The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index; none of the 33 sources its claims cite is checked for support yet.
Stochastic memristive devices for computing and neuromorphic applications
Open accessAbstract
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially large variations in space and time in these nanoscale devices. Here we show that in metal-filament based memristive devices the switching can be fully stochastic. While individual switching events are random, the distribution and probability of switching can be well predicted and controlled. Rather than trying to force high switching probabilities using excess voltage or time, the inherent stochastic nature of resistive switching allows these binary devices to be used as building blocks for novel error-tolerant computing schemes such as stochastic computing and provides the needed "analog" feature for neuromorphic applications. To verify such potential, we demonstrated memristor-based stochastic bitstreams in both time and space domains, and show that an array of binary memristors can act as a multi-level "analog" device for neuromorphic applications.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Memristive devices based on Ag filament formation in amorphous silicon show fully stochastic resistive switching, but the switching probability can be predicted and controlled by voltage and pulse width. This enables random bitstreams in time and space and allows a 4-device array to act as a multi-bit synapse for stochastic computing and neuromorphic applications.
Key findings
- Wait times before switching are broadly distributed and fit a Poisson distribution with one fitting parameter τ. For applied voltages of 2.5 V, 3.5 V, and 4.5 V, τ = 340 ms, 4.7 ms, and 0.38 ms, respectively; a 2 V increase in voltage drops the characteristic time by almost three orders of magnitude.
- Switching probability is cumulative and predictable: a 2.5 V/300 ms pulse switched the device 20 times out of 50 trials (bias ratio p = 0.4), while a 2.5 V/1000 ms pulse switched it 38 times out of 50 (p = 0.76), closely matching Eq. (2).
- Random bitstreams were generated in the time domain, and identical pulses applied to different devices produced 20-bit random bitstreams with similar bias ratios. A 4-device array programmed in parallel produced 4-bit-wide random bitstreams and, used as a whole, represents a 2-bit weight synapse; the authors state this is the first experimental demonstration of such a concept.
- Discrete short pulses are cumulative regardless of gap: with 2.5 V, 100 ms pulses, almost identical τ values were obtained for gaps of 10 ms and 500 ms, and the number of pulses needed to switch followed a Poisson distribution.
Methods
- Two-terminal crossbar devices were fabricated with a 60 nm boron-doped polysilicon bottom electrode, a 35 nm amorphous silicon insulating layer, and a silver top electrode, using electron beam lithography, PECVD, liftoff, and RIE.
- Electrical measurements used a National Instruments USB-6259 BNC data acquisition system with a DL 1211 current preamplifier and MATLAB code; parallel testing used analog switches (ADG1412) and an Atmega32u4 microcontroller.
- Switching statistics were measured by resetting devices to the OFF state, applying constant voltages (2.5, 3.5, 4.5 V) and repeating 100 times per bias, or applying fixed pulses (e.g., 2.5 V/300 ms and 2.5 V/1000 ms for 50 attempts); array experiments used a single 2.75 V, 1000 ms pulse for parallel programming and 2 V, 100 ms pulses for individual readout.
Limitations
- The paper states this is a proof-of-concept demonstration; large-scale implementation must address spatial variations, power, speed, endurance, and reliability, and logic applications require very high endurance with switching probability that does not degrade over time and is uniform among devices.
- The paper states that efficient peripheral circuit design to account for endurance limits under low-voltage operation and degradation of switching probability with time remains for future studies.
- The text makes evident that experiments were limited to single devices and a 4-device array, with small trial counts (50 or 100), and did not demonstrate large-scale arrays or long-term reliability.
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Claims the paper makes
30 Own result · 17 Not checked yet · 15 Own interpretation · 8 No basis given · 3 Sources not found · 2 Own method
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 Poisson distribution of the wait time, with the standard deviation equaling the mean, further verifies that the switching is random and stochastic in nature.”
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In the paper
“However, careful examination of the switching effects reveals that significant randomness can exist, even in the same device.”
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In the paper
“The wait time before switching shows apparent randomness.”
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In the paper
“While individual switching events are random”
- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 3 · plain
“the individual switching events occur randomly in nature.”
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- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 3 · plain
In the paper
“Binary resistive switching can be reliably observed in such devices, as shown in the inset to Fig. 1a and as discussed earlier (15).”
What the sources say
- Not checked yetcites [15] 15. Jo, S. H.; Lu, W. CMOS compatible nanoscale nonvolatile resistance switching memory. Nano letters 2008, 8 (2), 392-397. source
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In the paper
“even for a given voltage applied to the same device, the wait time is not fixed but rather shows a large distribution.”
- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
“Even for a fixed voltage on the same device, the wait time is not fixed and is random and broadly distributed.”
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- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
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In the paper
“we demonstrated memristor-based stochastic bitstreams in both time and space domains”
- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
“The memristive devices have been used to generate random bitstreams with predicable bias ratios in time and space domains.”
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- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
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In the paper
“Here we show that in metalfilament based memristive devices the switching can be fully stochastic.”
- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
“we show binary memristive devices can exhibit native stochastic nature of resistive switching.”
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- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
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
“show that an array of binary memristors can act as a multi-level 'analog' device for neuromorphic applications.”
- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
“The array of binary memristive devices can also act as a multi-bit synapse in neuromorphic applications.”
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- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
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In the paper
“With an increase in 2 V in the applied voltage, the characteristic time drops sharply by almost three orders of magnitude (Fig. 1d).”
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- Probably not a substantial claim. 14
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In the paper
“As can be seen from Fig. 1b and Fig. 1c, the switching time distribution at different programming voltages preserves the Poisson nature but the characteristic wait time decreases significantly as the bias voltage is increased.”
