The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index; 7 of the 35 sources its claims cite were checked for support.
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Open accessAbstract
The accumulation and extrusion of Ca 2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca 2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.
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Claims the paper makes
7 of 35 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In chemical synapses, the dynamical balance of the Ca 2+ concentration is shaped by both influx via voltage-sensitive calcium channels (VSCC) and N-methyl-D-aspartate receptors (NMDAR) 42,43 , and extrusion via the plasma membrane Ca 2+ -ATPase (PMCA) and Na + /Ca 2+ exchanger (NCE) 43,44 that restores [Ca 2+ ]i to the basal concentration.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [42] Burgoyne, R. D. Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat. Rev. Neurosci. 8, 182–193 (2007). sourceExperimental — accuracy not yet measured
The “Changes in Ca2+ concentration” section says neuronal calcium can enter through voltage-gated calcium channels or NMDA receptors and identifies a resting intracellular concentration. It does not identify PMCA or the Na+/Ca2+ exchanger as the means of extrusion or say that they restore calcium to baseline. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [43] Clapham, D. E. Calcium signaling. Cell 131, 1047–1058 (2007). source
- Partly supportedcites [44] Catterall, W. A. & Few, A. P. Calcium channel regulation and presynaptic plasticity. Neuron 59, 882–901 (2008). sourceExperimental — accuracy not yet measured
The Introduction states that presynaptic voltage-gated calcium channels admit Ca2+ and that residual Ca2+ is cleared by a Ca2+-ATPase and a Na+/Ca2+ exchanger. It does not identify NMDARs as an influx route in this account or explicitly say these mechanisms restore intracellular calcium to its basal concentration. (LLM, conf=0.87)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The micro-devices represented by Fig. 1a have an area of 10µm×10µm and nano-devices with an area of 100nm×100nm exhibited similar switching behaviours (Supplementary Fig. S4).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“their operating characteristics were experimentally verified by demonstrating both short-and long-term plasticity, including mechanisms that have not been unambiguously demonstrated previously.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here we report such an emulator, which is a memristor based on metal atom diffusion and spontaneous nanoparticle formation, as determined by in situ high-resolution transmission electron microscopy (HRTEM) and nanoparticle dynamics simulations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An applied voltage above an apparent threshold abruptly switched the device to a conductance state limited by an external compliance current (Fig. 1b).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Repeatable and symmetric switching was demonstrated using wider bipolar voltage pulses with over a million switching cycles in Fig. 1d.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To demonstrate that the device spontaneously relaxed back to an insulating configuration upon removing the bias (without applying an opposite polarity voltage)”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The simulations for Ag in dielectric agreed well with the experimental HRTEM observations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under an applied pulse, the device exhibited threshold switching to a low resistance state after an incubation period τd, as shown in Fig. 3a.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 59
- Probably not a substantial claim. 9
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The calcium inside the postsynaptic membrane also plays important regulating roles in long-term potentiation and depression where Ca 2+ accumulation is necessary to regulate enzymes, i.e.Ca 2+ /calmodulindependent protein kinase II (CaMKII), calcineurin, and protein phosphatase 1 42,43,46,47 , which in turn triggers rapid and persistent modification of synaptic strengths by changing the number and”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [42] Burgoyne, R. D. Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat. Rev. Neurosci. 8, 182–193 (2007). sourceExperimental — accuracy not yet measured
The 'Different signals, diverse outcomes' section says postsynaptic Ca2+ signals can trigger LTP or LTD, and 'Ca2+ sensor proteins: calmodulin' links Ca2+/calmodulin to CaMKII and identifies calcineurin as a calmodulin target (page numbers unavailable). The paper does not establish the claimed pathway through protein phosphatase 1 or say that these enzymes persistently change synaptic strength by altering AMPAR number or conductance. Its References discuss possible AMPA-receptor endocytosis in LTD through hippocalcin, a different mechanism. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [43] Clapham, D. E. Calcium signaling. Cell 131, 1047–1058 (2007). source
- Partly supportedcites [46] Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004). sourceExperimental — accuracy not yet measured
In the passages headed “LTD Expression and Maintenance” (page unavailable), the review links LTP and LTD to AMPAR phosphorylation and identifies CaMKII as acting on an AMPAR phosphorylation site. Other passages describe postsynaptic Ca²⁺ as a trigger for LTD and report roles for calcineurin and protein phosphatase 1. The retrieved passages support changes in AMPAR surface expression, but do not establish the claim that these enzymes persistently change synaptic strength through altered AMPAR conductance. (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [47] Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Pinning can occur through interactions of the nanoparticles with impurities, the substrate and/or the ionic lattice 41 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Could not verifycites [41] Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016). sourceExperimental — accuracy not yet measured
