Reconfigurable MoS<sub>2</sub> Memtransistors for Continuous Learning in Spiking Neural Networks
Jiangtan Yuan, Stephanie E. Liu, Ahish Shylendra, William A. Gaviria Rojas, Silu Guo, Hadallia Bergeron, Shaowei Li, Hong‐Sub Lee, Shamma Nasrin, Vinod K. Sangwan, Amit Ranjan Trivedi, Mark C. Hersam
Abstract
Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning.