Programmable Iontronic Memristor for Neuromorphic Computing Applications
Guo-Xing Miao
University of Waterloo
Wednesday, October 30, 2024
11:00 a.m.
In-person: QNC 1101
Abstract: Iontronics benefits from the controllable motion and detection of ions in electronics devices. We demonstrate an ion-coupled memristive system with solid-state electrolyte lithium phosphorus oxynitride as the ion source and the embedding and releasing of Li ions inside the cathodic like TiOx for volatile conductance responses. The system exhibits synapse-like short-term plasticity behaviour without requiring a forming process beforehand or a compliance current during switching, rendering a natural platform for hardware simulating neuron functionalities. Different short-term pulse-based phenomena, including paired pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity were observed with unique self-relaxation characteristics. Based on the voltage excitation period, the timescale of the volatile memory can be tuned. In addition, the volatile analog devices can be configured into non-volatile memory units with multibit storage capabilities after an electroforming process. Therefore, on the same platform, we can configure volatile units as nonlinear dynamic reservoirs for performing neuromorphic training and the non-volatile units as the weight storage layer. These phenomena can be generalized to other ion active systems and can effectively process and update temporal information for reservoir and neuromorphic computing paradigms. We proceed to simulate voice recognition as an example with the variable time scale and a minimal training dataset. We also show their actual back-end-of-line (BOEL) integration on 180 nm CMOS chips as a demonstration of principle.