The Noise spectra section discusses fluctuating atomic-scale contacts in memristors, while Methods mentions a substrate only as part of the device construction. Searches for nanoparticle pinning and interactions with impurities, substrates, or ionic lattices found no passage addressing the claim; our copy therefore does not verify it. The retrieved passages have no page numbers. (LLM, conf=0.76)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Moreover, a qualitatively similar result (although requiring considerably more computational resources) has been obtained when we modelled the interfacial energy as an interaction between particles 41 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Could not verifycites [41] Yi, W. et al. Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors. Nat. Commun. 7, 11142 (2016). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) describes a Molecular Dynamics–Langevin simulation that reproduces a crossover in memristor noise spectra. Searches for interfacial energy, particle–particle interactions, and the claimed modelling comparison found no passage in our copy addressing them. (LLM, conf=0.75)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The material systems exhibiting a substantial relaxation were those with large wetting contact angles 32 , such as MgOx:Ag, SiOxNy:Ag, and HfOx:Ag in Fig. 1b, consistent with the reported psudoelasticity of silver nanoparticles 33 and the hypothesis that interfacial energy facilitates filament rupture in volatile switching [34][35][36] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Partly supportedcites [32] Stoneham, A. M. Systematics of metal–insulator interfacial energies: a new rule for wetting and strong catalyst-support interactions. Appl. Surf. Sci. 14, 249–259 (1983). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked — no full text of the sourcecites [33] Sun, J. et al. Liquid-like pseudoelasticity of sub-10-nm crystalline silver particles. Nat. Mater. 13, 1007–1012 (2014). source
- Not matchedcites [34][35][36]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Symmetric hysteresis loops were observed with the opposite polarity bias (Supplementary Fig. S3), showing that the threshold switching is unipolar in nature and significantly different from non-volatile drift-type memristors, especially in the OFF-switching process.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“These devices are similar to electrochemical metallization memory (ECM) [21][22][23][24][25][26] cells in terms of utilizing mobile species of noble metals, but they differ substantially in terms of the structural symmetry and operating voltage polarities, metal concentration and profile, and transient switching behaviour.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [21][22][23][24][25][26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“X-ray photoelectron spectroscopy (XPS) revealed that the Ag was metallic, which was further confirmed by HRTEM micrographs showing Ag nanocrystals in SiOxNy:Ag”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization,”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The resistance ratio between the conducting and insulating states was 5-orders of magnitude in SiOxNy:Ag and over 10-orders in HfOx:Ag devices, the highest reported in threshold switching devices so far [27][28][29][30] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [27][28][29][30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“The volatile switching had sharp turn-on slopes of ~10mV/decade in MgOx:Ag and SiOxNy:Ag, and an extraordinary ~1mV/decade in HfOx:Ag, the sharpest demonstrated to date [27][28][29][30] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [27][28][29][30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reliable threshold switching was observed under a 100nA compliance in ambient conditions with a typical relaxation time constant of ~11ms (Supplementary Fig. S5b-c).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“If a subsequent pulse arrives before particles are re-absorbed, i.e., if the time between pulses is shorter than the diffusion relaxation time, more particles are pushed into the gap between terminals resulting in a gradual increase in device conductance, similar to the paired-pulse facilitation (PPF) phenomenon in bio-synapses.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in an inflection of the device conductance due to excessive stimulation, capturing another synaptic behaviour, i.e., PPF followed by PPD (paired pulse depression).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Eventually, most of the nanoparticles have merged into larger clusters to minimize interfacial energy, and the high resistance state is re-established along with the original particle distribution almost restored (compare Fig. 4a1 and Fig. 4a4), leading to the observed volatility in Fig. 2.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
In the paper
“The above PPF and PPD demonstrations were realized with diffusive memristors only, resembling short-term plasticity 45 in synapses because any conductance change from the OFF state of diffusive memristors will vanish over time.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [45] Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Third, sequential high voltage pulses with a long enough interval (low frequency) may form a conducting bridge first, but before the next pulse arrives the bridge breaks and the particles are re-absorbed at the terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 3b, τr decreased as the ambient temperature increased, consistent with a diffusion activation energy of 0.27eV (inset of Fig. 3b)”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As soon as the power is turned off, the temperature drops, and the nanoparticles start to coalesce (Fig. 4a3), i.e. particles slowly diffuse to their minimum energy positions near the device terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When a voltage pulse is applied (Fig. 4a), the local temperature increases due to Joule heating and the potential is tilted by electric forces acting on particles with induced charge, both of which cause larger clusters to break up.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the nanoparticles become more uniformly distributed in the gap, the resistance drops, the current and temperature increase, and a positive feedback results in the formation of a conductive channel (Fig. 4a2).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A smaller ∆t corresponds to a smaller diffusive memristor resistance and results in a greater resistance change in the drift memristor and vice versa (Fig. 6d).