The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index; 7 of the 35 sources its claims cite were checked for support.
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Open accessAbstract
The accumulation and extrusion of Ca 2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca 2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.
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Claims the paper makes
53 Own result · 32 Own interpretation · 18 No basis given · 10 Sources not found · 8 No full text of the sources · 3 Partly supported · 2 Couldn’t confirm · 2 Own method · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In chemical synapses, the dynamical balance of the Ca 2+ concentration is shaped by both influx via voltage-sensitive calcium channels (VSCC) and N-methyl-D-aspartate receptors (NMDAR) 42,43 , and extrusion via the plasma membrane Ca 2+ -ATPase (PMCA) and Na + /Ca 2+ exchanger (NCE) 43,44 that restores [Ca 2+ ]i to the basal concentration.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [42] Burgoyne, R. D. Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat. Rev. Neurosci. 8, 182–193 (2007). sourceExperimental — accuracy not yet measured
The “Changes in Ca2+ concentration” section says neuronal calcium can enter through voltage-gated calcium channels or NMDA receptors and identifies a resting intracellular concentration. It does not identify PMCA or the Na+/Ca2+ exchanger as the means of extrusion or say that they restore calcium to baseline. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [43] Clapham, D. E. Calcium signaling. Cell 131, 1047–1058 (2007). source
- Partly supportedcites [44] Catterall, W. A. & Few, A. P. Calcium channel regulation and presynaptic plasticity. Neuron 59, 882–901 (2008). sourceExperimental — accuracy not yet measured
The Introduction states that presynaptic voltage-gated calcium channels admit Ca2+ and that residual Ca2+ is cleared by a Ca2+-ATPase and a Na+/Ca2+ exchanger. It does not identify NMDARs as an influx route in this account or explicitly say these mechanisms restore intracellular calcium to its basal concentration. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
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In the paper
“The micro-devices represented by Fig. 1a have an area of 10µm×10µm and nano-devices with an area of 100nm×100nm exhibited similar switching behaviours (Supplementary Fig. S4).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“their operating characteristics were experimentally verified by demonstrating both short-and long-term plasticity, including mechanisms that have not been unambiguously demonstrated previously.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Here we report such an emulator, which is a memristor based on metal atom diffusion and spontaneous nanoparticle formation, as determined by in situ high-resolution transmission electron microscopy (HRTEM) and nanoparticle dynamics simulations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An applied voltage above an apparent threshold abruptly switched the device to a conductance state limited by an external compliance current (Fig. 1b).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Repeatable and symmetric switching was demonstrated using wider bipolar voltage pulses with over a million switching cycles in Fig. 1d.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“To demonstrate that the device spontaneously relaxed back to an insulating configuration upon removing the bias (without applying an opposite polarity voltage)”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The simulations for Ag in dielectric agreed well with the experimental HRTEM observations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under an applied pulse, the device exhibited threshold switching to a low resistance state after an incubation period τd, as shown in Fig. 3a.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 59
- Probably not a substantial claim. 9
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The calcium inside the postsynaptic membrane also plays important regulating roles in long-term potentiation and depression where Ca 2+ accumulation is necessary to regulate enzymes, i.e.Ca 2+ /calmodulindependent protein kinase II (CaMKII), calcineurin, and protein phosphatase 1 42,43,46,47 , which in turn triggers rapid and persistent modification of synaptic strengths by changing the number and”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [42] Burgoyne, R. D. Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat. Rev. Neurosci. 8, 182–193 (2007). sourceExperimental — accuracy not yet measured
The 'Different signals, diverse outcomes' section says postsynaptic Ca2+ signals can trigger LTP or LTD, and 'Ca2+ sensor proteins: calmodulin' links Ca2+/calmodulin to CaMKII and identifies calcineurin as a calmodulin target (page numbers unavailable). The paper does not establish the claimed pathway through protein phosphatase 1 or say that these enzymes persistently change synaptic strength by altering AMPAR number or conductance. Its References discuss possible AMPA-receptor endocytosis in LTD through hippocalcin, a different mechanism. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [43] Clapham, D. E. Calcium signaling. Cell 131, 1047–1058 (2007). source
- Partly supportedcites [46] Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004). sourceExperimental — accuracy not yet measured
In the passages headed “LTD Expression and Maintenance” (page unavailable), the review links LTP and LTD to AMPAR phosphorylation and identifies CaMKII as acting on an AMPAR phosphorylation site. Other passages describe postsynaptic Ca²⁺ as a trigger for LTD and report roles for calcineurin and protein phosphatase 1. The retrieved passages support changes in AMPAR surface expression, but do not establish the claim that these enzymes persistently change synaptic strength through altered AMPAR conductance. (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [47] Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Pinning can occur through interactions of the nanoparticles with impurities, the substrate and/or the ionic lattice 41 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Could not verifycites [41] Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016). sourceExperimental — accuracy not yet measured
