”
Kim, M., Yoo, K., Jeon, S., Park, S. K., & Kim, Y. (2020) · Micromachines · doi:10.3390/mi11020154 · Cited by 21
“Due to these advantages, various emerging electronics such as neuromorphic circuits and systems have been demonstrated by utilizing the memristors as one of their key elements [8,9].”
BackgroundEffect of Ag Concentration Dispersed in HfOx Thin Films on Threshold Switching
Jeong, W. H., Han, J. H., & Choi, B. J. (2020) · Nanoscale Research Letters · doi:10.1186/s11671-020-3258-6 · Cited by 21
Sakellaropoulos, D., Bousoulas, P., Papakonstantinopoulos, C., Kitsios, S., & Tsoukalas, D. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3057841 · Cited by 21
“the pulse polarity and amplitude [22], [23].”
BackgroundSelf-Powered Memristive Systems for Storage and Neuromorphic Computing
Shi, J., Wang, Z., Tao, Y., Xu, H., Zhao, X., Lin, Y., & Liu, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.662457 · Cited by 24
“Brain-like artificial neural networks (ANNs) are currently gaining extensive attention as an evolving technology for artificial intelligence, enabling self-learning, speech recognition, and pattern recognition (Jiang et al., 2017; Van and Bohte, 2017; Peng et al., 2019; George et al., 2020; Kumar et al., 2020).”
BackgroundInfluentialSelf-citationBan, C., Wang, X., Zhou, Z., Mao, H., Cheng, S., Zhang, Z., Liu, Z., Li, H., Liu, J., & Huang, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-46884-4 · Cited by 21
Brain Organoid Computing for Artificial IntelligencePreprint
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Advancing Neural Networks: Innovations and Impacts on Energy Consumption
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Dynamic-quenching of a single-photon avalanche photodetector using an adaptive resistive switch
Zheng, J., Xue, X., Ji, C., Yuan, Y., Sun, K., Rosenmann, D., Wang, L., Wu, J., Campbell, J. C., & Guha, S. (2022) · Nature Communications · doi:10.1038/s41467-022-29195-7 · Cited by 22
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Dynamic resistive switching devices for neuromorphic computing
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“However, under high mobility and limited supply (figure 2(d)), a branched structure is formed, corresponding to the case in Au/SiO x N y :Ag/Au devices [11] depicted in figure 2(e).”
InfluentialResistive Switching Memristor: On the Direct Observation of Physical Nature of Parameter Variability
Wang, Z., Xiao, W., Yang, H., Zhang, S., Zhang, Y., Sun, K., Wang, T., Fu, Y., Wang, Q., Zhang, J., et al. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c19364 · Cited by 21
Self-citationA Bifunctional Memristor Enables Multiple Neuromorphic Computing Applications
Lyapunov, N., Zheng, X. D., Yang, K., Liu, H. M., Zhou, K., Cai, S., Ho, T. L., Suen, C. H., Yang, M., Zhao, J. X., et al. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202101235 · Cited by 20
HfO2-based memristor-CMOS hybrid implementation of artificial neuron model
Zhang, Y., Fang, Z., & Yan, X. (2022) · Applied Physics Letters · doi:10.1063/5.0091286 · Cited by 20
Athena, F. F., West, M. P., Basnet, P., Hah, J., Jiang, Q., Lee, W., & Vogel, E. M. (2022) · Journal of Applied Physics · doi:10.1063/5.0087001 · Cited by 21
Bio‐Voltage Memristors: From Physical Mechanisms to Neuromorphic Interfaces
Wang, S., Wang, R., Cao, Y., Ma, X., Wang, H., & Hao, Y. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202200972 · Cited by 21
Moon, S., Park, K., Chung, P. H., Sahu, D. P., & Yoon, T. (2023) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2023.171211 · Cited by 19
Qian, Y., Li, J., Shen, J., Ke, Y., Yu, S., Li, W., Xu, X. X., Ling, H., Huang, W., & Yi, M. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202300135 · Cited by 19
Bao, H., Yu, X., Zhang, Y., Liu, X., & Chen, M. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.114167 · Cited by 20
MAX Phase Ti2AlN for HfO2 Memristors with Ultra‐Low Reset Current Density and Large On/Off Ratio
Athena, F. F., Nnaji, M. O., Vaca, D., Tian, M., Buchmaier, W., Aabrar, K. A., Graham, S. L., Datta, S., Kumar, S., & Vogel, E. M. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202316290 · Cited by 21
Electrical characterization of MoS2 field-effect transistors with different dielectric polymer gate
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A Versatile and Accurate Compact Model of Memristor With Equivalent Resistor Topology
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“In an Au/SiOxNy:Ag/Au system [7], Ag atoms established conductive bridges under electrical bias and regrouped spontaneously under zero bias, which were responsible for the resistive switching.”
