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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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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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
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828 · cited 1×
“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
Items 201–300 of 307
Modeling Networks of Probabilistic Memristors in SPICE
Dowling, V. J., Slipko, V. A., & Pershin, Y. V. (2021) · Radioengineering · doi:10.13164/re.2021.0157 · Cited by 8
A Stochastic Compact Model Describing Memristor Plasticity and Volatility
Malik, A., Papavassiliou, C., & Stathopoulos, S. (2021) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs53924.2021.9665591 · Cited by 8
Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms
Barrows, F. P., Lin, J., Caravelli, F., & Chialvo, D. R. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400739 · Cited by 7
Theory of Heterogeneous Circuits With Stochastic Memristive Devices
Slipko, V. A., & Pershin, Y. V. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3091437 · Cited by 7
Effect of Device Mismatches in Differential Oscillatory Neural Networks
Shamsi, J., Avedillo, M. J., Linares-Barranco, B., & Serrano‐Gotarredona, T. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3221540 · Cited by 8
“On the other hand, memristors also suffer from device-to-device and cycle-tocycle variations [9], [10].”
BackgroundGautam, M. K., Kumar, S., Chaudhary, S., Hindoliya, L. K., Kumbhar, D. D., Park, J. H., Htay, M. T., & Mukherjee, S. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00598 · Cited by 7
Blooming and pruning: learning from mistakes with memristive synapses
Nikiruy, K. E., Pérez, E., Baroni, A., Reddy, K. D. S., Pechmann, S., Wenger, C., & Ziegler, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-57660-4 · Cited by 7
“This includes the intrinsic stochastic of the memristive devices and their integration into a suitable network computing scheme 3,21,22 .”
BackgroundWoods, W., Bürger, J., & Teuscher, C. (2014) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1145/2770287.2770292 · Cited by 7
“Stochastic switching was discussed in [4] and is closely related to cycleto-cycle variation of memristive devices.”
InfluentialApplications of Resistive Switching Memory as Hardware Security Primitive
Carboni, R., & Ielmini, D. (2019) · Springer series in advanced microelectronics · doi:10.1007/978-981-13-8379-3_4 · Cited by 7
“8d highlights that, although the single switching event is stochastic, the overall distribution of switching times can be predicted and controlled by the applied voltage [69, 68].”
BackgroundEnergy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications
Canales-Verdial, J. I., Wagner, J. R., Schmucker, L. A., Wetzel, M., Proctor, P., Carson, M., Meng, J., Withers, N. J., Harris, C. T., Nogan, J. J., et al. (2024) · Sensors · doi:10.3390/s24072144 · Cited by 6
Fu, S., Gao, H., Wang, S., Wang, X., Woodard, T. L., Wang, Z., Kong, J., Lovley, D. R., & Yao, J. (2025) · Nature Communications · doi:10.1038/s41467-025-63640-7 · Cited by 6
“Stochastic variations in both fi ring frequency andamplitudewereobserved,duetotheinherentstochasticdynamics of the memristor 51 .”
Dowling, V. J., & Pershin, Y. V. (2022) · Physical review. E · doi:10.1103/physreve.106.054156 · Cited by 6
“Under a constant voltage, the probability to switch follows the distribution [21, 22]”
BackgroundA Data-Driven Stochastic Memristor Model for Integrated Circuit Simulation
Xie, L., Shen, J., Feng, P., Mifsud, A., Malik, A., Nassibi, A., Manatchinapisit, V., & Papavassiliou, C. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3485584 · Cited by 6
“However, programming stochasticity, stemming from the stochastic nature of filament formation/rupture during mem-ristor programming and resulting in uncertain memristive effects [21], is excessively ignored from the modeling perspective.”
