DOI: https://doi.org/10.29363/nanoge.matnec.2022.011
Publication date: 23rd February 2022
With Moore's law coming to an end and Artificial Neural Networks gaining widespread adoption, specialized neuromorphic accelerators have become highly sought after. Electrochemical conductive-polymer devices with many linearly programmable resistance states enable uncomplicated, energy-efficient implementations. They suffer, however, from self-discharge and non-ideal writes caused by depolarizing impurities that are difficult to exclude and limit device performance. This is especially valid for scaled-down devices. Predicting these effects through numerical simulations could enable partial mitigation and highlight hard device limits.
Our work combines two-phase charge transport models based on the Nernst-Planck-Poisson equations [1] with electrochemical self-discharge based on a Butler-Volmer approach to simulate PEDOT:PSS based neuromorphic devices [2, 3] in detail. These device models are aggregated into virtual neuromorphic crossbar arrays, on which inference of neural networks under the influence of self-discharge is simulated. Our device models validate against experimental data for cyclic voltammetry and self-discharge experiments. They can predict optimal device parameters and operating conditions depending on depolarizing impurities and write error. For example, for devices based on PEDOT:PSS/PEI, between 81 and 107 discrete states stable for at least five minutes can be expected at 0.5 % write error. Our results show that a single-layer network previously implemented on a metal-oxide memristive crossbar [4] can be transferred to electrochemical devices and keep a perfect classification accuracy of 100 % for over three hours at ambient conditions. Furthermore, compensatory programming pulses based only on the crossbar state and time of inference revert the drift in the network's weights entirely within the read/write accuracy limits. We expect a multi-layer network to see a more substantial impact of self-discharge that the demonstrated compensatory pulses will help help to offset.
This work enables an in-depth understanding of electrochemical neuromorphic devices, their limitations, and how to mitigate them. It, therefore, takes an essential step towards the successful implementation of the first electrochemical neuromorphic accelerators.
Matthias Wessling acknowledges the European Research Council Advanced Investigator Programme (694946) for financial support and DFG funding through the Gottfried Wilhelm Leibniz Award 2019 (WE 4678/12-1).
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