Ultra Low Energy Charge Trapping MOSFET With Neuro-Inspired Learning Capabilities

Abstract

In this paper, a synaptic semiconductor device and its crossbar have been demonstrated for real-time artificial intelligence (AI) applications. The proposed device is a dual gate, metal-oxide-semiconductor field-effect transistor (MOSFET) with charge trapping and de-trapping capabilities to emulate synaptic weight modulation. It achieves ultra-low energy consumption in the range few-tenth of femto-Joules and can be further reduced by adjusting device parameters. Moreover, to prove the potential of the proposed device as an analog AI accelerator, it was trained and tested for an ANN based underwater image enhancement algorithm on Underwater Image Enhancement Benchmark (UIEB) dataset. The results were compared to the state-of-arts which show that our proposed algorithm works better than most of the previous works on qualitative as well as quantitative grounds. The ultra-low energy consumption of the order of one-tenth of femto-Joules makes the proposed crossbar architecture (AI accelerator) a potential candidate for real-time data intensive AI/ML applications.