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This important feature of bio-synapses, which has not been clearly demonstrated previously on two terminal devices, was predicted in Fig. 4b and observed experimentally in Fig. 5c.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The elongated cluster of nanoparticles that had likely formed the conductive bridge rapidly contracted from a length of 14.8nm to a circular profile with a diameter of 7.6nm by 5.7s, indicating Ostwald ripening 31 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [31] Voorhees, P. W. The theory of Ostwald ripening. J. Stat. Phys. 38, 231–252 (1985). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The device in its steady state shows PPD upon low frequency (196Hz) stimulation and experiences an increase in current (facilitation) once the stimulation frequency is raised (5000Hz).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Similar to Fig. 5b, a shorter tzero resulted in a greater increase in the conductance of the diffusive memristor and thus a larger voltage drop across the drift memristor, which thereby switched due to the voltage divider effect.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At 4.6s, the cluster reached a diameter of ~4.2nm.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A longer tzero resulted in a smaller increase in the diffusive memristor conductance and thus a smaller voltage drop across the drift memristor, leading to a smaller or non-detectable resistance change in the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Upon channel formation, the current jumped abruptly by several orders of magnitude, and then slowly increased further under bias as the channel thickened.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The drift memristor is not switched by the first spike, because the majority of the voltage drops across the diffusive memristor and turns it ON first.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“First, when the initial voltage pulse is applied, electric field-assisted diffusion pumps some of the Ag-particles out of the 'left' cluster and they start to bridge terminals.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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- The authors state this as an assumption: The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
- The authors state this as an assumption: The tunnelling resistance amplitude is taken to be identical for every island.
- The authors state this as an assumption: Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
- The authors state this as an assumption: The simulation model omits redox reactions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the pre-spike appears before the postspike, the drift memristor conductance increases (potentiation).If the pre-spike follows the post-spike, depression occurs.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“In the combined element, the resistance of the diffusive memristor in its OFF state is much larger than that of the drift memristor, while the resistance of its ON state is much smaller than that of the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“We observed a delay time of ~2s during which the measured current was <5nA and Ag nanoparticles formed in the gap region”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“The microscopic nature of both the threshold switching and relaxation of the diffusive memristor is revealed for the first time by in situ HRTEM and explained by nanoparticle dynamics simulation.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“curve) induced by a voltage pulse (red curve) shows a delay in the response and a gradual relaxation towards the low conducting state, consistent with the experimental threshold switching property in Fig. 3a.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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In the paper
“When the post-spike precedes the pre-spike, the device is reset (depressed), and when the prespike precedes the post-spike, the device is set (potentiated).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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In the paper
“As one can see from Fig. 4, the relaxation occurs on time scales of the order 10-20 1/κ, thus, the simulated system is clearly governed by diffusion rather than the temperature relaxation 19 .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
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In the paper
“from Fig 4b we conclude that the conductance reached 25% of the maximum after 4 pulses and about 75% after 7 pulses.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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In the paper
“Device current response (blue) to multiple dependent plasticity (SRDP and true STDP) behaviour of a combined device consisting of a diffusive and a drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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In the paper
“The long low voltage pulse in each spike turns the diffusive memristor ON, and the short high voltage pulse switches the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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In the paper
“The device did not fail during the measurement, demonstrating high robustness.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Highly non-linear, fast, and repeatable threshold switching behaviours of diffusive memristors”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Additional information
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In the paper
“The device requires a finite delay time to turn ON and has a finite relaxation time before it goes to the high resistance state after the switching pulse is removed.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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In the paper
“For long tzero, the change in the conductance of the diffusive memristor is lower (see Fig. 5b), resulting in a lower weight change of the drift memristor.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the external biasing is removed at 5.0s, the filament starts to deform, shrinking to a round spherical nanocluster implying an interfacial energy driven diffusion mechanism.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
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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.