MethodAn On-Chip Trainable and the Clock-Less Spiking Neural Network With 1R Memristive Synapses
Shukla, A., & Ganguly, U. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2831618 · Cited by 20
“Such STDP has been demonstrated in various RRAM devices [13]–[19].”
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Cheng, C., Li, Y., Zhang, T., Fang, Y., Zhu, J., Liu, K., Xu, L., Cai, Y., Yan, X., Yang, Y., et al. (2018) · Journal of Applied Physics · doi:10.1063/1.5037962 · Cited by 20
Dissipative self-organization in optical space
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A Survey on Architecture Advances Enabled by Emerging Beyond-CMOS Technologies
Chen, A., Datta, S., Hu, X. S., Niemier, M. T., Simunic, T., & Yang, J. J. (2019) · IEEE Design and Test · doi:10.1109/mdat.2019.2902359 · Cited by 20
Self-citationGuo, Y., Hu, W. H., Zhang, C., Peng, Y., & Guo, Y. (2020) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab7517 · Cited by 21
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Breakthrough in high ON-state current based on Ag–GeTe 8 selectors
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Deterministic mechanisms of spiking in diffusive memristors
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Effect of the Pressure Exerted by Probe Station Tips in the Electrical Characteristics of Memristors
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A review of memristive reservoir computing for temporal data processing and sensing
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Nonlinear Ion Dynamics Enable Spike Timing Dependent Plasticity of Electrochemical Ionic Synapses
Huang, M., Xu, L., del Alamo, J. A., Li, J., & Yildiz, B. (2025) · Advanced Materials · doi:10.1002/adma.202418484 · Cited by 18
2D MoTe 2 memristors for energy-efficient artificial synapses and neuromorphic applications
Khan, R., Rehman, N. U., Kalluri, S., Elumalai, S., Saritha, A., Fakhar-e-alam, M., Ikram, M., Abdullaev, S. S., Rahman, N., & Sangaraju, S. (2025) · Nanoscale · doi:10.1039/d5nr01509j · Cited by 22
Amorphous boron nitride: synthesis, properties and device application
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“An a-BN can be deposited at temperatures below 250 °C using CVD and PLD, thereby overcoming the temperature barrier for integrating a-BN devices into CMOS technology [15, 118].”
Leng, K., Yu, X., Ma, Z., Li, W., Xu, J., Xu, L., & Chen, K. (2022) · Applied Physics Letters · doi:10.1063/5.0078332 · Cited by 20
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High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors
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Liu, H., Dong, Y., Galib, M., Cai, Z., Stan, L., Zhang, L., Suwardi, A., Wu, J., Cao, J. X., Tan, C. K. I., et al. (2022) · Advanced Materials · doi:10.1002/adma.202203209 · Cited by 19
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Ion migration induced bipolar diode effect and ferroelectricity modulation in CuInP2S6
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Profumo, F., Borghi, F., Falqui, A., & Milani, P. (2023) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/acd704 · Cited by 18
“The resistance measured during the read pulses shows a transient increasing behaviour on the timescale of seconds, suggesting the presence of diffusion-driven conduction [4] and demonstrating the device’s volatile nature [1].”