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In the paper
“Fig. 1 shows that excellent fit of the wait times to the Poisson distribution in Eq. (1) can be obtained with just one fitting parameter ( τ ), in agreement of the hypothesis of thermodynamic activation over a dominant energy barrier during filament formation in these devices.”
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In the paper
“the distribution and probability of switching can be well predicted and controlled.”
- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
“the probability of switching can be predicted and controlled by the applied voltage and the pulse width used to program the device.”
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- Also stated in: § Index Terms -Resistive memory, memristor, bitstreams, stochastic, neuromorphic computing · p. 11 · plain
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In the paper
“It can be seen from DC sweeps that the devices exhibit standard switching hysteresis with clearly defined ON and OFF states and an apparent threshold voltage of ca. 5 V (Fig. 1a inset).”
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“Since the characteristic wait time τ is strongly voltage dependent, the average wait time can be reduced drastically at higher voltages too.”
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“As expected, the distribution for the number of pulses needed to switch the device also follows a Poisson distribution and can be fitted with just one fitting parameter τ .”
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In the paper
“It is also important to note here we can control only the bias ratio of the bit-stream (i.e. the overall number of 1s), while the locations of individual 1's are random.”
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In the paper
“Indeed, if the 4 devices share the same series resistor, we indeed observed only one device in the ON state after each programming pulse, i.e. the device array simply behaves as a single, larger device.”
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In the paper
“In Fig. 4 we see that each bit in the bitstream was generated at a different time instance. In other words, the random bits were produced in serial fashion one after the other. This is the commonly termed as 'stochastic bitstream in time'.”
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In the paper
“However, if each device has its own local, series resistor, as shown in Fig. 5a inset, the completion of the dominant filament in one device does not cause the decrease of voltage seen by other devices thus multiple devices can be switched.”
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In the paper
“To modulate this bias ratio or, in other words, to generate another number y , all we need to do is to change the programming time or the programming voltage.”
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In the paper
“Finally, we would like to point out that this study presents a proof-of-concept demonstration in stochastic and neuromorphic computing approaches by taking advantage of the inherent temporal variations in resistive devices.”
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In the paper
“The device switches to the ON state twenty times out of the fifty trials or attempts. This number closely matches that predicted from the theoretical curve given by Eq. (2).”
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In the paper
“In other words, each time the array of 4 devices is programmed, we get a 4-bit wide random bitstream from the array, and exactly which device will switch cannot be predicted at the start of the programming cycle.”
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In the paper
“The concept of using an array of memristive devices to represent a multi-bit synapse was proposed a few years ago (25), and to our knowledge this is the first experimental demonstration of such a concept.”
What the sources say
- Not matchedcites [25] 25. Likharev, K.; Mayr, A.; Muckra, I.; T. CrossNets: High-Performance Neuromorphic Architectures for CMOL Circuits. Annals of the New York Academy of Sciences 2003, 1006 (1), 146-163.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Filament formation involves oxidation, ion transport and reduction processes, all of which are thermodynamically driven (5,18) and require overcoming specific activation energies.”
What the sources say
- Not checked yetcites [5] 5. Valov, I.; Waser, R.; Jameson, J. R.; Kozicki, M. N. Electrochemical metallization memoriesfundamentals, applications, prospects. Nanotechnology 2011, 22 (25), 254003. source
- Not checked yetcites [18] 18. Lu, W.; Jeong, D. S.; Kozicki, M.; Waser, R. Electrochemical metallization cells-blending nanoionics into nanoelectronics? MRS bulletin 2012, 37 (02), 124-130. source
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In the paper
“As a result, even for the same filament in the same device, the wait time will be broadly distributed and in principle can only be predicted in terms of statistics while the individual switching events occur randomly in nature.”
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In the paper
“The change from OFF to ON state is explained by the formation of a dominating Ag filament (15,16).”
What the sources say
- Not checked yetcites [15] 15. Jo, S. H.; Lu, W. CMOS compatible nanoscale nonvolatile resistance switching memory. Nano letters 2008, 8 (2), 392-397. source
- Not checked yetcites [16] 16. Yang, Y.; Gao, P.; Gaba, S.; Chang, T.; Pan, X.; Lu, W. Observation of conducting filament growth in nanoscale resistive memories. Nature Communications 2012, 3, 732. source
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In the paper
“Thus by increasing the programming voltage, τ can be reduced significantly down to the nanosecond regime (14) so switching with high success rate can be achieved.”
What the sources say
- Not checked yetcites [14] 14. Jo, S. H.; Kim, K.-H.; Lu, W. Programmable resistance switching in nanoscale two-terminal devices. Nano letters 2009, 9 (1), 496-500. source
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In the paper
“Since stochastic operations require lower voltages than conventional binary switching it is expected that even better endurance can be obtained using the proposed approach.”
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In the paper
“For more efficient computing, bitstreams in space domain, i.e. bits generated in parallel or 'stochastic bitstreams in space' are also required, which leads to parallel processing of the bitstreams.”
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In the paper
“SNGs account for the majority of the resource usage in stochastic computing, with a cost as high as 80% of the entire system (24).”
What the sources say
- Not matchedcites [24] 24. Alaghi, A.; Hayes, J. P. Survey of Stochastic Computing. ACM Transactions on Embedded Computing Systems (TECS) to appear .
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Recent studies have already shown that similar devices can be very reliable, robust and switch within nanoseconds (31,32,33).”
What the sources say
- Not checked yetcites [31] 31. Lee, M.-J.; Lee, C. B.; Lee D.; Lee, S. R.; Chang M.; Hur J. H., Kim Y.-B.; Kim, C- .J.; Seo, D. H.; Seo, S.; Chung, U.-I.; Yoo I.-K. & Kim K. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O 5x /TaO 2x bilayer structures. Nature Materials, 2011 , 10, 625-630.
- Not checked yetcites [32] 32. Yang, J.-J.; Zhang, M.-X.; Strachan, J. P.; Miao, F.; Pickett, M.D.; Kelley, R. D.; Mederos-Ribeiro G and Williams, R. S. High switchign endurance in TaOx memristive devices. Appl Phys. Lett. 2010 , 97, 232102 .
- Not checked yetcites [33]
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In the paper
“In this experiment, after each programming pulse the device was reset to the same original OFF state so each programming pulse starts with the same initial condition.”
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In the paper
“However, using excessively high voltage and excessively long pulses can result in an increased power envelope and lead to unnecessary device degradation.”
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In the paper
“Binary synapses are easier to fabricate, more robust and offer larger dynamic range (4,5).”
What the sources say
- Not checked yetcites [4] 4. Waser, R.; Aono, M. Nanoionics-based resistive switching memories. Nature materials 2007, 6 (11), 833-840. source
- Not checked yetcites [5] 5. Valov, I.; Waser, R.; Jameson, J. R.; Kozicki, M. N. Electrochemical metallization memoriesfundamentals, applications, prospects. Nanotechnology 2011, 22 (25), 254003. source
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In the paper
“Stochastic computing was first proposed in the 1960s (26,27) but like many concepts proposed ahead of their time it never become mainstream due to the exponential growth of digital computers.”
What the sources say
- Not checked yetcites [26] 26. Gaines, B. Stochastic computing systems. Advances in Information Systems Science 1969, 2 (2), 37172. source
- Not checked yetcites [27] 27. Poppelbaum, W.; Afuso, C.; Esch, J. Stochastic computing elements and systems. Proceedings of the November 14-16, 1967, fall joint computer conference , 1967; pp 635-644. source
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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
“Rather than trying to eliminate the non-determinism to use these devices in deterministic memory applications, the ability to produce random bitstreams and multi-bit synapses using binary memristive devices based on the native stochastic switching principle may potentially lead to novel non von-Neumann, alternative computing paradigms.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“It is possible to achieve multi-bit in binary memristive devices by controlling the programming current (5), however, precise current control in a large network is extremely challenging (30).”
What the sources say
- Not checked yetcites [5] 5. Valov, I.; Waser, R.; Jameson, J. R.; Kozicki, M. N. Electrochemical metallization memoriesfundamentals, applications, prospects. Nanotechnology 2011, 22 (25), 254003. source
- Not checked yetcites [30] 30. Kim, K.-H.; Gaba, S.; Wheeler, D.; Cruz-Albrecht, J. M.; Hussain, T.; Srinivasa, N.; Lu, W. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano letters 2011, 12 (1), 389-395. source
hedgeddirect- 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
“First, we note that the switching probability can be calculated by integrating the Poisson distribution Eq. (1), which leads to”
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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Probably not a substantial claim.
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 ability to generate random bitstreams using memristive devices can represent an intriguing option for efficient computation of data intensive problems such as image processing using stochastic approaches.”
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“In particular, in these devices the filament characteristics can be controlled during programming by external circuit parameters such as series-resistance (29) and programming pulse height/width (14,17) so similar switching characteristics can be obtained in different devices.”
What the sources say
- Not checked yetcites [29] 29. Gaba, S.; Choi, S.; Sheridan, P.; Chang, T.; Yang, Y.; Lu, W. Improvement of RRAM Device Performance Through On-Chip Resistors. MRS Proceedings , 2012. source
- Not checked yetcites [14] 14. Jo, S. H.; Kim, K.-H.; Lu, W. Programmable resistance switching in nanoscale two-terminal devices. Nano letters 2009, 9 (1), 496-500. source
- Not checked yetcites [17] 17. Sheridan, P.; Kim, K.-H.; Gaba, S.; Chang, T.; Chen, L.; Lu, W. Device and SPICE modeling of RRAM devices. Nanoscale 2011, 3 (9), 3833-3840. source
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.
For stochastic computing multiplication via a bit-wise AND operation, the input bitstreams x and y are independent and completely uncorrelated.
“This operation assumes that x and y are independent bitstreams that are completely uncorrelated.”
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?
In the citing paper
“On the other hand, this natural stochasticity can be valuable for hardware security applications.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [26]
Citations
Cites
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“Devices based on resistive switching (memristive devices or memristors (2-6) have been identified as a major contender in applications ranging from nonvolatile memory storage, logic, to neuromorphic systems due to their scalability (7), low power operation (8), and large connectivity offered in a crossbar structure (9…”
BackgroundMemristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 3×
“(2) is that we can now predict the switching probability at a given programming voltage and pulse width, even though each switching event is random.”
BackgroundNanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923 · cited 3×
“Binary synapses are easier to fabricate, more robust and offer larger dynamic range (4,5).”
BackgroundMemristive devices for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885 · cited 2×
“oxygen vacancy in oxides) (6) based memristive systems as well.”
BackgroundLee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228
Observation of conducting filament growth in nanoscale resistive memories
Yang, Y., Gao, P., Gaba, S., Chang, T., Pan, X., & Lu, W. (2012) · Nature Communications · doi:10.1038/ncomms1737 · Cited by 1,208 · cited 1×
“The change from OFF to ON state is explained by the formation of a dominating Ag filament (15,16).”
BackgroundSelf-citationA scalable neuristor built with Mott memristors
Pickett, M. D., Medeiros‐Ribeiro, G., & Williams, R. S. (2012) · Nature Materials · doi:10.1038/nmat3510 · Cited by 1,046 · cited 1×
“Active circuits based on memristors have also been demonstrated recently (10,11).”
BackgroundKim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J. M., Hussain, T., Srinivasa, N., & Lu, W. (2012) · Nano Letters · doi:10.1021/nl203687n · Cited by 924 · cited 1×
“It is possible to achieve multi-bit in binary memristive devices by controlling the programming current (5), however, precise current control in a large network is extremely challenging (30).”
BackgroundSelf-citationElectrochemical metallization memories—fundamentals, applications, prospects
Valov, I., Waser, R., Jameson, J. R., & Kozicki, M. N. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/25/254003 · Cited by 921 · cited 6×
“Devices based on resistive switching (memristive devices or memristors (2-6) have been identified as a major contender in applications ranging from nonvolatile memory storage, logic, to neuromorphic systems due to their scalability (7), low power operation (8), and large connectivity offered in a crossbar structure (9…”
BackgroundInfluentialGaines, B. R. (1969) · Advances in Information Systems Science · doi:10.1007/978-1-4899-5841-9_2 · Cited by 745 · cited 1×
“Stochastic computing was first proposed in the 1960s (26,27) but like many concepts proposed ahead of their time it never become mainstream due to the exponential growth of digital computers.”
Background
Cited by
Sorted by citations
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
In-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298
Pan, F., Gao, S., Chen, C., Song, C., & Zeng, F. (2014) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2014.06.002 · Cited by 1,412
Stochastic phase-change neurons
Tůma, T., Pantazi, A., Gallo, M. L., Sebastian, A., & Eleftheriou, E. S. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.70 · Cited by 1,107
An artificial nociceptor based on a diffusive memristor
Yoon, J. H., Wang, Z., Kim, K. M., Wu, H., Ravichandran, V., Xia, Q., Hwang, C. S., & Yang, J. J. (2018) · Nature Communications · doi:10.1038/s41467-017-02572-3 · Cited by 531
Mixed-precision in-memory computing
Le Gallo, M., Sebastian, A., Mathis, R., Manica, M., Giefers, H., Tuma, T., Bekas, C., Curioni, A., & Eleftheriou, E. (2018) · Nature Electronics · doi:10.1038/s41928-018-0054-8 · Cited by 485
An overview of phase-change memory device physics
Le Gallo, M., & Sebastian, A. (2020) · Journal of Physics D: Applied Physics · doi:10.1088/1361-6463/ab7794 · Cited by 460
A novel true random number generator based on a stochastic diffusive memristor
Jiang, H., Belkin, D., Savel’ev, S. E., Lin, S., Wang, Z., Li, Y., Joshi, S., Midya, R., Li, C., Rao, M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00869-x · Cited by 459
“However, this random behavior can be helpful in stochastic computing and hardware security applications 26 – 28 .”
BackgroundChen, S., Mahmoodi, M. R., Shi, Y., Mahata, C., Yuan, B., Liang, X., Wen, C., Hui, F., Akinwande, D., Strukov, D. B., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-00473-w · Cited by 448
Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems
Vincent, A. F., Larroque, J., Locatelli, N., Ben Romdhane, N., Bichler, O., Gamrat, C., Zhao, W. S., Klein, J.-O., Galdin-Retailleau, S., & Querlioz, D. (2015) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2414423 · Cited by 360
“This stochastic effect is not caused by technological imperfections or filamentary effects like in other resistive memory technologies [22], [24], but is intrinsic to the physics of magnetic switching.”
Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications
Wang, Z., Rao, M., Midya, R., Joshi, S., Jiang, H., Lin, P., Song, W., Asapu, S., Zhuo, Y., Li, C., et al. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201704862 · Cited by 364
Nanoscale resistive switching devices: mechanisms and modeling
Yang, Y., & Lu, W. D. (2013) · Nanoscale · doi:10.1039/c3nr03472k · Cited by 271
Self-citationStandards for the Characterization of Endurance in Resistive Switching Devices
Lanza, M., Waser, R. M., Ielmini, D., Yang, J. J., Goux, L., Suñé, J., Kenyon, A. J., Mehonić, A., Spiga, S., Rana, V., et al. (2021) · ACS Nano · doi:10.1021/acsnano.1c06980 · Cited by 272
Ambrogio, S., Balatti, S., Milo, V., Carboni, R., Wang, Z.-Q., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2526647 · Cited by 256
“Statistical variations are generally detrimental to digital memory operation; however, they can be tolerated in some computing applications, such as the generation of random numbers [6]–[9] and the neuromorphic networks [10]–[12].”
BackgroundState of the art of metal oxide memristor devices
Mohammad, B. S., Jaoudé, M. A., Kumar, V., Homouz, D., Abunahla, H., Al‐Qutayri, M., & Christoforou, N. (2015) · Nanotechnology Reviews · doi:10.1515/ntrev-2015-0029 · Cited by 230
A Mini Review of Neuromorphic Architectures and Implementations
Nawrocki, R. A., Voyles, R. M., & Shaheen, S. E. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2598413 · Cited by 225
“logical spiking synapse came from a group at the University of Michigan [80], [81].”