The Noise spectra section discusses fluctuating atomic-scale contacts in memristors, while Methods mentions a substrate only as part of the device construction. Searches for nanoparticle pinning and interactions with impurities, substrates, or ionic lattices found no passage addressing the claim; our copy therefore does not verify it. The retrieved passages have no page numbers. (LLM, conf=0.76)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Moreover, a qualitatively similar result (although requiring considerably more computational resources) has been obtained when we modelled the interfacial energy as an interaction between particles 41 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Could not verifycites [41] Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) describes a Molecular Dynamics–Langevin simulation that reproduces a crossover in memristor noise spectra. Searches for interfacial energy, particle–particle interactions, and the claimed modelling comparison found no passage in our copy addressing them. (LLM, conf=0.75)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The material systems exhibiting a substantial relaxation were those with large wetting contact angles 32 , such as MgOx:Ag, SiOxNy:Ag, and HfOx:Ag in Fig. 1b, consistent with the reported psudoelasticity of silver nanoparticles 33 and the hypothesis that interfacial energy facilitates filament rupture in volatile switching [34][35][36] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [32] Stoneham, A. M. Systematics of metal–insulator interfacial energies: a new rule for wetting and strong catalyst-support interactions. Appl. Surf. Sci. 14, 249–259 (1983). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked — no full text of the sourcecites [33] Sun, J. et al. Liquid-like pseudoelasticity of sub-10-nm crystalline silver particles. Nat. Mater. 13, 1007–1012 (2014). source
- Not matchedcites [34][35][36]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Symmetric hysteresis loops were observed with the opposite polarity bias (Supplementary Fig. S3), showing that the threshold switching is unipolar in nature and significantly different from non-volatile drift-type memristors, especially in the OFF-switching process.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“These devices are similar to electrochemical metallization memory (ECM) [21][22][23][24][25][26] cells in terms of utilizing mobile species of noble metals, but they differ substantially in terms of the structural symmetry and operating voltage polarities, metal concentration and profile, and transient switching behaviour.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [21][22][23][24][25][26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“X-ray photoelectron spectroscopy (XPS) revealed that the Ag was metallic, which was further confirmed by HRTEM micrographs showing Ag nanocrystals in SiOxNy:Ag”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization,”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The resistance ratio between the conducting and insulating states was 5-orders of magnitude in SiOxNy:Ag and over 10-orders in HfOx:Ag devices, the highest reported in threshold switching devices so far [27][28][29][30] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [27][28][29][30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Not matched
In the paper
“The volatile switching had sharp turn-on slopes of ~10mV/decade in MgOx:Ag and SiOxNy:Ag, and an extraordinary ~1mV/decade in HfOx:Ag, the sharpest demonstrated to date [27][28][29][30] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [27][28][29][30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reliable threshold switching was observed under a 100nA compliance in ambient conditions with a typical relaxation time constant of ~11ms (Supplementary Fig. S5b-c).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“If a subsequent pulse arrives before particles are re-absorbed, i.e., if the time between pulses is shorter than the diffusion relaxation time, more particles are pushed into the gap between terminals resulting in a gradual increase in device conductance, similar to the paired-pulse facilitation (PPF) phenomenon in bio-synapses.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in an inflection of the device conductance due to excessive stimulation, capturing another synaptic behaviour, i.e., PPF followed by PPD (paired pulse depression).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Eventually, most of the nanoparticles have merged into larger clusters to minimize interfacial energy, and the high resistance state is re-established along with the original particle distribution almost restored (compare Fig. 4a1 and Fig. 4a4), leading to the observed volatility in Fig. 2.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
In the paper
“The above PPF and PPD demonstrations were realized with diffusive memristors only, resembling short-term plasticity 45 in synapses because any conductance change from the OFF state of diffusive memristors will vanish over time.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [45] Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Third, sequential high voltage pulses with a long enough interval (low frequency) may form a conducting bridge first, but before the next pulse arrives the bridge breaks and the particles are re-absorbed at the terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 3b, τr decreased as the ambient temperature increased, consistent with a diffusion activation energy of 0.27eV (inset of Fig. 3b)”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As soon as the power is turned off, the temperature drops, and the nanoparticles start to coalesce (Fig. 4a3), i.e. particles slowly diffuse to their minimum energy positions near the device terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When a voltage pulse is applied (Fig. 4a), the local temperature increases due to Joule heating and the potential is tilted by electric forces acting on particles with induced charge, both of which cause larger clusters to break up.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the nanoparticles become more uniformly distributed in the gap, the resistance drops, the current and temperature increase, and a positive feedback results in the formation of a conductive channel (Fig. 4a2).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A smaller ∆t corresponds to a smaller diffusive memristor resistance and results in a greater resistance change in the drift memristor and vice versa (Fig. 6d).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“This important feature of bio-synapses, which has not been clearly demonstrated previously on two terminal devices, was predicted in Fig. 4b and observed experimentally in Fig. 5c.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“The elongated cluster of nanoparticles that had likely formed the conductive bridge rapidly contracted from a length of 14.8nm to a circular profile with a diameter of 7.6nm by 5.7s, indicating Ostwald ripening 31 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [31] Voorhees, P. W. The theory of Ostwald ripening. J. Stat. Phys. 38, 231–252 (1985). source
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In the paper
“The device in its steady state shows PPD upon low frequency (196Hz) stimulation and experiences an increase in current (facilitation) once the stimulation frequency is raised (5000Hz).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“Similar to Fig. 5b, a shorter tzero resulted in a greater increase in the conductance of the diffusive memristor and thus a larger voltage drop across the drift memristor, which thereby switched due to the voltage divider effect.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“At 4.6s, the cluster reached a diameter of ~4.2nm.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A longer tzero resulted in a smaller increase in the diffusive memristor conductance and thus a smaller voltage drop across the drift memristor, leading to a smaller or non-detectable resistance change in the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“Upon channel formation, the current jumped abruptly by several orders of magnitude, and then slowly increased further under bias as the channel thickened.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“The drift memristor is not switched by the first spike, because the majority of the voltage drops across the diffusive memristor and turns it ON first.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“First, when the initial voltage pulse is applied, electric field-assisted diffusion pumps some of the Ag-particles out of the 'left' cluster and they start to bridge terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the pre-spike appears before the postspike, the drift memristor conductance increases (potentiation).If the pre-spike follows the post-spike, depression occurs.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“In the combined element, the resistance of the diffusive memristor in its OFF state is much larger than that of the drift memristor, while the resistance of its ON state is much smaller than that of the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“We observed a delay time of ~2s during which the measured current was <5nA and Ag nanoparticles formed in the gap region”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The microscopic nature of both the threshold switching and relaxation of the diffusive memristor is revealed for the first time by in situ HRTEM and explained by nanoparticle dynamics simulation.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“curve) induced by a voltage pulse (red curve) shows a delay in the response and a gradual relaxation towards the low conducting state, consistent with the experimental threshold switching property in Fig. 3a.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When the post-spike precedes the pre-spike, the device is reset (depressed), and when the prespike precedes the post-spike, the device is set (potentiated).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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In the paper
“As one can see from Fig. 4, the relaxation occurs on time scales of the order 10-20 1/κ, thus, the simulated system is clearly governed by diffusion rather than the temperature relaxation 19 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
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In the paper
“from Fig 4b we conclude that the conductance reached 25% of the maximum after 4 pulses and about 75% after 7 pulses.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Device current response (blue) to multiple dependent plasticity (SRDP and true STDP) behaviour of a combined device consisting of a diffusive and a drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The long low voltage pulse in each spike turns the diffusive memristor ON, and the short high voltage pulse switches the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The device did not fail during the measurement, demonstrating high robustness.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Highly non-linear, fast, and repeatable threshold switching behaviours of diffusive memristors”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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In the paper
“The device requires a finite delay time to turn ON and has a finite relaxation time before it goes to the high resistance state after the switching pulse is removed.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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In the paper
“For long tzero, the change in the conductance of the diffusive memristor is lower (see Fig. 5b), resulting in a lower weight change of the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the external biasing is removed at 5.0s, the filament starts to deform, shrinking to a round spherical nanocluster implying an interfacial energy driven diffusion mechanism.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this range the characteristic single hopping time scale 𝑒𝑒 𝑤𝑤 𝑝𝑝 /𝑘𝑘 𝐵𝐵 𝑇𝑇 changed from about 2/κ to 6/κ and the diffusion relaxation time (estimated as a characteristic jump time multiplied by the number of jumps to reach the interfacial minimum near the device terminal) was about 20/κ to 60/κ.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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In the paper
