Organic neuromorphic devices: Past, present, and future challenges

Tuchman, Yaakov; Mangoma, Tanyaradzwa N.; Gkoupidenis, Paschalis; Burgt, Yoeri van de; John, Rohit Abraham; Mathews, Nripan; Shaheen, Sean E.; Daly, Ronan; Malliaras, George G.; Salleo, Alberto

doi:10.1557/mrs.2020.196

Cited by 67 publications

(62 citation statements)

References 80 publications

(84 reference statements)

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“…Three-terminal electrochemical random-access memory (ECRAM), also known as redox transistors, can address the accuracy, energy, and latency deficiencies of two-terminal memristors (Fuller et al, 2017(Fuller et al, , 2019bvan de Burgt et al, 2017;Sharbati et al, 2018;Tang et al, 2018;Kim et al, 2019;Li et al, 2019Li et al, , 2020aMelianas et al, 2020;Tuchman et al, 2020;Yao X. et al, 2020). ECRAM achieves exceptionally reproducible, linear, and symmetric weight updates by encoding information in resistance values that reflect changes in the average bulk concentration of dopants like protons in transistor-like channels.…”

Section: Introductionmentioning

confidence: 99%

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

Xiao

Bennett

et al. 2021

Front. Neurosci.

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In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

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Section: Introductionmentioning

confidence: 99%

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

Xiao

Bennett

et al. 2021

Front. Neurosci.

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“…[4][5][6] In comparison to the manipulation of atomic defects in inorganic materials, the large number of state variables or physical degrees of freedom available in ionotronic systems and the ability to directly tune the material characteristics such as redox states, allow these materials to be used as building blocks for efficient "scaled-in" neuromorphic architectures. [7,8] This concept of "scaling-in" to boost the information space per cell without escalating the material and area budget, bears close resemblance to biological neural networks and is a crucial approach toward realizing a practical system.…”

Section: Introductionmentioning

confidence: 99%

Diffusive and Drift Halide Perovskite Memristive Barristors as Nociceptive and Synaptic Emulators for Neuromorphic Computing

et al. 2021

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With the current research impetus on neuromorphic computing hardware, realizing efficient drift and diffusive memristors are considered critical milestones for the implementation of readout layers, selectors, and frameworks in deep learning and reservoir computing networks. Current demonstrations are predominantly limited to oxide insulators with a soft breakdown behavior. While organic ionotronic electrochemical materials offer an attractive alternative, their implementations thus far have been limited to features exploiting ionic drift a.k.a. drift memristor technology. Development of diffusive memristors with organic electrochemical materials is still at an early stage, and modulation of their switching dynamics remains unexplored. Here, halide perovskite (HP) memristive barristors (diodes with variable Schottky barriers) portraying tunable diffusive dynamics and ionic drift are proposed and experimentally demonstrated. An ion permissive poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate interface that promotes diffusive kinetics and an ion source nickel oxide (NiOx) interface that supports drift kinetics are identified to design diffusive and drift memristors, respectively, with methylammonuim lead bromide (CH3NH3PbBr3) as the switching matrix. In line with the recent interest on developing artificial afferent nerves as information channels bridging sensors and artificial neural networks, these HP memristive barristors are fashioned as nociceptive and synaptic emulators for neuromorphic sensory signal computing.

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“…[ 43 ] In addition, the ion transduction of OECTs combined with their neuromorphic properties, [ 21,41 ] render them as an ideal technology platform for numerous applications including bioelectronics, neuro‐inspired processing, and sensing. [ 21,41,44–46 ]…”

Section: Introductionmentioning

confidence: 99%

An Iontronic Multiplexer Based on Spatiotemporal Dynamics of Multiterminal Organic Electrochemical Transistors

Koutsouras

Amiri

Blom

et al. 2021

Adv Funct Materials

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The seamless integration of electronics with biology requires new bio-inspired approaches that, analogously to nature, rely on the presence of electrolytes for signal multiplexing. On the contrary, conventional multiplexing schemes mostly rely on electronic carriers and require peripheral circuitry for their implementation, which imposes severe limitations toward their adoption in bio-applications. Here, a bio-inspired iontronic multiplexer based on spatiotemporal dynamics of organic electrochemical transistors (OECTs), with an electrolyte as the shared medium of communication, is shown. The iontronic system discriminates locally random-access events with no need of peripheral circuitry or address assignment, thus deceasing significantly the integration complexity. The form factors of OECTs that allow for intimate biointerfacing as well as the electrochemical nature of the communication medium, open new avenues for unconventional multiplexing in the emerging fields of bioelectronics, wearables, and neuromorphic computing or sensing.

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Organic neuromorphic devices: Past, present, and future challenges

Abstract: Abstract

Cited by 67 publications

References 80 publications

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

Diffusive and Drift Halide Perovskite Memristive Barristors as Nociceptive and Synaptic Emulators for Neuromorphic Computing

An Iontronic Multiplexer Based on Spatiotemporal Dynamics of Multiterminal Organic Electrochemical Transistors

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