BackgroundOn‐Demand Reconfiguration of Nanomaterials: When Electronics Meets Ionics
Lee, J., & Lu, W. D. (2017) · Advanced Materials · doi:10.1002/adma.201702770 · Cited by 197
Self-citationIelmini, D., & Pedretti, G. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00845 · Cited by 201
Biomemristors as the next generation bioelectronics
Sun, B. T., Zhou, G., Guo, T., Zhou, Y., & Wu, Y. A. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.104938 · Cited by 165
Domain wall memory: Physics, materials, and devices
Kumar, D., Jin, T., Sbiaa, R., Kläui, M., Bedanta, S., Fukami, S., Ravelosona, D., Yang, S., Liu, X., & Piramanayagam, S. N. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.02.001 · Cited by 167
Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity
Yang, Y., Chen, B., & Lu, W. D. (2015) · Advanced Materials · doi:10.1002/adma.201503202 · Cited by 156
Self-citationComputational phase-change memory: beyond von Neumann computing
Sebastian, A., Gallo, M. L., & Eleftheriou, E. S. (2019) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab37b6 · Cited by 154
Stochastic Memory Devices for Security and Computing
Carboni, R., & Ielmini, D. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900198 · Cited by 150
“[131], [171] demonstrated stochastic computing with random input spiking signals obtained by the stochastic switching of RRAM devices.”
BackgroundTrue Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices
Balatti, S., Ambrogio, S., Wang, Z., & Ielmini, D. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2426492 · Cited by 147
“Switching variability in RRAM was also used to generate random bits for stochastic computing, where any number ( e.g. , 0.4) can be represented by a sequence of random bits representing 0 and 1 ( e.g. , 60% of 0 and 40% of 1) [5,6].”
MethodInfluentialEmergent dynamics of neuromorphic nanowire networks
Álvarez, A. D., Higuchi, R., Sanz‐Leon, P., Marcus, I., Shingaya, Y., Stieg, A. Z., Gimzewski, J. K., Kuncic, Z., & Nakayama, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-51330-6 · Cited by 141
Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors
Yi, W., Savel'ev, S. E., Medeiros-Ribeiro, G., Miao, F., Zhang, M.-X., Yang, J. J., Bratkovsky, A. M., & Williams, R. S. (2016) · Nature Communications · doi:10.1038/ncomms11142 · Cited by 141
Bioinspired Programming of Memory Devices for Implementing an Inference Engine
Querlioz, D., Bichler, O., Vincent, A. F., & Gamrat, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2437616 · Cited by 142
“This effect is also seen in CBRAMs and other kinds of memory when using weak programming pulses [28], [59], [72], [73].”
BackgroundTuning Resistive Switching Characteristics of Tantalum Oxide Memristors through Si Doping
Kim, S., Choi, S., Lee, J., & Lu, W. D. (2014) · ACS Nano · doi:10.1021/nn503464q · Cited by 129
“With larger hopping distance, the stochastic properties of ion hopping will be more pronounced since the stochastic movement of just a few ions may already cause significant resistance changes which in turn affect the dynamic CF growth and dissolution processes, causing variations in the CF shape and resistance variat…”
BackgroundSelf-citationMemristors Empower Spiking Neurons With Stochasticity
Al-Shedivat, M., Naous, R., Cauwenberghs, G., & Salama, K. N. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2435512 · Cited by 127
“Following the experimental findings in [30], we assume that the memristors switch stochastically.”
BackgroundMethodInfluentialPhysical Unbiased Generation of Random Numbers With Coupled Resistive Switching Devices
Balatti, S., Ambrogio, S., Carboni, R., Milo, V., Wang, Z., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2537792 · Cited by 127
“…physical fluctuation phenomena are, therefore, under scrutiny for efficient and stable RNG. Recently, novel RNG concepts based on the switching variability in low-voltage memory technologies, such as spin-transfer-torque (STT), magnetic memory (MRAM) [6], [7], and RRAM [8], [9], have been proposed.”
BackgroundKim, G., In, J. H., Kim, Y. S., Rhee, H., Park, W., Song, H., Park, J., & Kim, K. M. (2021) · Nature Communications · doi:10.1038/s41467-021-23184-y · Cited by 126
Neuromorphic Computing with Memristor Crossbar
Zhang, X., Huang, A., Hu, Q., Xiao, Z., & Chu, P. K.-H. (2018) · physica status solidi (a) · doi:10.1002/pssa.201700875 · Cited by 126
Multilevel Ultrafast Flexible Nanoscale Nonvolatile Hybrid Graphene Oxide–Titanium Oxide Memories
Nagareddy, V. K., Barnes, M. D., Zipoli, F., Lai, K. T., Alexeev, A. M., Craciun, M. F., & Wright, C. D. (2017) · ACS Nano · doi:10.1021/acsnano.6b08668 · Cited by 122
Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device
Ilyas, N., Li, D., Li, C., Jiang, X., Jiang, Y., & Li, W. (2020) · Nanoscale Research Letters · doi:10.1186/s11671-020-3249-7 · Cited by 114
A Native Stochastic Computing Architecture Enabled by Memristors
Knag, P. C., Lu, W. D., & Zhang, Z. (2014) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2014.2300342 · Cited by 107
“9(a) and the basic concept was recently demonstrated [46].”
BackgroundInfluentialSelf-citationWijesinghe, P., Ankit, A., Sengupta, A., & Roy, K. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2829924 · Cited by 113
“Despite these benefits, high programming voltages and long pulse durations, [18] or other feedback write mechanisms [19]”
BackgroundInfluentialMemristive Synapses for Brain‐Inspired Computing
Wang, J., & Zhuge, F. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201800544 · Cited by 108
All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron
Zhang, D., Zhao, W. S., Zeng, L., Cao, K., Wang, M., Peng, S., Zhang, Y., Zhang, Y., Klein, J., & Wang, Y. (2016) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2533798 · Cited by 105
“emerging post-CMOS non-volatile memory (NVM) technologies such as resistive RAM (RRAM) and spin transfer torque magnetic RAM (STT-MRAM) with low power dissipation and high density in the form of crossbar [8]–[26].”