BackgroundMethodInfluentialMean Field Theory of Self‐Organizing Memristive Connectomes
Caravelli, F., Milano, G., Ricciardi, C., & Kuncic, Z. (2023) · Annalen der Physik · doi:10.1002/andp.202300090 · Cited by 19
Jang, B., Kim, J., Lee, J., et al. (2024) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2023.12.016 · Cited by 19
Chauhan, M. S., Choudhary, S. K., & Sharma, S. K. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202300724 · Cited by 20
Cai, D., Liu, Y., Wang, J., Zhao, T., Shen, M., Zhang, F., Jiang, Y., & Gu, D. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202314660 · Cited by 20
Das, S., Pal, V., Mukherjee, S., et al. (2024) · Advanced Optical Materials · doi:10.1002/adom.202400037 · Cited by 19
Short-Term Relaxation in HfOx/CeOx Resistive Random Access Memory With Selector
Hsieh, C., Chang, Y., Jeon, Y., et al. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2710955 · Cited by 19
Recent progress in memristors for stimulating synaptic plasticity
Zhang, C., Yan, C. H. E. N., Yi, M., Ying, Z. H. U., Li, T., Liu, L., Laiyuan, W. A. N. G., Xie, L., & Huang, W. (2018) · Scientia Sinica Informationis · doi:10.1360/n112017-00022 · Cited by 19
Biological Band-Pass Filtering Emulated by Oxide-Based Neuromorphic Transistors
Wan, X., He, Y., Nie, S., Shi, Y., & Wan, Q. C. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2869095 · Cited by 19
Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems
Sung, S. H., Kim, D. H., Kim, T., Kang, I., & Lee, K. J. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201900080 · Cited by 19
Ultrathin TiOx Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors
Xiao, M. W., Yeow, T. W.-K., Nguyễn, V. H., Muñoz‐Rojas, D., Musselman, K. P., Duley, W. W., & Zhou, Y. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900142 · Cited by 19
Maas, K., Villepreux, É., Cooper, D., et al. (2019) · Journal of Materials Chemistry C · doi:10.1039/c9tc03972d · Cited by 19
Montano, K., Milano, G., & Ricciardi, C. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4d86 · Cited by 20
“…equation: with g the normalized conductance and k p , k d the potentiation and depression coefficient respectively, defined as a function of input voltage only: (2) Equations (2) and (3) derive from the physical description of the ionic diffusion [29, 36, 37], making the model a physics-based one.”
BackgroundLi, R., Huang, P. C., Feng, Y., Zhou, Z., Zhang, Y., Ding, X., Liu, L., & Kang, J. (2022) · Micromachines · doi:10.3390/mi13030433 · Cited by 19
“Early research studies have proved that memristor devices can exhibit SRDP-like behaviors, including SiO x N y :Ag-based diffusive memristor [43], HfO x -based memristor [44], TiO x /AlO y -based [45] oxide memristor, AgInSbTe-based chalcogenide memristor [46], hybrid CMOS/memristor structure…”
BackgroundWang, C., Song, B., & Zeng, Z. (2017) · AIP Advances · doi:10.1063/1.5009717 · Cited by 19
Wells, R. A., & Robertson, A. W. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400121 · Cited by 18
An ultra high-endurance memristor using back-end-of-line amorphous SiC
Kapur, O. R., Guo, D., Reynolds, J. D., Newbrook, D. W., Han, Y., Beanland, R., Jiang, L., de Groot, C. H., & Huang, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-64499-2 · Cited by 19
Bio‐Realistic Synaptic‐Replicated “V” Type Oxygen Vacancy Memristor
Zou, L., Peng, Z., Sun, H., Yi, Y., Zhu, C., Xu, J., Zhang, J., Hu, X., Dang, Y., Ye, L., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416325 · Cited by 18
Decoding halide perovskites for neuromorphic and memristive devices
Das, D., Assi, D. S., Kazim, S., Roy, V. A. L., & Ahmad, S. (2025) · Materials Horizons · doi:10.1039/d5mh00534e · Cited by 18
Dynamics of synchronous encoding in vanadium dioxide (VO2) memristive neurons
Zhu, Z., Dong, J., Yu, Y., Chen, Q., & Ma, J. (2025) · Science China Technological Sciences · doi:10.1007/s11431-024-2981-2 · Cited by 18
Wlaźlak, E., Przyczyna, D., Gutierrez, R., Cuniberti, G., & Szacilowski, K. T. (2020) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ab7e11 · Cited by 19
“A more advanced approach (second generation neural networks) is based on the same geometry (connectivity) as the perceptron, but (i) it may contain more layers and (ii) it uses as an activation a logistic function (10) instead of a threshold function: ( ) 1 1 s f s e = + (10)”
BackgroundCharacteristic investigation of highly oriented Hf0.5Zr0.5O2 thin-film resistive memory devices
Yan, X., Xiao, Z., & Lü, C. (2020) · Applied Physics Letters · doi:10.1063/1.5141132 · Cited by 18
TiO2 Nanowire Array Memristive Devices Emulating Functionalities of Biological Synapses
Ebenhoch, C., & Schmidt‐Mende, L. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202000950 · Cited by 18
Flexible neuromorphic electronics based on low-dimensional materials
Jin, T., Gao, J. L., Wang, Y., & Chen, W. (2022) · Science China Materials · doi:10.1007/s40843-021-1979-3 · Cited by 18
Brain-like synaptic memristor based on lithium-doped silicate for neuromorphic computing
Ke, S., Jiang, L., Zhao, Y., Xiao, Y., Jiang, B., Cheng, G., Wu, F., Cao, G., Peng, Z., Zhu, M., et al. (2022) · Frontiers of Physics · doi:10.1007/s11467-022-1173-2 · Cited by 18
Das, S., Chen, A., & Marinella, M. J. (2021) · doi:10.1109/irds54852.2021.00011 · Cited by 24
An Oxide Based Spiking Thermoreceptor for Low-Power Thermography Edge Detection
Shi, K., Heng, S., Wang, X., Liu, S., Cui, H., Chen, C., Zhu, Y., Xu, W., Wan, C., & Wan, Q. (2022) · IEEE Electron Device Letters · doi:10.1109/led.2022.3215693 · Cited by 18
“The switching mechanism of the Ag-based TSM can be explained by the formation of the Ag filament between the top and bottom electrodes driven by the electric field and the rupture when self-diffuses to the minimum dielectric interface energy [19], [22], [23], [24].”
BackgroundMethodNeotype kuramite optoelectronic memristor for bio-synaptic plasticity simulations
Dong, X., Wei, W., Sun, H., Li, S., Chen, J., Chen, J., Zhang, X., Zhao, Y., & Li, Y. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0151205 · Cited by 20
Carrier Type Exchange with the Sweep Direction in a WORM Memory Device
Bhattacharjee, D., Hussain, S. A., Paul, P. K., et al. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00617 · Cited by 18
Self‐repairable, high‐uniform conductive‐bridge random access memory based on amorphous NbSe2
Lu, B., Hu, D., Yang, R., Du, J., Hu, L., Li, S., Wang, F., Huang, J., Liu, P., Zhuge, F., et al. (2023) · SmartMat · doi:10.1002/smm2.1240 · Cited by 19
Application of artificial synapse based on Al-doped SrTiO3 thin film in neuromorphic computing
Shen, Z., Li, W., Tang, X.-G., Chen, H., Hu, J. S., Wang, K.-Y., Meng, K., Jiang, Y., & Guo, X. (2023) · Vacuum · doi:10.1016/j.vacuum.2023.112568 · Cited by 19
Wei, Q., Gao, B., Tang, J., Qian, H., & Wu, H. (2023) · IEEE Electron Devices Magazine · doi:10.1109/med.2023.3296084 · Cited by 20
“The former comprise ions of active metals like Ag and Cu [14], [15], while in the latter, oxygen vacancy is involved [16].”
BackgroundMethodA Diffusive Artificial Synapse Based on Charged Metal Nanoparticles
Guo, J., Liu, L., Wang, J., Zhao, X., Zhang, Y., & Yan, Y. L. (2024) · Nano Letters · doi:10.1021/acs.nanolett.3c04224 · Cited by 19
Electrochemical anodic oxidation assisted fabrication of memristors
Hua, S., Tian, J., & Guo, X. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad2c61 · Cited by 18
“Apart from NVMs, memristors are also applied to logic operations [37], bio-inspired neuromorphic computations [38– 42], and data encryption [43, 44].”
MethodInfluence of Compact Memristors’ Stability on Machine Learning
Zhu, R., Ye, S., Tang, Z., Lin, P., Huang, Q., Wang, H., He, J. L., & Chang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2909295 · Cited by 17
“In recent years, different materials and structures are used to implement kinds of memristors, such as planar Au/SiOxNy:Ag/Au-based diffusive memristor [1], silicon nitride memristor [2], multilayer hexagonal boron nitride (h-BN) memristor [3], and polycrystalline monolayer molybdenum disulfide memristor [4].”
BackgroundSelf-citationReal-Time Visualization of Solid-Phase Ion Migration Kinetics on Nanowire Monolayer
He, Z., Chang, L., Lin, Y., et al. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c02137 · Cited by 18
Self-citationNeuromorphic computing: From devices to integrated circuits
Saxena, V. (2020) · Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · doi:10.1116/6.0000591 · Cited by 20
Artificial Intelligence Accelerators Based on Graphene Optoelectronic Devices
Gao, W., Yu, C., & Chen, R. (2021) · Advanced Photonics Research · doi:10.1002/adpr.202100048 · Cited by 20
Hassanzadeh, P. (2021) · Life Sciences · doi:10.1016/j.lfs.2021.119272 · Cited by 18
Role of noise in spiking dynamics of diffusive memristor driven by heating-cooling cycles
Ushakov, Y., Balanov, A. G., & Savel'ev, S. E. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110803 · Cited by 18
Self-citationFlexible Threshold Switching Selectors with Ultrahigh Endurance Based on Halide Perovskites
Tang, L.-Z., Huang, Y., Wang, C., Zhao, Z., Yang, Y., Bian, J., Wu, H., Zhang, Z., & Zhang, D. W. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100771 · Cited by 18
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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