Granular memristors with tunable stochasticity
Ghosh, U., Bhaumik, A., Vasudeva, N., & Pandey, A. (2024) · Nanoscale · doi:10.1039/d4nr02899f · Cited by 6
Probabilistic model of resistance jumps in memristive devices
Slipko, V. A., & Pershin, Y. V. (2023) · Physical review. E · doi:10.1103/physreve.107.064117 · Cited by 6
Bio-Inspired Information Pathways
Ziegler, M., Mussenbrock, T., & Kohlstedt, H. (2023) · Springer series on bio- and neurosystems · doi:10.1007/978-3-031-36705-2 · Cited by 7
Maximizing the Number of Threshold Logic Functions Using Resistive Memory
Mozaffari, S. N., & Tragoudas, S. (2018) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2018.2822285 · Cited by 6
“The difference between the application time of the write pulse and the time that the device starts to switch is called the wait (switching) time [35]–[37].”
BackgroundIntegrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices
Marukame, T., Sugino, J., Kitamura, T., Takahashi, K., Tamura, Y., Nomura, K., Mizushima, K., & Nishi, Y. (2020) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ab8164 · Cited by 6
Modeling-Based Design of Memristive Devices for Brain-Inspired Computing
Zhao, Y., Chen, R., Huang, P. C., & Kang, J. (2021) · Frontiers in Nanotechnology · doi:10.3389/fnano.2021.654418 · Cited by 7
“…computing, which emulates the generation of neural spikes processed by the human brain in the form of long sequences of noisy voltage spikes (Gaba et al., 2013; Suri et al., 2013; Knag et al., 2014; Moons and Verhelst, 2014; Ielmini and Wong, 2018; Wang et al., 2018; Carboni and Ielmini,…”
BackgroundInfluentialA Variability‐Aware Behavioral Model of Monolayer MoS2 RRAM for Tunable Stochastic Sources
Peddaboina, L., Agrawal, K., Kumar, P., Hegde, G., Badami, O., & Bhattacharjee, S. (2025) · Advanced Theory and Simulations · doi:10.1002/adts.202401235 · Cited by 5
Analog-Tunable Nonvolatile Regulator-Free Memristors for Neuromorphic Controlling
Ki, S. J., Lee, S., An, X., Kim, J., Chen, M., & Liang, X. (2026) · ACS Nano · doi:10.1021/acsnano.5c16447 · Cited by 5
Saraswat, V., & Ganguly, U. (2021) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac408a · Cited by 5
“These devices are natural noise generators which has found numerous applications from security to stochastic neural networks for optimisation 15,16 .”
BackgroundAn Energy-Efficient Solid-State Organic Device Array for Neuromorphic Computing
Hu, L., Fattori, M., Schilp, W., Verbeek, R., Kazemzadeh, S., van de Burgt, Y., Kronemeijer, A. J., Gelinck, G. H., & Cantatore, E. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3327947 · Cited by 6
“This, together with the use of NODes, which do not suffer from stochastic switching like memristors [11], enabled the programming of the crossbar array without the use of complex online feedback schemes, typical to two-terminal neuromorphic devices such as memristors [9].”
2D MoS2 monolayers integration with metal oxide-based artificial synapses
Gautam, M. K., Kumar, S., Rani, S., Zeimpekis, I., & Georgiadou, D. G. (2024) · Frontiers in Nanotechnology · doi:10.3389/fnano.2024.1400666 · Cited by 5
Vector multiplications using memristive devices and applications thereof
Zidan, M. A., & Lu, W. D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00009-5 · Cited by 5
Self-citationA Secure Integrity Checking System for Nanoelectronic Resistive RAM
Majumder, M. B., Hasan, M. S., Uddin, M., & Rose, G. S. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2876693 · Cited by 5
“A similar two-phase write method is applied to write the reserved row which also leverages stochasticity in memristor switching [34], [35].”
MethodMidya, R., Wang, Z., Rao, M., Upadhyay, N. K., & Yang, J. J. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102584-0.00015-2 · Cited by 6
Ferroelectric FET-Based Bayesian Inference Engine for Disease Diagnosis
Chakraborty, A., Rafiq, M., Zarkob, Y. H., Chauhan, Y. S., & Sahay, S. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3533044 · Cited by 4
“Recently, it was shown in [8] that Bayesian inference can be performed using a network of Muller C-elements [9] even in the presence of autocorrelation in the input bit-streams and temporal variations in the emerging devices [6], [10], [11], [12], [13].”