“CMOS circuits have been employed to mimic synaptic Ca 2+ dynamics, but three-terminal devices bear limited resemblance to bio-counterparts at the mechanism level and require significant numbers and complex circuits to simulate synaptic behavior [1][2][3] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [1][2][3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Various types of memristors based on ionic drift (drift-type memristor) [4][5][6][7][8] have recently been utilized for this purpose in neuromorphic architectures [9][10][11][12][13][14][15] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [4][5][6][7][8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [9][10][11][12][13][14][15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Recently, Lu's group adopted second-order drift memristors to approximate the Ca 2+ dynamics of chemical synapses by utilizing thermal dissipation 19 or mobility decay 20 , which successfully demonstrated STDP with non-overlapping spikes and other synaptic functions”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This approach features repeatability and simplicity, but the significant differences of the dynamical response from actual synapses limit the fidelity and variety of desired synaptic functions.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
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In the paper
“Similar issues also exist in MOS-based memristor emulators [16][17][18] , although they are capable of simulating a variety of synaptic functions including spike-timing-dependent plasticity (STDP).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [16][17][18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although qualitative synaptic functionality has been demonstrated, the fast switching and non-volatility of drift memristors optimized for memory applications do not faithfully replicate the nature of plasticity.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“Ca 2+ dynamics is responsible for initiating both short-and long-term plasticity of synapses, forming the basis of memory and learning [42][43][44] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [42][43][44]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The particular shape of neither the interfacial potential nor the pinning energy profile are essential; the only property that matters is that the potential 𝑈𝑈 has two different energy scales and the weaker pinning potential has many wells (for example it can be random with fluctuating 𝑤𝑤 𝑝𝑝 and 𝑖𝑖 𝑝𝑝 ).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- The authors state this as an assumption: The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the dynamics of the diffusive memristor provides an intrinsic timing mechanism for the combined element, the spike-ratedependent plasticity and STDP do not require complex pulse engineering or spike overlapping.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Ag dynamics of the diffusive memristors functionally resemble the synaptic Ca 2+ behavior in chemical synapses and lead to a direct and natural emulation of multiple synaptic functions for both short-term and long-term plasticity, such as PPF, PPD, PPD following PPF, SRDP and STDP.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to providing a synapse emulator, the diffusive memristor can also serve as a selector with a large transient nonlinearity that is critical for the operation of a large crossbar array as a neural network.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The electrical bias together with diffusion is responsible for switching to a high conducting state and depletion of nanoparticles at one of the device terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- The authors state this as an assumption: The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This substantially reduces the complexity of both circuit and algorithm design and enables lowenergy operations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The stronger fluctuations support larger Joule heating and vice versa, breaking up the large Ag clusters to form a string of smaller nanoparticles and thus a conductive path between the terminals, as observed experimentally in the HRTEM studies.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“diffusion into the gap region between Ag nanoclusters with field assistance and clearance of Ag filament from the gap region by the interfacial energy or mechanical stress once the voltage signal is removed, which is a close emulation of Ca 2+ dynamics.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Equations ( 4) and ( 5) are sufficient to describe the memristor dynamics subject to the current through the device, which is controlled by the electric circuit in which it is embedded.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“programming is at the instrument noise level (<100nA) for the current measurement range used.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These quantities can be easily obtained from both simulations (averaged over 30 realizations) and experimental I-V hysteresis loops, thus directly linking the units in the simulations and the experiments.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
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In the paper
“The first one depends on temperature, potential profile, and electric drift, thus, this time scale changes depending on voltage pulse intensity and temperature.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
“For the state-dependent resistance we assume sequential electron tunnelling: first from the input terminal to the nearest metallic nanoparticle, and then from this nanoparticle to the next one and so on with the last tunnelling event to the output terminal.”
The authors state this as an assumption.
The tunnelling resistance amplitude is taken to be identical for every island.
“R 𝑡𝑡 the tunneling resistance amplitude (assumed the same for all islands)”
The authors state this as an assumption.
Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
“we employed an overdamped Langevin equation for each mobile metallic nanoparticle trapped by a potential 𝑈𝑈 and subject to a random force 𝜉𝜉 𝑖𝑖 , the magnitude of which is determined by the device local temperature 𝑇𝑇,”
The authors state this as an assumption.
The simulation model omits redox reactions.
“The model results here did not include redox reactions, although they can be added in order to more closely resemble the electrochemical models proposed previously [21][22][23][24][25][26] .”
The authors state this as an assumption.
The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
“The potential 𝑈𝑈 = 𝑈𝑈 𝐼𝐼 + 𝑈𝑈 𝑝𝑝 has two energy scales -the interfacial energy 𝑈𝑈 𝐼𝐼 responsible for formation of large metallic clusters near the device terminals and a weaker nanoparticlepinning energy 𝑈𝑈 𝑝𝑝 with many smaller wells between the electrodes.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The decay of the conductance is considered to arise from the surface diffusion of Ag caused by the Gibbs -Thomson effect 41 -45 .”
