Side-Gated Iontronic Memtransistor: A Fast and Energy-Efficient Neuromorphic Building Block

Abstract

Iontronic memtransistors have emerged as technologically superior to conventional memristors for neuromorphic applications due to their low operating voltage, additional gate control, and enhanced energy efficiency. In this study, a side-gated iontronic organic memtransistor (SG-IOMT) device is explored as a potential energy-efficient hardware building block for fast neuromorphic computing. Its operational flexibility, which encompasses the complex integration of redox activities, ion dynamics, and polaron generation, makes this device intriguing for simultaneous information storage and processing, as it effectively overcomes the von Neumann bottleneck of conventional computing. The SG-IOMT device achieves linear channel conductance performance metrics with switching speeds in the microsecond range and energy efficiency down to a few femtojoules, comparable to those of the brain. This finding demonstrates robustness, supporting the Atkinson–Shiffrin memorization model, and the four most common Hebbian learning rules. Overall, this SG-IOMT device architecture offers significant advantages over conventional architectures, as it yields remarkable image classification performance in convolutional neural network simulations.