BackgroundA Data-Driven Verilog-A ReRAM Model
Messaris, I., Serb, A., Stathopoulos, S., Khiat, A., Nikolaidis, S., & Prodromakis, T. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2791468 · Cited by 106
“2791468 probabilistic fashion [6], while others can have their resistive”
BackgroundA compound memristive synapse model for statistical learning through STDP in spiking neural networks
Bill, J., & Legenstein, R. A. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00412 · Cited by 108
“Stochastic switching was demonstrated for both directions (active ↔ inactive) (Suri et al., 2013) with switching probabilities being adjustable via the duration (Gaba et al., 2013; Suri et al., 2013) and amplitude (Jo et al., 2009b; Gaba et al., 2013) of the voltage applied across the terminals.”
BackgroundMethodInfluentialMahmoodi, M. R., Prezioso, M., & Strukov, D. B. (2019) · Nature Communications · doi:10.1038/s41467-019-13103-7 · Cited by 102
Stochasticity Modeling in Memristors
Naous, R., Al-Shedivat, M., & Salama, K. N. (2015) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2015.2493960 · Cited by 101
A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems
Chicca, E., & Indiveri, G. (2020) · Applied Physics Letters · doi:10.1063/1.5142089 · Cited by 100
A multiply-add engine with monolithically integrated 3D memristor crossbar/CMOS hybrid circuit
Chakrabarti, B., Lastras-Montaño, M. Á., Adam, G. C., Prezioso, M., Hoskins, B. D., Payvand, M., Madhavan, A., Ghofrani, H. A., Theogarajan, L. S., Cheng, K.-T. T., et al. (2017) · Scientific Reports · doi:10.1038/srep42429 · Cited by 100
Resistive switching and conductance quantization in Ag/SiO2/indium tin oxide resistive memories
Gao, S., Chen, C., Zhai, Z., Liu, H. Y., Lin, Y., Li, S. Z., Lu, S., Wang, G. Y., Song, C., Zeng, F., et al. (2014) · Applied Physics Letters · doi:10.1063/1.4893277 · Cited by 99
Truly Electroforming‐Free and Low‐Energy Memristors with Preconditioned Conductive Tunneling Paths
Yoon, J. H., Zhang, J., Ren, X., Wang, Z., Wu, H., Li, Z., Barnell, M., Wu, Q., Lauhon, L. J., Xia, Q., et al. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201702010 · Cited by 95
A Low‐Current and Analog Memristor with Ru as Mobile Species
Yoon, J. H., Zhang, J., Lin, P., Upadhyay, N. K., Yan, P., Liu, Y., Xia, Q., & Yang, J. J. (2020) · Advanced Materials · doi:10.1002/adma.201904599 · Cited by 96
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
Xin, W., Saxena, V., & Zhu, K. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2433552 · Cited by 91
Payvand, M., Nair, M. V., Müller, L. K., & Indiveri, G. (2018) · Faraday Discussions · doi:10.1039/c8fd00114f · Cited by 88
Design of a Controllable Redox‐Diffusive Threshold Switching Memristor
Sun, Y., Song, C., Yin, S., Qiao, L., Wan, Q., Wang, R., Zeng, F., & Pan, F. (2020) · Advanced Electronic Materials · doi:10.1002/aelm.202000695 · Cited by 88
Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
Frascaroli, J., Brivio, S., Covi, E., & Spiga, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-25376-x · Cited by 83
“The logarithm of the switching time follows a straight line implying an exponential relation between Δ V and Δ t 32 .”
BackgroundReconfigurable Memristive Device Technologies
Edwards, A. H., Barnaby, H., Campbell, K. A., Kozicki, M. N., Liu, W., & Marinella, M. J. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2441752 · Cited by 82
“Ion conducting devices have been studied in such applications as neural networks [103] and computing [104], and in threshold logic [105].”
BackgroundFeature Extraction Using Memristor Networks
Sheridan, P. M., Du, C., & Lu, W. D. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2482220 · Cited by 79
“The resistance switching process is strongly affected by the local field and oxygen vacancy profile and shows significant device-to-device and cycle-to-cycle variations [23].”
BackgroundSelf-citationNovel charm of 2D materials engineering in memristor: when electronics encounter layered morphology
Batool, S. A., Idrees, M., Zhang, S., Han, S., & Zhou, Y. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00031h · Cited by 79
Neuromorphic learning, working memory, and metaplasticity in nanowire networks
Loeffler, A., Álvarez, A. D., Zhu, R., Ganesh, N., Shine, J. M., Nakayama, T., & Kuncic, Z. (2023) · Science Advances · doi:10.1126/sciadv.adg3289 · Cited by 81
Stochastic Electronics: A Neuro-Inspired Design Paradigm for Integrated Circuits
Hamilton, T. J., Afshar, S., André, V. S., & Tapson, J. C. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2310713 · Cited by 73
In-memory computing with emerging nonvolatile memory devices
Cheng, C., Tiw, P. J., Cai, Y., Yan, X., Yang, Y., & Huang, R. (2021) · Science China Information Sciences · doi:10.1007/s11432-021-3327-7 · Cited by 73
“The high efficiency of the unipolar devices on the realization of the XNOR function makes it possible to construct novel encryption hardware, in which the encryption keys are generated according to the inherent randomness of HRS to ensure information security in a simple and effective method [245,246].”
BackgroundA Memristor SPICE Model Accounting for Volatile Characteristics of Practical ReRAM
Berdan, R., Lim, C. K. K., Khiat, A., Papavassiliou, C., & Prodromakis, T. (2014) · IEEE Electron Device Letters · doi:10.1109/led.2013.2291158 · Cited by 71
“non interrupted single stimulus or sequence of stimuli, exploiting the devices’ accumulating nature [12].”