From Enhanced analog synaptic behavior of SiNx/a-Si bilayer memristors through Ge implantation (2020) · cites this paper as [42]
Experimental — accuracy not yet measuredIn the citing paper
“In contrast to ref. 34 we have also taken into account the memristor self-capacitance, which is critically important to describe the delay time distributions.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [34]
Citations
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Cited by
Sorted by citations
Items 1,201–1,300 of 2,351
Abbas, H., Li, J., Ali, A., Hussain, S., Jung, J., & Ang, D. S. (2024) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2024.07.033 · Cited by 17
Reconfigurable Al2O3-Based Memristor for All-in-One Artificial Synapse and Nociceptor Neurons
Du, H., Wang, F., Li, Z., Song, L., Luo, Y., Chen, X., Zheng, L., Han, Y., Cheng, Y., Luo, Q., et al. (2025) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5c00184 · Cited by 16
Self-citationCurrent opinions on memristor-accelerated machine learning hardware
Jiang, M., Xu, Y., Li, Z., & Li, C. (2025) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2025.101226 · Cited by 20
Self-citationGhazanfar, H., Rabeel, M., Nisar, S., Ahmad, M., Zulfiqar, M. W., Sulaman, M., Kim, H., Dastgeer, G., & Kim, D. (2026) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2026.139943 · Cited by 21
Memristive learning cellular automata for edge detection
Karamani, R.-E., Fyrigos, I.-A., Τσάκαλος, Κ.-Α., Ntinas, V. G., Tsompanas, M., & Sirakoulis, G. C. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110700 · Cited by 17
An SBT-memristor-based crossbar memory circuit*
Guo, M. L., Liu, R.-Y., Dou, M.-L., & Dou, G. (2021) · Chinese Physics B · doi:10.1088/1674-1056/abd7dc · Cited by 17
Shu, H., Long, H., Sun, H., Li, B., Zhang, H., & Wang, X. (2022) · ACS Omega · doi:10.1021/acsomega.1c06864 · Cited by 18
Adaptive SRM neuron based on NbO memristive device for neuromorphic computing
Huang, J., Wang, T., Huang, H.-M., & Guo, X. (2022) · Chip · doi:10.1016/j.chip.2022.100015 · Cited by 17
A Leaky Integrate-and-Fire Neuron Based on Hexagonal Boron Nitride (h-BN) Monocrystalline Memristor
Qian, F., Chen, R., Wang, R., Wang, J., Xie, P., Mao, J., Lv, Z., Ye, S., Yang, J., Wang, Z., et al. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3206170 · Cited by 18
“The experimental investigations are further regarded as the basis for understanding and modeling the internal Ag ions migration dynamics by the Monte Carlo simulation [16], [17], [18].”
BackgroundMethodSelf-citationXi, F., Grenmyr, A., Zhang, J., Han, Y., Bae, J. H., Grützmacher, D., & Zhao, Q. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202201155 · Cited by 18
Stochastic current response in diffusive memristor for security applications
Xing, X., Huang, S., Gong, Y., Wang, J., Lv, Z., Zhou, Y., Zhao, X., Hao, J., & Han, S. (2023) · Materials Today Nano · doi:10.1016/j.mtnano.2023.100315 · Cited by 18
Recent progress of layered memristors based on two-dimensional MoS2
Tong, W., & Liu, Y. (2023) · Science China Information Sciences · doi:10.1007/s11432-023-3751-y · Cited by 17
Memristors based on NdNiO 3 nanocrystals film as sensory neurons for neuromorphic computing
Zhao, J., Ran, Y., Pei, Y., Wei, Y., Sun, J., Zhang, Z., Wang, J., Zhou, Z., Wang, Z., Sun, Y., et al. (2023) · Materials Horizons · doi:10.1039/d3mh00835e · Cited by 17
Self-citationHuang, C., Wu, C., Lin, Y., Chou, Y., & Lee, K. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202306030 · Cited by 17
Emerging Opportunities for 2D Materials in Neuromorphic Computing
Feng, C., Wu, W., Liu, H., Wang, J., Wan, H., Ma, G., & Wang, H. (2023) · Nanomaterials · doi:10.3390/nano13192720 · Cited by 22
Adjustable short-term memory of SiO x :Ag-based memristor for reservoir computing
Li, R., Yang, H., Zhang, Y., Tang, N., Chen, R., Zhou, Z., Liu, L., Kang, J., & Huang, P. C. (2023) · Nanotechnology · doi:10.1088/1361-6528/acfb0a · Cited by 17
“When the bias is removed, the device relaxes back to the original state due to spontaneous ion diffusion induced by the interfacial energy minimization [ 42 – 48 ] .”