Hardware Implementation of Bayesian Decision‐Making with Memristors
Song, L., Liu, P., Liu, Y., Pei, J., Cui, W., Liu, S., Wen, Y., Ma, T., Pun, K., Ng, L. W. T., et al. (2025) · Advanced Electronic Materials · doi:10.1002/aelm.202500134 · Cited by 4
Controlling Memristive Switching Behavior of Sr2TiO4 Thin Films by Miscut Nb:SrTiO3 Substrate
Kurnia, F., & Nagarajan, V. (2024) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.4c01238 · Cited by 4
Stochastic CBRAM-Based Neuromorphic Time Series Prediction System
Merkel, C. E., Kudithipudi, D., Suri, M., & Wysocki, B. T. (2017) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/2996193 · Cited by 4
Noisy neuromorphic neurons with RPG on-chip noise source
Yue, K., & Parker, A. C. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965992 · Cited by 4
“Moreover, the true randomness of the memristor, which shows a logarithm distribution in resistance, is insufficient for modeling neural noise, which has continuous variation in amplitude or frequency [6].”
BackgroundPerformance improvement of chip-level CMOS-integrated ReRAM cells through material optimization
Shahrabi, E., LaGrange, T., Demirci, T., & Leblebici, Y. (2019) · Microelectronic Engineering · doi:10.1016/j.mee.2019.04.018 · Cited by 4
Brownlee, B. J., Tsui, L., Vempati, K., Plumley, J. B., Iverson, B. D., Peng, T. L., & Garzón, F. H. (2020) · Journal of Applied Physics · doi:10.1063/5.0004120 · Cited by 4
Impact of Memristor Defects in a Neuromorphic Radionuclide Identification System
Canales-Verdial, J. I., Woods, W., Teuscher, C., Osiński, M., & Zarkesh-Ha, P. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180669 · Cited by 4
Li, Y., Xie, L., Zheng, C., Yu, D., & Eshraghian, J. K. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0124793 · Cited by 4
A Nonvolatile Bipolar Optical Fiber Memory Based on Phase Change Materials
Sun, J., Li, H., Qin, Y., Cheng, S., Mou, J., Gao, J., Zhang, Y. X., & Liu, Z. (2023) · ACS Photonics · doi:10.1021/acsphotonics.3c01022 · Cited by 4
Marukame, T., Sugino, J., Kitamura, T., Ishikawa, K., Takahashi, K., Tamura, Y., Berdan, R., Nomura, K., & Nishi, Y. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702643 · Cited by 4
“Memristive arrays tend to show nonlinearity [30, 31], even though they are well-tuned; therefore, other approaches should be studied.”
BackgroundEnhanced memristor‐based MNNs performance on noisy dataset resulting from memristive stochasticity
Wu, K., & Wang, X. (2019) · IET Circuits Devices & Systems · doi:10.1049/iet-cds.2018.5532 · Cited by 4
“There are several other applications using the stochastic switching behaviour of memristors, such as stochastic computing [11, 19] and random number generator [14, 20].”