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In the paper
“Various types of memristors based on ionic drift (drift-type memristor) [4][5][6][7][8] have recently been utilized for this purpose in neuromorphic architectures [9][10][11][12][13][14][15] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [4][5][6][7][8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [9][10][11][12][13][14][15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Recently, Lu's group adopted second-order drift memristors to approximate the Ca 2+ dynamics of chemical synapses by utilizing thermal dissipation 19 or mobility decay 20 , which successfully demonstrated STDP with non-overlapping spikes and other synaptic functions”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not checked — no full text of the sourcecites [19] Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015). source
- Not checked — no full text of the sourcecites [20] Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015). source
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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.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although qualitative synaptic functionality has been demonstrated, the fast switching and non-volatility of drift memristors optimized for memory applications do not faithfully replicate the nature of plasticity.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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In the paper
“Ca 2+ dynamics is responsible for initiating both short-and long-term plasticity of synapses, forming the basis of memory and learning [42][43][44] .”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
What the sources say
- Not matchedcites [42][43][44]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The particular shape of neither the interfacial potential nor the pinning energy profile are essential; the only property that matters is that the potential 𝑈𝑈 has two different energy scales and the weaker pinning potential has many wells (for example it can be random with fluctuating 𝑤𝑤 𝑝𝑝 and 𝑖𝑖 𝑝𝑝 ).”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the dynamics of the diffusive memristor provides an intrinsic timing mechanism for the combined element, the spike-ratedependent plasticity and STDP do not require complex pulse engineering or spike overlapping.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Ag dynamics of the diffusive memristors functionally resemble the synaptic Ca 2+ behavior in chemical synapses and lead to a direct and natural emulation of multiple synaptic functions for both short-term and long-term plasticity, such as PPF, PPD, PPD following PPF, SRDP and STDP.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This substantially reduces the complexity of both circuit and algorithm design and enables lowenergy operations.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Abstract
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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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Equations ( 4) and ( 5) are sufficient to describe the memristor dynamics subject to the current through the device, which is controlled by the electric circuit in which it is embedded.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“programming is at the instrument noise level (<100nA) for the current measurement range used.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These quantities can be easily obtained from both simulations (averaged over 30 realizations) and experimental I-V hysteresis loops, thus directly linking the units in the simulations and the experiments.”
§ Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing > Electrical Measurements.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
The model treats electron flow as a series of tunnelling hops: input terminal to closest metal particle, particle to particle, and last particle to output terminal.
“For the state-dependent resistance we assume sequential electron tunnelling: first from the input terminal to the nearest metallic nanoparticle, and then from this nanoparticle to the next one and so on with the last tunnelling event to the output terminal.”
The authors state this as an assumption.
The tunnelling resistance amplitude is taken to be identical for every island.
“R 𝑡𝑡 the tunneling resistance amplitude (assumed the same for all islands)”
The authors state this as an assumption.
Each mobile metal nanoparticle is described by an overdamped Langevin equation with a trapping potential and a random force whose strength depends on the local device temperature.
“we employed an overdamped Langevin equation for each mobile metallic nanoparticle trapped by a potential 𝑈𝑈 and subject to a random force 𝜉𝜉 𝑖𝑖 , the magnitude of which is determined by the device local temperature 𝑇𝑇,”
The authors state this as an assumption.
The simulation model omits redox reactions.
“The model results here did not include redox reactions, although they can be added in order to more closely resemble the electrochemical models proposed previously [21][22][23][24][25][26] .”
The authors state this as an assumption.
The model's potential energy landscape has two distinct energy scales: a stronger interfacial term that forms large metallic clusters near the device terminals and a weaker pinning term with many wells between electrodes.
“The potential 𝑈𝑈 = 𝑈𝑈 𝐼𝐼 + 𝑈𝑈 𝑝𝑝 has two energy scales -the interfacial energy 𝑈𝑈 𝐼𝐼 responsible for formation of large metallic clusters near the device terminals and a weaker nanoparticlepinning energy 𝑈𝑈 𝑝𝑝 with many smaller wells between the electrodes.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The decay of the conductance is considered to arise from the surface diffusion of Ag caused by the Gibbs -Thomson effect 41 -45 .”
From Enhanced analog synaptic behavior of SiNx/a-Si bilayer memristors through Ge implantation (2020) · cites this paper as [42]
Experimental — accuracy not yet measuredIn the citing paper
“In contrast to ref. 34 we have also taken into account the memristor self-capacitance, which is critically important to describe the delay time distributions.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [34]
Citations
Cites
Calcium's Role in Mechanotransduction during Muscle Development
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Research works printed but not linked: 2
- Jo, S. H., Kumar, T., Narayanan, S., Lu, W. D. & Nazarian, H. Electron Devices Meeting (IEDM) 6.7.1–6.7.4 (IEEE International, 2014).
- Luo, Q. et al. Electron Devices Meeting (IEDM) 10.14.11–10.14.14 (IEEE International, 2015).