Self-Assembled Au Nanoelectrodes: Enabling Low-Threshold-Voltage HfO2-Based Artificial Neurons
Dou, H., Lin, Z., Hu, Z., Tsai, B. K., Zheng, D., Song, J., Lu, J., Zhang, X., Jia, Q. X., MacManus‐Driscoll, J. L., et al. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c02217 · Cited by 19
Chen, X. L., Huang, X., Li, Y., Wang, W. S., Huang, Y. J., Xiao, H., Wan, Q. C., & Zhu, L. Q. (2023) · Applied Materials Today · doi:10.1016/j.apmt.2023.102034 · Cited by 18
Locality of interactions for planar memristive circuits
Caravelli, F. (2017) · Physical review. E · doi:10.1103/physreve.96.052206 · Cited by 17
Tappertzhofen, S., & Hofmann, S. (2017) · Nanoscale · doi:10.1039/c7nr05847k · Cited by 17
Electrochemical metallization cell with solid phase tunable Ge2Sb2Te5 electrolyte
Zhang, Z., Wang, Y., Wang, G., Mu, J., Ma, M., He, Y., Yang, R., & Li, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-29778-9 · Cited by 17
A Demonstration of Implication Logic Based on Volatile (Diffusive) Memristors
Pershin, Y. V. (2018) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2018.2873635 · Cited by 17
“memristors Ag atoms spread under electrical bias and regroup spontaneously under zero/small bias because of interfacial energy minimization [10], [11].”
BackgroundForming-free artificial synapses with Ag point contacts at interface
Jiang, L., Lv, F.-C., Yang, R., Hu, D.-C., & Guo, X. (2018) · Journal of Materiomics · doi:10.1016/j.jmat.2018.11.001 · Cited by 17
Przyczyna, D., Lis, M., Pilarczyk, K., & Szacilowski, K. T. (2019) · Molecules · doi:10.3390/molecules24152738 · Cited by 17
Huang, L., Yao, W., Xiang, J., et al. (2020) · Complexity · doi:10.1155/2020/2408460 · Cited by 17
“Due to the nonlinearity of memristor, it has been applied inmany fields, such as flashmemory [2, 3], neuromorphic computing [4, 5], neural network [6, 7], and chaotic system [8–11] based on chaos synchronization for encryption algorithms [12, 13] and secure communication [14, 15].”
MethodIntermittent and metastable chaos in a memristive artificial neuron with inertia
Wojtusiak, A., Balanov, A. G., & Savel'ev, S. E. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110383 · Cited by 17
Self-citationSun, Y., Ding, Y., Xie, D., et al. (2021) · Optics Letters · doi:10.1364/ol.414820 · Cited by 17
Improved artificial synapse performance of Pt/HfO2/BiFeO3/HfO2/TiN memristor through N2 annealing
Xiao, Y., Wu, X., Jin, Y., et al. (2022) · Ceramics International · doi:10.1016/j.ceramint.2022.08.045 · Cited by 17
Zhang, T., Yin, M., Lu, X., Cai, Y., Yang, Y., & Huang, R. (2017) · Nano Futures · doi:10.1088/2399-1984/aa678b · Cited by 16
Biomedical applications of sensing devices with memristive behaviors
Yang, Y., Sun, B. T., Mao, S., Qin, J., Yang, Y., Liu, M., Rao, Z., Lin, W., & Zhao, Y. X. (2024) · Journal of Materials Chemistry C · doi:10.1039/d4tc02289k · Cited by 17
Towards scalable memristive hardware for spiking neural networks
Chen, P., Zhang, B., He, E., Xiao, Y., Liu, F., Lin, P., Wang, Z., & Pan, G. (2025) · Materials Horizons · doi:10.1039/d4mh01676a · Cited by 19
Self-citationInkjet‐printed reconfigurable and recyclable memristors on paper
Chen, J., Xiao, M., Chen, Z., Khan, S., Ghosh, S., Macadam, N., Chen, Z., Zhou, B., Yun, G., Wilk, K., et al. (2025) · InfoMat · doi:10.1002/inf2.70000 · Cited by 21
A heterointerface effect of Mo1-xWxS2-based artificial synapse for neuromorphic computing
Hwang, J., Sung, J., Lee, E., & Choi, W. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.161622 · Cited by 18
An Artificial Nerve with Complementary Memristor for Neuromorphic Self‐Protection
Tian, Q., Zhao, X., Tang, X., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202507503 · Cited by 16
Self-citationLow-power optoelectronic synaptic devices with ZnMgO in deep-ultraviolet encryption computation
Li, J., Chen, K., Wu, C., Wu, F., Li, S., Wu, Z., Tang, W., Fang, Z., & Guo, D. (2025) · Applied Physics Letters · doi:10.1063/5.0268617 · Cited by 18
Solid state ionics for the development of artificial intelligence components
Terabe, K., Tsuchiya, T., & Tsuruoka, T. (2022) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ac64e5 · Cited by 16
Hah, J., West, M. P., Athena, F. F., Hanus, R. C., Vogel, E. M., & Graham, S. L. (2022) · Journal of Materials Science · doi:10.1007/s10853-022-07067-1 · Cited by 16
“[6] Wang Z, Joshi S, Savelev SE, Jiang H, Midya R, Lin P, Hu”
BackgroundGuo, J., Liu, L., Bian, B., Wang, J., Zhao, X., Zhang, Y., & Yan, Y. L. (2022) · Nano Letters · doi:10.1021/acs.nanolett.2c02675 · Cited by 17
Ferroelectric Devices for Content-Addressable Memory