BackgroundA memristive spiking neuron with firing rate coding
Ignatov, M., Ziegler, M., Hansen, M., Petraru, A., & Kohlstedt, H. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00376 · Cited by 74
“We would like to mention that the resistive switching process in the used Ag/TiO 2 − x /Al memristive cells has an inherent stochastic nature (Gaba et al., 2013), where the needed number of voltage pulses to set the device resistance depends on local Ag migration processes.”
BackgroundHardware implementation of Bayesian network based on two-dimensional memtransistors
Zheng, Y., Ravichandran, H., Schranghamer, T. F., Trainor, N., Redwing, J. M., & Das, S. (2022) · Nature Communications · doi:10.1038/s41467-022-33053-x · Cited by 70
“So far, probabilistic CMOS 8 , fi eld-programmable gate arrays (FPGAs) 9 – 11 , memristors 12 – 14 , and spintronic devices 15 – 21 have been successfully used for BN implementation.”
In-memory computing to break the memory wall*
Huang, X., Liu, C., Jiang, Y., & Zhou, P. (2020) · Chinese Physics B · doi:10.1088/1674-1056/ab90e7 · Cited by 71
Shamsi, J., Mohammadi, K., & Shokouhi, S. B. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2815025 · Cited by 70
“candidate for neuromorphic computing, the memristor crossbar suffers from several drawbacks such as process variation [20], [35]–[43], SAFs [44], sneak paths [45], and IR Fig.”
BackgroundMemristive devices for stochastic computing
Gaba, S., Knag, P. C., Zhang, Z., & Lu, W. D. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865703 · Cited by 66
Self-citationWoo, K. S., Wang, Y., Kim, J., Kim, Y., Kwon, Y. J., Yoon, J. H., Kim, W., & Hwang, C. S. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800543 · Cited by 64
Ignatov, M., Ziegler, M., Hansen, M., & Kohlstedt, H. (2017) · Science Advances · doi:10.1126/sciadv.1700849 · Cited by 64
True Random Number Generation Using Read Noise of Flash Memory Cells
Ray, B., & Milenković, A. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2792436 · Cited by 64
Development of Ag/ZnO/FTO thin film memristor using aqueous chemical route
Dongale, T. D., Khot, K. V., Mali, S. S., Patil, P. S., Gaikwad, P. K., Kamat, R. K., & Bhosale, P. N. (2015) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2015.07.004 · Cited by 63
High-speed true random number generation based on paired memristors for security electronics
Zhang, T., Yin, M., Xu, C., Lu, X., Sun, X., Yang, Y., & Huang, R. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa8b3a · Cited by 63
“Previous random number generation based on memristors typically exploits stochastic switching behavior of the devices that fundamentally originate from randomness in the switching mechanism [6], that is, stochastic ion migration [15] as well as formation and dissolution of nanoscale conducting filaments [16–18].”
BackgroundMemristive computing devices and applications
Zidan, M. A., Chen, A., Indiveri, G., & Lu, W. D. (2017) · Journal of Electroceramics · doi:10.1007/s10832-017-0103-0 · Cited by 65
“Device runtime stochasticity, often encountered in aggressively scaled memristive devices, can be even used as a useful property [14, 103, 104].”
BackgroundSelf-citationHow to Build a Memristive Integrate-and-Fire Model for Spiking Neuronal Signal Generation
Kang, S. M., Choi, D., Eshraghian, J. K., Zhou, P., Kim, J., Kong, B., Zhu, X., Demirkol, A. Ş., Ascoli, A., Tetzlaff, R., et al. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3126555 · Cited by 62
“(c) An action potential waveform showing rest, threshold and reset potentials. discretely packaged memristors [29], [30].”
BackgroundSelf-citationDastgeer, G., Afzal, A. M., Aziz, J., Hussain, S., Jaffery, S. H. A., Kim, D., Imran, M., & Assiri, M. A. (2021) · Materials · doi:10.3390/ma14247535 · Cited by 59
Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic Computing
Huang, P. C., Zhu, D., Chen, S., Zhou, Z., Chen, Z., Gao, B., Liu, L., Liu, X., & Kang, J. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2643162 · Cited by 57
“two electrodes and the device will be switched to LRS with probability P after a positive voltage spike pulse [36].”
BackgroundIsah, A., & Bilbault, J. (2022) · Journal of Low Power Electronics and Applications · doi:10.3390/jlpea12030044 · Cited by 57
Synchronization of two memristively coupled van der Pol oscillators
Ignatov, M., Hansen, M., Ziegler, M., & Kohlstedt, H. (2016) · Applied Physics Letters · doi:10.1063/1.4942832 · Cited by 56
A Novel True Random Number Generator Design Leveraging Emerging Memristor Technology
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“Moreover, the memristive behaviors in various memristive devices have been thoroughly investigated, in which the stochastic processes have been clearly demonstrated [12][13].”
BackgroundAziza, H., Postel-Pellerin, J., Bazzi, H., Canet, P., Moreau, M., della Marca, V., & Harb, A. (2020) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2020.2976735 · Cited by 57
“Over the last few years there has been a lot of activity across research groups proposing efficient TRNG based on resistiveswitching devices [4-6].”
BackgroundVariability and Energy Consumption Tradeoffs in Multilevel Programming of RRAM Arrays
Pérez, E., Mahadevaiah, M. K., Quesada, E. P.-B., & Wenger, C. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3072868 · Cited by 55
Stochastic model of memristor based on the length of conductive region
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Bayesian Inference With Muller C-Elements
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“This estimate ignores the overhead associated with random number generation, which may be efficiently achieved with emerging nanodevices [16], [20], [21] and will therefore become essential”
BackgroundA resistive switching memory device with a negative differential resistance at room temperature
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Memristor‐Based Efficient In‐Memory Logic for Cryptologic and Arithmetic Applications
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Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit
Rhee, H., Kim, G., Song, H., Park, W., Kim, D. H., In, J. H., Lee, Y., & Kim, K. M. (2023) · Nature Communications · doi:10.1038/s41467-023-43085-6 · Cited by 52
“Moreover,differingfromconventionalpbits 9,21,22 ,suchoscillator-based pbits can generate a self-sustaining bitstream without bit-generating signal pulses.”
Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing
Dang, B., Liu, K., Zhu, J., Xu, L., Zhang, T., Cheng, C., Wang, H., Yang, Y., Hao, Y., & Huang, R. (2019) · APL Materials · doi:10.1063/1.5109090 · Cited by 50
Spin-transfer torque magnetic memory as a stochastic memristive synapse
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“memory technologies [18], [20], but is intrinsic to the physics of magnetic switching.”
Neuromorphic computing with memristive devices
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Self-citationRecent trends in hardware security exploiting hybrid CMOS-resistive memory circuits
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Hardware Acceleration of Simulated Annealing of Spin Glass by RRAM Crossbar Array
Shin, J. H., Jeong, Y. J., Zidan, M. A., Wang, Q., & Lu, W. D. (2018) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2018.8614698 · Cited by 44
Self-citationMilo, V., Pedretti, G., Carboni, R., Calderoni, A., Ramaswamy, N., Ambrogio, S., & Ielmini, D. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2818978 · Cited by 44
“, 1/ f noise [35] or random telegraph noise [36], or by random set processes in RRAM devices [37], [38].”
BackgroundInherent stochasticity during insulator–metal transition in VO 2
Cheng, S., Lee, M., Tran, R., Shi, Y., Li, X., Navarro, H., Adda, C., Meng, Q., Chen, L., Dynes, R. C., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105895118 · Cited by 43
“Instead of using the excess voltage (much higher than Vth) to achieve large switching probabilities, we could take advantage of this inherent stochasticity and use moderate external stimuli to trigger these binary (on and off states) devices, which can be used as the fundamental elements for error-tolerant neuromorphi…”
BackgroundMemristor-based neural networks: Synaptic versus neuronal stochasticity
Naous, R., Al-Shedivat, M., Neftci, E., Cauwenberghs, G., & Salama, K. N. (2016) · AIP Advances · doi:10.1063/1.4967352 · Cited by 42
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Lammie, C., Eshraghian, J. K., Lu, W. D., & Azghadi, M. R. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3065932 · Cited by 41
“Distribution of switching time of a fabricated CBRAM device [14] under an applied voltage of 4.”
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Self-organization of an inhomogeneous memristive hardware for sequence learning
Payvand, M., Moro, F., Nomura, K., Dalgaty, T., Vianello, E., Nishi, Y., & Indiveri, G. (2022) · Nature Communications · doi:10.1038/s41467-022-33476-6 · Cited by 42
Memristive Non-Idealities: Is there any Practical Implications for Designing Neural Network Chips?
Krestinskaya, O., Irmanova, A., & James, A. P. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702245 · Cited by 38
“and this phenomena [3], [4], [5], [6] is recently being investigated for different small scale architectures [7].”
BackgroundHighly tunable β-relaxation enables the tailoring of crystallization in phase-change materials
Cheng, Y., Yang, Q., Wang, J., Dimitriadis, T., Schumacher, M., Zhang, H., Müller, M. J., Amini, N., Yang, F., Schoekel, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35005-x · Cited by 38
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“Furthermore, one can see from Figure 1d that there exists high variation in the resistance of the HRS and the threshold voltages during the cyclic voltage sweeping, which is in agreement with the intrinsic stochasticity during ionic resistive/threshold switching [18], [20], [21].”
BackgroundMagnetic domain walls: types, processes and applications
Venkat, G., Allwood, D. A., & Hayward, T. J. (2023) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ad0568 · Cited by 36
More Efficient Testing of Metal-Oxide Memristor–Based Memory
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“write operations [16], [17], and such variability must be taken into consideration when testing for manufacturing defects.”
BackgroundInfluentialCampbell, K. A., Drake, K., & Smith, E. H. B. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00097 · Cited by 35
“Bioinspired neuromorphic computing has the potential of becoming realizable through the application of memristors (Chua, 2015) as artificial synapses (Jo et al., 2010; Chang et al., 2011; Erokhin and Fontana, 2011; Rose et al., 2011a,b; Gaba et al., 2013; Serrano-Gotarredona et al., 2013a; Subramaniam et al., 2013; Th…”
BackgroundYan, X., Ma, J., Wu, T., Zhang, A., Wu, J., Chin, M. L., Zhang, Z., Dubey, M., Wu, W., Chen, M. S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26012-5 · Cited by 36
Metadata
- Authors
- Gaba, S., Sheridan, P., Zhou, J., Choi, S., & Lu, W.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Lu, Wei
- Venue
- Nanoscale
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 5
- Issue
- 13
- Pages
- 5872
- ISSN
- 2040-3364 (ISSN-L)
- 2040-3372
- OpenAlex
- W2148267286
- Semantic Scholar
- 3f0d761f6dabea7d51ed9a69e9cde6865122afd9
- Cited by
- 307
- References
- 35
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 14 · 2023: 22 · 2022: 24 · 2021: 28 · 2020: 32 · 2019: 30 · 2018: 27 · 2017: 29 · 2016: 19 · 2015: 25 · 2014: 16 · 2013: 3
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Nanoscale (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Phase-change materials and chalcogenides
- Keywords
- memristors, resistive switching, stochastic switching, stochastic computing, neuromorphic computing, error-tolerant computing, memristor arrays, non-volatile memory
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