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Introduction to photo-electroactive nonvolatile memory
Mao, J., & Zhou, Y. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819717-2.00001-1 · Cited by 1
System-level integration in neuromorphic co-processors
Indiveri, G., Linares-Barranco, B., & Payvand, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00018-6 · Cited by 1
Synaptic realizations based on memristive devices
Milo, V., Dalgaty, T., Ielmini, D., & Vianello, E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00017-4 · Cited by 1
Zhu, X., & Hu, Y. (2020) · Acta Physica Sinica · doi:10.7498/aps.69.20200502 · Cited by 1
Doping-Enabled Reconfigurable Strongly Correlated Phase in a Quasi-2D Perovskite
Zhang, L., Jiang, J., Cai, Y., Yao, S., Azhar, B., Chen, Z., Hu, Y., Pendse, S., Guo, Y., Jia, R., et al. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00353 · Cited by 1
Diffusive dynamics of Brownian nanoparticles in a flashing optical field
Luo, Z., Liu, D., Qiu, J., Peng, L. J., Luo, K., & Han, P. (2022) · Optics Communications · doi:10.1016/j.optcom.2022.128069 · Cited by 1
Neuromorphic applications using MOx-based memristors
Brivio, S., & Vianello, E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-814629-3.00014-3 · Cited by 1
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Erokhin, V. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-79492-7_6 · Cited by 1
Transient Response and Firing Behaviors of Memristive Neuron Circuit
Fang, X., Tan, Y., Zhang, F., Duan, S., & Wang, L. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.922086 · Cited by 1
“Memristors have been applied to the emulation of synapses and neurons (Lu et al., 2020), the realization of neuromorphic computing (Wang et al., 2016), and the implementation of the bio-inspired intelligent system (Duan et al., 2020).”
MethodTa/HfO2-based Memristor and Crossbar Arrays for In-Memory Computing
Jiang, H., Li, C., Lin, P., Wang, Z., Yang, J. J., & Xia, Q. (2022) · Springer eBooks · doi:10.1007/978-3-030-90582-8_8 · Cited by 1
Self-citationDual sampling neural network: Learning without explicit optimization
Teramae, J.-N., & Tsubo, Y. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043051 · Cited by 1
Oxide Thin Films for Memristive Devices
Valov, I. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.13874-0 · Cited by 1
Asymptotic behavior of memristive circuits and combinatorial optimization.Preprint
Caravelli, F. (2017) · arXiv (Cornell University) · Cited by 1
In-memory computing by RRAM for machine learning
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Hardware Implementations of Spiking Neural Networks
Zheng, N., & Mazumder, P. (2019) · doi:10.1002/9781119507369.ch5 · Cited by 1
Electronegative metal dopants improve switching consistency in Al2O3 resistive switching devicesPreprint
Tan, Z.-J., Somjit, V., Toparli, C., Yildiz, B., & Fang, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13301
Self-Adaptive Spike Voltage-dependent Plasticity Emulated by a Metalloporphyrin-Based MemristorPreprint
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Self-citationModeling and Characterization of Stochastic Resistive Switching in Single Ag2S NanowiresPreprint
Frick, N., Hosseini, M., Guilbaud, D., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-1096723/v1 · Cited by 1
Melrose, J., & Smith, M. M. (2024) · Preprints.org · doi:10.20944/preprints202406.1275.v1 · Cited by 1
2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
Self-citationAssociative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays
Prasad, N., Mukim, P., Madhavan, A., & Stiles, M. D. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac7d05 · Cited by 13
Ge, N. (2017) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/69440
Self-citationRole of diversity in taming chaos in driven memristive arrays
Buscarino, A., Corradino, C., Fortuna, L., et al. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351879
“In [5], the emulation of both short- and long-term plasticity of biological synapses has been achieved with diffusive memristors representing an advance in hardware implementation of neuromorphic functionalities.”