Tarkov, M. S., Tikhonenko, F. V., Popov, V. P., Antonov, V. A., Miakonkikh, A. V., & Rudenko, K. V. (2022) · Nanomaterials · doi:10.3390/nano12244488 · Cited by 16
Song, Y. G., Kim, J. E., Kwon, J. U., Chun, S. Y., Soh, K., Nahm, S., Kang, C., & Yoon, J. H. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c21019 · Cited by 16
Electrolyte Gated Synaptic Transistor based on an Ultra‐Thin Film of La 0.7 Sr 0.3 MnO 3
López, A., Tornos, J., Peralta, A., Barbero, I. E., Fernandez‐Canizares, F., Sánchez‐Santolino, G., Varela, M., Rivera, A., Camarero, J., Leon, C., et al. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202300007 · Cited by 16
Liu, Y., Cai, D., Zhao, T., Shen, M., Zhou, X., Zhang, Z. H., Meng, X., & Gu, D. (2023) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2023.04.025 · Cited by 16
Criticality and Neuromorphic Sensing in a Single Memristor
Ma, Z., Chen, W., Cao, X., et al. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c00389 · Cited by 16
An overview memristor based hardware accelerators for deep neural network
Gökgöz, B., Gül, F., & Aydın, T. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7997 · Cited by 19
“156 Creating a device that replicates the physical characteristics of biological Ca + 2 dynamics will enhance our ability to mimic synaptic function more effectively and expand the potential applications of neuromorphic computing.”
BackgroundMethodNonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing
Go, S., Lim, K.-G., Lee, T., & Loke, D. K. (2024) · Small Science · doi:10.1002/smsc.202300139 · Cited by 15
Self-sensitizable neuromorphic device based on adaptive hydrogen gradient
Zhang, T., Hu, M., Mia, M. Z. A., Zhang, H., Mao, W., Fukutani, K., Matsuzaki, H., Wen, L., Wang, C., Zhao, H., et al. (2024) · Matter · doi:10.1016/j.matt.2024.03.002 · Cited by 16
Self-citationA Fully Printed ZnO Memristor Synaptic Array for Neuromorphic Computing Application
Chen, J.-W., Xu, Q., Li, Y., Cao, J., Liu, X., Qiu, J., Chen, Y., Liu, M.-Y., Yu, J., Zhang, X.-M., et al. (2024) · IEEE Electron Device Letters · doi:10.1109/led.2024.3387455 · Cited by 10
“This phenomenon might result from that the accumulated growth of filaments under high-frequency stimulation is difficult to maintain, and the rupture of filaments becomes dominant when being switched to low-frequency stimulation [22].”
BackgroundNanoscale potential fluctuations in nonstoichiometrics tantalum oxide
Gritsenko, V. A., Володин, В. А., Perevalov, T. V., et al. (2018) · Nanotechnology · doi:10.1088/1361-6528/aad430 · Cited by 16
Kim, Y., Bae, C., Jung, H. S., & Shin, H. (2019) · APL Materials · doi:10.1063/1.5109525 · Cited by 16
Hadiyawarman, Eguchi, M., & Tanaka, H. (2019) · Japanese Journal of Applied Physics · doi:10.7567/1347-4065/ab5c77 · Cited by 16
Wu, M., Shang, S., Wei, Q., Liu, C., Li, A., Gao, X., Wang, S., Yin, J., Xia, Y., & Liu, Z. G. (2020) · Advanced Electronic Materials · doi:10.1002/aelm.201901255 · Cited by 16
Ioannou, P. S., Kyriakides, E., Schneegans, O., & Giapintzakis, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-65237-0 · Cited by 16
High Robustness Memristor Neural State Machines
Tian, L., Wang, Y., Shi, L., & Zhao, R. J. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00700 · Cited by 16
Banerjee, W., Karpov, I. V., Agrawal, A., et al. (2020) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm13553.2020.9371960 · Cited by 16
A simple time-delay memristor and its application in 2D HR neuron model
Li, R., & Ding, R. (2021) · International Journal of Modern Physics B · doi:10.1142/s0217979221501666 · Cited by 16
Meng, Y., Yip, S., Wang, W., Liu, C., & Ho, J. C. Y. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202100072 · Cited by 16
An Effective Sneak‐Path Solution Based on a Transient‐Relaxation Device
Fu, T., Fu, S., Sun, L., Gao, H., & Yao, J. (2022) · Advanced Materials · doi:10.1002/adma.202207133 · Cited by 16
Volatile HRS asymmetry and subloops in resistive switching oxides
Torre, C. L., Kindsmüller, A., Wouters, D. J., Graves, C. E., Gibson, G. A., Strachan, J. P., Williams, R. S., Waser, R. M., & Menzel, S. (2017) · Nanoscale · doi:10.1039/c7nr04896c · Cited by 16
Self-citationKim, H. J., Kim, J., Park, T. G., Yoon, J. H., & Hwang, C. S. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100209 · Cited by 16
Thermodynamic ComputingPreprint
Conte, T. M., DeBenedictis, E. P., Ganesh, N., Hylton, T. L., Strachan, J. P., Williams, R. S., Alemi, A. A., Altenberg, L., Crooks, G. E., Crutchfield, J. P., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.01968 · Cited by 50
“2018) enabled by diffusive memristors (Wang et al. 2017), which function based on electrochemical potential and interfacial energy minimization.”
