Si‐Based Dual‐Gate Field‐Effect Transistor Array for Low‐Power On‐Chip Trainable Hardware Neural Networks

Abstract

Herein, dual‐gate field‐effect transistors (DG FETs) fabricated on Si substrate and a corresponding NOR‐type array designed for low‐power on‐chip trainable hardware neural networks (HNNs) are presented. The fabricated DG FET exhibits notable endurance characteristics, with the subthreshold swing remaining consistently within a 2.45% range of change and ΔV th per cycle maintaining stability at 4.5% over repetitive program and erase operations, up to 104 cycles. Furthermore, a multilevel characteristic is achieved through low‐power program/erase operations based on Fowler–Nordheim (FN) tunneling, which exhibit 0.09 and 0.99 fJ per spike, respectively. These characteristics provide the HNN stability, along with high performance and power efficiency. The NOR‐type array in this work demonstrates selective update and bidirectional vector‐by‐matrix multiplication capabilities. This enables on‐chip training based on a gradient descent algorithm, without the need for an additional array for backpropagation. Subsequently, a simulation of the Modified National Institute of Standards and Technology classification is conducted to evaluate the accuracy and training power consumption of the proposed device in comparison to other two‐terminal memristor devices. The results show that the DG FET array achieves superior accuracy while maintaining over 180.4 times higher energy efficiency, demonstrating the potential of the DG FET as a promising candidate for low‐power HNN applications.

Keywords

• dual-gate field-effect transistors
• gradient descent algorithms
• hardware-based neural networks
• NOR-type arrays
• on-chip training