BackgroundAn HAM-Based Analytic Modeling Methodology for Memristor Enabling Fast ConvergencePreprint
Hu, W., Du, Y., Luo, H., Chen, C., & Wei, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00317 · Cited by 2
“EMRISTOR (also known as Resistive Random Access Memory) can be used in memory [1]–[4], neurobiology [5]–[7], artificial intelligence [8]–[10], and neural networks [11], [12], etc., with great application prospects, due to its advantages such as fast speed [13], nanometers scale [14], and low-power…”
BackgroundAn Artificial Bio-Synapse Based on Ag/a-Si:Ag/a-Si/X Memristors With Different Bottom Electrode X
Chen, Y., Li, D.-Y., Liu, C., Jiang, X.-D., & Li, W. (2018) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/452/4/042160
Epitaxial SiGe synapses for neuromorphic arrays
Tan, S. H. (2018) · DSpace@MIT (Massachusetts Institute of Technology)
Energy Harvesting is Charging Up
Gummeson, J. (2019) · GetMobile Mobile Computing and Communications · doi:10.1145/3325867.3325877
Neuromorphic Computing with Memristors: From Devices to Integrated Systems
Cai, F. (2019) · Deep Blue (University of Michigan)
Resistive Switching Devices: Mechanism, Performance and Integration
Liu, M., Liu, Q., Lv, H., & Long, S. (2019) · Springer eBooks · doi:10.1007/978-3-319-76375-0_30
Performance and Comparisons of STDP based and Non-STDP based Memristive Neural Networks on HardwarePreprint
Tang, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09126
Muto, S., Sakai, S., Tsurumaki‐Fukuchi, A., 有田正志, & Takahashi, Y. (2020) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ab79eb
Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems
Yang, K. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Neuronal realizations based on memristive devices
Wang, Z., Midya, R., & Yang, J. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00016-2 · Cited by 1
Self-citationWeighted synaptic behaviors of HfON based RRAM device by a novel waveform modulation method
Ma, Y., Li, R., Yu, A., et al. (2020) · IEEE Silicon Nanoelectronics Workshop · doi:10.1109/snw50361.2020.9131670
“Another form of memory device , conductive bridge RAM (CBRAM) is employed to realize paired-pulse facilitation/depression (PPF/PPD) by modulating the pulse stimulation[2].”
MethodSelf-citationGate-Oxide Trapping Enabled Synaptic Logic Transistor
Ju, X., & Ang, D. S. (2020) · IEEE International Reliability Physics Symposium proceedings · doi:10.1109/irps45951.2020.9129338
“Such devices include resistive random access memory (RRAM) [10-15], phase-change random access memory (PCRAM) [16-17], ferroelectric random access memory (FeRAM) [18-19], etc.”
BackgroundPotential of Memristors in Medical Image Processing and Concerns in the Clinical Context
Grothe, P., & Haase, J. (2020) · IEEE-HYDCON · doi:10.1109/hydcon48903.2020.9242751
“Apart from memory implementations, the memristor is promising for applications in programmable circuits [9] and neuromorphic computing [10].”
MethodArtificial Intelligence Accelerators based on Graphene Optoelectronic DevicesPreprint
Gao, W., Yu, C., & Chen, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01979
Adaptive Extreme Edge Computing for Wearable DevicesPreprint
Covi, E., Donati, E., Heidari, H., Kappel, D., Liang, X., Payvand, M., & Wang, W. J. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-200278
RRAM Device Characterizations and Modelling
Huang, P. C., Gao, B., & Kang, J. (2021) · Springer eBooks · doi:10.1007/978-981-15-6912-8_11
Analysis of Memristive Quantized Convolutional Neural Network Accelerator with Device Nonideality
Bao, H., Huang, X., Chen, J., Yi, L., & Miao, X. (2020) · IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA) · doi:10.1109/icta50426.2020.9332143
“The other is the holistic shift of each conductance state, due to the competition of the conductive filament intensify and dissolution, described as the mean p of the weights [9, 10], During simulation, a Gaussian noise with different o (0-0.5) is applied on the well-trained weights for inference.”
MethodInfluentialSousa, J. B., Ventura, J. O., & Pereira, A. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-46097-2.00006-9
Redox-based memristive devices : towards highly scalable synaptic electronics
Dananjaya, P. A. (2020) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/146143
Liu, C., Zhu, L. S., Wang, L.-G., & Li, A. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.97753
“The new memristor has nonlinear transfer characteristics similar to the bio-synapse and is regarded as the closest to the synaptic device [4].”