MethodElectronically controllable neuristor based logic gates and their applications
Tozlu, Ö. F., Kaçar, F., & Babacan, Y. (2021) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2021.153834 · Cited by 4
Stochastic resistive switching in memristors as physically unclonable functions
Beak, C., Park, S., Park, J. H., Park, H., & Lee, S. (2025) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ae09b8 · Cited by 5
Integration of Low‐Voltage Nanoscale MoS 2 Memristors on CMOS Microchips
Lee, J., Cruces, S., Liu, X., Chen, Y., Maroufidis, V. A., Jo, J., Völkel, L., Braun, D., Möller, M., Brackmann, L., et al. (2026) · Advanced Functional Materials · doi:10.1002/adfm.202527644 · Cited by 3
Guarcello, C., Dubkov, A. A., Valenti, D., & Spagnolo, B. (2026) · Quantum Economics and Finance · doi:10.1177/29767032261446933 · Cited by 3
“…of resistive switching has been widely observed and analyzed in the literature (Lee et al. 2015; Frick et al. 2022; Stotland and Di Ventra 2012; Gaba et al. 2013; Naous et al. 2016; Guarcello et al. 2017a; Filatov et al. 2019; Ntinas et al. 2019; Naous et al. 2021; Ntinas et al. 2021; Wang et…”
Noisy neuromorphic circuit modeling Obsessive Compulsive Disorder
Barzegarjalali, S., Yue, K., & Parker, A. C. (2016) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc.2016.7905502 · Cited by 3
Exploiting the stochasticity of memristive devices for computing
Mizrahi, F. A., Laurent, R., Grollier, J., & Querlioz, D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00011-3 · Cited by 3
A Memristive True Random Number Generator
Malik, A., & Papavassiliou, C. (2023) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs58634.2023.10382731 · Cited by 3
“The physical mechanism behind this stochasticity, is predominately attributed to interface dynamics [9] at the material junctions, which depend on the modulation of charge carrier distributions and the existence of traps in the device structure [10].”
BackgroundMemristor Device Engineering and CMOS Integration for Reconfigurable Logic Applications
Xia, Q. (2013) · Springer eBooks · doi:10.1007/978-1-4614-9068-5_11 · Cited by 3
“For logic computing purposes, some of the nonuniformity issues can be addressed by adopting new computing algorithms, such as statistical computing [19] that uses the stochastic nature of the memristive devices and use a bit stream from several devices rather than one to represent the logic state.”
BackgroundConstruction of a Bit Stream Using Telegraphic Switching of a Two-Input Magnetic Tunnel Junction
Bae, G. Y., Suh, D. I., Kim, Y., Lee, H., & Park, W. (2017) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2017.2712821 · Cited by 3
“For this stochastic computing architecture, the introduction of an electronic element with an inherent stochastic nature is critical to reduce computational costs, since randomization of the bit streams requires considerable hardware usage for stochastic computing [8], [9].”
BackgroundA Circuit Implementation of Random Number Generator Utilizing Memristor Stochasticity
Guo, Y., Wu, Q., & Wang, X. (2021) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci52617.2021.9435904 · Cited by 4
“Never-theless, the stochasticity of memristor can be of interest in other applications such as stochastic computing or information security [4], [5], [6].”
BackgroundRRAM fabric for neuromorphic and reconfigurable compute-in-memory systems
Zidan, M. A., & Lu, W. D. (2018) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2018.8357066 · Cited by 3
Self-citationSynthesis of memristive one-ports with piecewise-smooth characteristics
Семенов, В. В. (2022) · International Journal of Electronics Letters · doi:10.1080/21681724.2022.2148286 · Cited by 3
“…for a broad spectrum of applications including the development of next-generation memory and computing systems (Di Ventra and Pershin (2013), Gaba et al. (2013), Ielmini and Waser (2016), Pershin and Di Ventra (2012), and Tetzlaff (2014), and Vourkas and Sirakoulis (2016), and Wang et al.…”
BackgroundPal, M., Yadav, B., Mondal, I., Kaur, M., Vidhyadhiraja, N. S., & Kulkarni, G. U. (2025) · Nanoscale · doi:10.1039/d5nr01617g · Cited by 2
Designing Asymmetric Memristive Behavior in Proton Mixed Conductors for Neuromorphic Applications
Besisa, N. H. A., Tsuji, M., Furue, Y., Geng, Y., Pati, S. P., Hamasuna, S., & Yajima, T. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202525988 · Cited by 2
Park, S., Na, H., Choi, J., Ismail, M., Mahata, C., Ryu, D., Kim, S., Lee, J., Yu, J., & Kim, S. (2025) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2025.11.025 · Cited by 2
Electrochemical Metallization Memristors: From Bulk to 2D Materials
Lopes, V., Bae, Y., Araújo, G., Schulman, A., Abreu, T., & Capasso, A. (2026) · Advanced Functional Materials · doi:10.1002/adfm.76117 · Cited by 2
“However, despite their potential, bulk devices face challenges such as scalability, switching unpredictability, and energy efficiency [ 28–31 ].”
