A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

Abstract

The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10 μm devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

“…[3,[27][28][29][30] In the hardware implementation of FC networks, e.g., multi-layer perceptrons (MLPs), the weight matrices could be directly mapped to the conductance matrices of memristive crossbar arrays. [3,[27][28][29][30] In the hardware implementation of FC networks, e.g., multi-layer perceptrons (MLPs), the weight matrices could be directly mapped to the conductance matrices of memristive crossbar arrays.…”

“…[108][109][110][111] In addition, electrochemical random-access memory (ECRAM) based on ion intercalation has recently been reported as a promising synaptic cell, showing multi-states and incremental switching with near-ideal switching symmetry and linearity. [29,[112][113][114][115][116][117][118][119] The electrochemically driven ion intercalation process is more controllable than filament-related ion movements in RRAM; therefore, ECRAM also exhibits a much smaller stochasticity. In addition, by borrowing the battery concept, those devices successfully decouple the read and write operations and thus realize low programming energy and long retention time simultaneously.…”

A Universal Platform for Fabricating Organic Electrochemical Devices

“…[38,39] With the LiPS architecture, we can also fabricate aqueous-based neuromorphic devices using conventional semiconducting polymers. [38,39] With the LiPS architecture, we can also fabricate aqueous-based neuromorphic devices using conventional semiconducting polymers.…”

Biomimetic approaches toward smart bio-hybrid systems

“…It can be envisioned that a ‘cyborg’ brain where new interfaces are established between neurons and multifunctional bio-hybrids that display synaptic-like plasticity [81]. Current large-scale memrisistor networks have reached a sufficient level of sophistication for the emulation of many of the neuronal behaviors [82]; further research is required to make the memrisistor network smaller, softer and biocompatible [83]. Finally, the smart bio-hybrids can lead to the creation of new cellular materials that have the potential to open up completely new areas of application, such as in hybrid information processing systems.…”