BackgroundDias, C., Castro, D., Aroso, M., Ventura, J. O., & Aguiar, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.20.444941
Implementation of organic RRAM with ink-jet printer: from design to using in RFID-based application
Thanh, T. D., Pham, V., & VOLOS, C. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821184-7.00026-8
Basnet, P., Anderson, E., Chakrabarti, B., West, M. P., Athena, F. F., & Vogel, E. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02247
Research on emergent physical properties of integrated nanomaterials
リ, チ., & Li, Q. (2020)
Using memristors in a functional spiking-neuron model of activity-silent working memory
Boekestjin, J. W. (2021)
Sahu, P. (2021) · University of Minnesota Digital Conservancy (University of Minnesota)
Processing and Switching Mechanisms of Materials for Memory Devices in Flexible Electronics
Almadhoun, M. N. (2021) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-y1vd-hj60
逐次モンテカルロ法を用いたBayes的顔画像認識(テーマセッション(4),CVのためのパターン認識・学習理論の新展開)
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Морфологическая характеристика алмазов из туффизитов месторождения «Ефимовское» Вишерского Урала
Korotchenkova, O. V., & Чайковский, И. И. (2012) · CyberLeninK (CyberLeninka)
Neuromorphic Computing Systems with Emerging Devices
Wei, Q., Tang, J., Gao, B., Li, X., Qian, H., & Wu, H. (2021) · Wiley eBooks · doi:10.1002/9783527835317.ch6 · Cited by 2
Reset Switching Statistics of TaOx-Based Memristor
Lian, X., Wang, M., Yan, P., Yang, J. J., & Miao, F. (2021) · Kluwer international series in electronic materials: science and technology · doi:10.1007/978-3-030-42424-4_8
Self-citationSiO2-Based Conductive-Bridging Random Access Memory
Chen, W., Tappertzhofen, S., Barnaby, H., & Kozicki, M. N. (2021) · Kluwer international series in electronic materials: science and technology · doi:10.1007/978-3-030-42424-4_7
An Artificial Neutral Network Chip Based on Two-Dimensional SemiconductorPreprint
Ma, S., Wu, T., Chen, X., Wang, Y., Tang, H., Yao, Y., Wang, Y., Deng, J., Wan, J., Sun, Z., et al. (2020) · doi:10.21203/rs.3.rs-101486/v1
The Fuzziness in Molecular, Supramolecular, and Systems Chemistry
Gentili, P. L. (2020) · MDPI eBooks · doi:10.3390/books978-3-03943-179-3
Nanoscale HfO2-based memristive devices for neuromorphic computing
Hoffmann‐Eifert, S. (2022) · Conference digest · doi:10.1109/drc55272.2022.9855810
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Karmakar, A., Mukherjee, A., Dhar, S., et al. (2022) · Semiconductor Science and Technology · doi:10.1088/1361-6641/ac92a3
Hylton, T. L. (2022) · River Publishers eBooks · doi:10.1201/9781003338215-6 · Cited by 2
Memristive/CMOS Devices for Neuromorphic Applications
Milo, V., Malavena, G., Compagnoni, C. M., & Ielmini, D. (2022) · Springer handbooks · doi:10.1007/978-3-030-79827-7_32
Huang, P. C., Zhao, Y., & Kang, J. (2022) · Springer handbooks · doi:10.1007/978-3-030-79827-7_29
Sichani, A. S., Kadari, K. K., & Moreno, W. A. (2022) · IEEE Latin American Electron Devices Conference (LAEDC) · doi:10.1109/laedc54796.2022.9908216
“Many experimental studies on memristive devices have illustrated synaptic functionalities, so STDP learning through memductance modulation, revealing the memristors' suitability in neuromorphic circuit design [7,8].”
BackgroundZheng, J., Yu, S., Wu, J., Wang, Y., Deng, C., & Lin, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20503-3_39
Graphene for Silicon Optoelectronic Synaptic Devices
Xu, Y., Shehzad, K., Bodepudi, S. C., Imran, M. A., & Yu, B. (2023) · doi:10.1002/9783527841011.ch9
Transfer-free graphene passivation of sub 100 nm thin Pt and Pt–Cu electrodes for memristive devices
Tappertzhofen, S., Braeuninger‐Weimer, P., Gumprich, A., Chircă, I., Potočnik, T., Alexander-Webber, J., & Hofmann, S. (2023) · SN Applied Sciences · doi:10.1007/s42452-023-05314-x
Neuromorphic computing devices based on the asymmetric temperature gradient
Chen, H.-J., Chiang, C.-C., Cheng, C. Y., et al. (2023) · Applied Physics Letters · doi:10.1063/5.0155229
Zhang, P., Ma, G., Xiong, Z., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4508355
Acacia Senegal-based Non-Newtonian Electrolyte for Memristive ApplicationsPreprint
Lokhandwala, A., Thakkar, P., Gosai, J., Oza, S., & Solanki, A. (2023) · Research Square · doi:10.21203/rs.3.rs-3105809/v1
Ren, X., Wu, X., Qin, M., et al. (2023) · Physica B Condensed Matter · doi:10.1016/j.physb.2023.415168
A Floating Memristor Emulator with Inverse Frequency CharacteristicPreprint
Ananda, Y. R., Raj, N., & Trivedi, G. (2023) · doi:10.36227/techrxiv.24072450.v1
Zhang, P., Ma, G., Xiong, Z., et al. (2023) · Vacuum · doi:10.1016/j.vacuum.2023.112623
Organic Resistive Memories for Neuromorphic Electronics
Zhuang, Y., Liu, S., & Zhao, Q. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/bk9781839169946-00060
Silicon Oxide-based CBRAM Memory and Neuromorphic Properties
Bousoulas, P., & Tsoukalas, D. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/bk9781839169946-00515
Quantum transport simulation of synaptic FETs based on two-dimensional semiconductors
Ishibashi, T., & Souma, S. (2023) · International Conference on Simulation of Semiconductor Processes and Devices · doi:10.23919/sispad57422.2023.10319560
Non-traditional New Structure Devices
Cai, Y., Xu, J., Liang, R., et al. (2023) · Springer eBooks · doi:10.1007/978-981-99-2836-1_81
Self-citationNanoscale Mem-Devices for Chemical Sensing
Ibarlucea, B., Yıldırım, E. E., Tetzlaff, R., et al. (2023) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs58634.2023.10382795
“Memristors [2], neuro-transistors [3], and other similar devices have emerged as promising solutions in this domain.”