Fida, A. A., & Mittal, S. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad9a3e · Cited by 2
Advantages of imperfect dice rolls over coin flips for random number generation
Pfeffer, D. T., Allemang, C., Misra, S., Severa, W., & Smith, J. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-96492-8 · Cited by 2
A compact model of stochastic switching in STT magnetic RAM for memory and computing
Carboni, R., Vernocchi, E., Siddik, M., Harms, J., Lyle, A., Sandhu, G. S., & Ielmini, D. (2019) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1109/nanoarch47378.2019.181290 · Cited by 2
“To support the model for circuit design and simulation, we show stochastic computing primitives for analogue multiplication [10] and spiking neural network with stochastic neurons [11].”
MethodRRAM fabric for neuromorphic and reconfigurable compute-in-memory systems
Zidan, M. A., & Lu, W. D. (2019) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2019.8780185 · Cited by 2
“Here the native stochasticity during resistive switching in RRAM can be used to provide the needed stochastic bitstreams with a controlled bias [56].”
MethodSelf-citationMemristive Computing Devices and Applications
Zidan, M. A., Chen, A., Indiveri, G., & Lu, W. D. (2021) · Kluwer international series in electronic materials: science and technology · doi:10.1007/978-3-030-42424-4_2 · Cited by 2
Self-citationMTJ-based random number generation and its application in SNN handwritten digits recognition
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A Lightweight CNN Based on Memristive Stochastic Computing for Electronic Nose
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Emulating homoeostatic effects with metal-oxide memristors T-dependencePreprint
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“CMOS [7, 8], memristor [9-11] and spintronics [12-14] based SC architectures have already been demonstrated in the past.”
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Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing
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Memristor Modeling -- Static, Statistical, and Stochastic Methodologies
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“metal oxide memristors [9, 10] for the same stochastic nature, that is, the percolation property of the thin dielectric soft breakdown [17].”
BackgroundMethodA Variation-Aware CMOS Platform for Multi-Level Memristor Characterisation
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“However, achieving multi-level programming poses challenges due to the intrinsic randomness of memristors [8].”
BackgroundQualitative SPICE modeling accounting for volatile dynamics of TiO2 memristors
Berdan, R., Khiat, A., Papavassiliou, C., & Prodromakis, T. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865564 · Cited by 1
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“4 It is difficult to simulate electronic circuits with stochastic components as the behavior of such circuits, in general, is not deterministic.”
BackgroundNeuromorphic applications using MOx-based memristors
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“Novel analog, digital, and hybrid neuromorphic design paradigm being developing at the system level to implement memristor-based brain-inspired computation [1, 5, 7-12].”
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“Switching events in memristors seem to follow a stochastic behavior rather than being deterministic, as being observed both in RESET [63] and SET processes [64].”
BackgroundCapacitor-Less Memristive Integrate-and-Fire Neuron with Stochastic Behavior
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“This results in probabilistic switching behavior, which can be modeled statistically with a statistical distribution [14].”
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BackgroundPhysical Constraints for Ideal Memelements
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“It is shown in [19] that the stochasticity time of magnetic switching can be well characterized and controlled, and stochastic uncorrelated switching events can be used for novel computing schemes, including stochastic computing and neuromorphic applications.”
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“It also involves two sub-processes, namely the nucleation and subsequent growth of a gap that results from the rupture of CFs. Inherently, the growth of this gap during the RESET process is also stochastic [9].”
Empirical Programming Protocol and Monitor-Free Multi-Level Modulation for Memristors
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Optimizing Event Camera Data Processing with Asynchronous Hold for Stochastic Neural Networks
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Resistive memory co-design in CMOS technologies
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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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