BackgroundSelf-citationFlexible neuromorphic transistors for neuromorphic computing and perception application
Ke, S., Zhu, Y., Fu, C., Mao, H., Shi, K., Qiao, L., & Wan, Q. C. (2024) · Moore and More · doi:10.1007/s44275-024-00009-w · Cited by 16
A review of rare earth materials for emerging memory devices for neuromorphic computing
Ghazanfar, H., Kim, H., Rabeel, M., Ahmad, M., Nisar, S., Zulfiqar, M. W., Rehman, A., Dastgeer, G., & Kim, D. (2025) · Materials Today Physics · doi:10.1016/j.mtphys.2025.101763 · Cited by 15
Memristors Based on 2D Materials: Bridging Device Theory and Potential Applications
Liu, X., Pan, Z., Xia, Z., Li, L., Deng, Q., Pan, Y., Li, J., & Huo, N. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202520816 · Cited by 19
Design and FPAA simulation of multi-scroll attractors in a memristor-based Hopfield neural network
Randrıanantenaına, J. L., Baran, A. Y., Korkmaz, N., & Kılıç, R. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117386 · Cited by 17
Luo, T., Yang, L., Zhang, H., Qu, C., Wang, X., Cui, Y., Wong, W., & Goh, R. S. M. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3120068 · Cited by 14
“While TrueNorth used conventional SRAM memory, energy-efficient emergent nonvolatile memory (NVM) technologies, such as resistive-RAM (RRAM) [18], can be adopted in the memory crossbar instead [19]–[21].”
MethodJhang, W.-C., & Hsu, C.-C. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3192797 · Cited by 15
“The migration of Ag cations and formation of Ag CBs in dielectric materials can be well explained by electrochemical and diffusive dynamics [34], [35].”
BackgroundEnergy and Space Efficient Parallel Adder Using Molecular Memristors
Yi, S., Rath, S. P., ., D., Venkatesan, T., Bhat, N., Goswami, S., Williams, R. S., & Goswami, S. (2022) · Advanced Materials · doi:10.1002/adma.202206128 · Cited by 15
Self-citationDevice‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning
Tang, Y., Zamani, P. T., Chen, R., Ma, J., Qi, M., Yu, C., & Gao, W. (2022) · Laser & Photonics Review · doi:10.1002/lpor.202200381 · Cited by 15
Polymer‐based neuromorphic devices: resistive switches and organic electrochemical transistors
Yamamoto, S. (2023) · Polymer International · doi:10.1002/pi.6520 · Cited by 15
A high linearity and energy-efficient artificial synaptic device based on scalable synthesized MoS 2
Zhao, Y., Jin, Y., Wang, X., Zhao, J., Wu, S., Li, M., Wang, J., Fan, S., & Li, Q. (2023) · Journal of Materials Chemistry C · doi:10.1039/d3tc00438d · Cited by 15
Oxide-based synaptic transistors gated by solid biopolymer electrolytes
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“memristors [5, 6], phase change memories [7, 8] and field-effect transistors [9–11].”
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In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper
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“The great scalability potential of SRMs provides a solid hardware foundation for ultra-high-preci-sion in-memory computing [99], neuromorphic computing [50], and hardware security [100].”
Self-citationTunable Negative Differential Resistance and Resistive Switching Properties of Amorphous WOₓ Devices
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Observing resistive switching behaviors in single Ta2O5 nanotube-based memristive devices
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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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