BackgroundChandrasekar, L., Shaik, R. R., Rajakumari, V., et al. (2024) · Semiconductor Science and Technology · doi:10.1088/1361-6641/ad3844
One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units
李, Y. 亚. L., Shi, K., 朱, Y. 一. Z., et al. (2024) · Chinese Physics B · doi:10.1088/1674-1056/ad39d6
Kim, W. J., Lee, J., Kim, R. N., Lee, D., & Kim, W. (2024) · Korean Journal of Materials Research · doi:10.3740/mrsk.2024.34.3.152
Low-Power Perovskite-Based Threshold Switching Memristor for Artificial NociceptorPreprint
Li, Y., Li, J., Ni, J., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4809104
Gate tunable MoS2 memristive neuron for early fusion multimodal spiking neural networkPreprint
Tian, H., Liu, Y., Peng, Y., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4190143/v1
AL2O3/ALOX Memristor with Nearly Ideal Synaptic Characteristics
He, Q., Wang, H.-L., Bai, Y., Hu, J., Li, H., Ma, W., Xu, Y., Zhang, Y.-S., & Yu, B. (2024) · China Semiconductor Technology International Conference · doi:10.1109/cstic61820.2024.10531814
“To address this issue, researchers have turned to memristors, which emulate the structure and working principles of the biological synapse, offering a promising approach to developing neuromorphic computing hardware [1].”
BackgroundRecent Progress of Memristor-based Neuromorphic Computing
Zhou, J. (2024) · Transactions on Computer Science and Intelligent Systems Research · doi:10.62051/1kany131
“[7] This makes memristor-based NMC systems potentially more efficient than traditional CMOS processors in handling complex pattern recognition and data processing tasks.”
MethodThe current status of the development of photomemoryristors in the field of neuromorphic vision
Wang, K. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/72/20241064
Bojić, L., & Čabarkapa, M. (2024) · Napredak · doi:10.5937/napredak5-51738
Response of a Memristor to an External Noise Signal
Filatov, D. O., Vrzheshch, D. V., & Dubkov, A. A. (2024) · Fluctuation and Noise Letters · doi:10.1142/s0219477524400649
Performing hardness classification using diffusive memristor based artificial neurons
Sharma, Y., & Pattnaik, D. P. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad937a
Emulating complex synapses using interlinked proton conductors
Zhang, L., Li, J., Hu, Y., Jiang, J., Lai, R., Benna, M. K., & Shi, J. Z. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.014027
Neuromorphic systems for real-time image processing
Pasupathy, V., & Khilar, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21480-6.00002-x · Cited by 1
Dynamical Systems and the Corresponding Electronic Models
Семенов, В. В. (2024) · Springer series in synergetics · doi:10.1007/978-3-031-81553-9_5
Laser-Guided Ion Dynamics in a Dual-Mode Memristor for Bioinspired Neuronal and Synaptic IntegrationPreprint
Lee, K. J., Jeong, Y. J., Kim, K., Kim, Y. B., Shin, H., Oh, J. W., & Sung, S. H. (2025) · Research Square · doi:10.21203/rs.3.rs-6140700/v1
Lim, S., & Woo, J. (2025) · AIP Advances · doi:10.1063/5.0270506
A review on footsteps of a revolution in electronics: Spin memristors
AL-EDRESI, A., Aydın, G., Abboubi, M. E., Kazan, S., Candan, İ., & San, S. E. (2025) · Materials Today Physics · doi:10.1016/j.mtphys.2025.101760
Meghana, E. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem49065
Design and Simulation of an Efficient Memristor Based Multiplier Using Hybird Logic
Paramasivam, K., R.Nithya, A.Jayashree, S.Aneesh, & Vijay, R. (2025) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr62650.2025.11019118
“The model validated the memristor's appropriateness for digital logic and arithmetic applications by faithfully representing the dynamic variation of resistance states during SET and RESET operations [15].”
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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