Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

Keene, Scott T.; Melianas, Armantas; Fuller, Elliot James; van de Burgt, Yoeri; Talin, Albert Alec; Salleo, Alberto

doi:10.1088/1361-6463/aabe70

Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

Journal Article · Tue May 08 00:00:00 EDT 2018 · Journal of Physics. D, Applied Physics

DOI:https://doi.org/10.1088/1361-6463/aabe70· OSTI ID:1464199

^[1]; ^[1]; Fuller, Elliot James ^[2]; ^[3]; Talin, Albert Alec ^[2]; Salleo, Alberto ^[1]

Stanford Univ., Stanford, CA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Eindhoven Univ. of Technology, Eindhoven (Netherlands)

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both training via backpropagation and in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (~170 fJ • µm^–2). Furthermore, this work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.

View Accepted Manuscript (DOE)

Research Organization:: Energy Frontier Research Centers (EFRC) (United States). Nanostructures for Electrical Energy Storage (NEES); Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1464199

Report Number(s):: SAND--2018-8253J; 666441

Journal Information:: Journal of Physics. D, Applied Physics, Journal Name: Journal of Physics. D, Applied Physics Journal Issue: 22 Vol. 51; ISSN 0022-3727

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (5)

Mechanisms for Enhanced State Retention and Stability in Redox-Gated Organic Neuromorphic Devices Keene, Scott Tom; Melianas, Armantas; van de Burgt, Yoeri Advanced Electronic Materials, Vol. 5, Issue 2 https://doi.org/10.1002/aelm.201800686	journal	November 2018
Organic electronics for neuromorphic computing van de Burgt, Yoeri; Melianas, Armantas; Keene, Scott Tom Nature Electronics, Vol. 1, Issue 7 https://doi.org/10.1038/s41928-018-0103-3	journal	July 2018
Reversible uptake and release of sodium ions in layered SnS ₂ -reduced graphene oxide composites for neuromorphic devices Jang, Eun-Kyeong; Park, Youngjun; Lee, Jang-Sik Nanoscale, Vol. 11, Issue 32 https://doi.org/10.1039/c9nr03073e	journal	January 2019
Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing Ling, Haifeng; Koutsouras, Dimitrios A.; Kazemzadeh, Setareh Applied Physics Reviews, Vol. 7, Issue 1 https://doi.org/10.1063/1.5122249	journal	March 2020
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing Fuller, Elliot J.; Keene, Scott T.; Melianas, Armantas Science, Vol. 364, Issue 6440 https://doi.org/10.1126/science.aaw5581	journal	April 2019

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97 MATHEMATICS AND COMPUTING
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electrochemical organic neuromorphic device
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