Li Zheng, Huang Xinyu, Xu Langlang, Peng Zhuiri, Yu Xiang-Xiang, Shi Wenhao, He Xiao, Meng Xiaohan, Yang Daohong, Tong Lei, Miao Xiangshui, Ye Lei
School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Hubei Yangtze Memory Laboratories, Wuhan 430205, China.
Nano Lett. 2023 Dec 27;23(24):11710-11718. doi: 10.1021/acs.nanolett.3c03553. Epub 2023 Oct 27.
Compared with binary systems, ternary computing systems can utilize fewer devices to realize the same information density. However, most ternary computing systems based on binary CMOS circuits require additional devices to bridge binary processing and ternary computing. Exploring new device architectures for direct ternary processing and computing becomes the key to promoting ternary computing systems. Here, we demonstrated a 2D van der Waals vertical heterojunction transistor (V-HTR) with three flat conductance states, which can be the basic cell in ternary circuits to perform ternary processing and computing, without additional devices. A ternary neural network (TNN) and a ternary inverter were demonstrated based on the V-HTRs. The TNN can eliminate fuzzy data and output only clear data by building a ternary quantization function. By demonstrating both ternary logic and a TNN on the same device architecture, the 2D V-HTR shows potential as a basic hardware unit for future ternary computing systems.
与二元系统相比,三元计算系统可以使用更少的器件来实现相同的信息密度。然而,大多数基于二元CMOS电路的三元计算系统需要额外的器件来连接二元处理和三元计算。探索用于直接三元处理和计算的新器件架构成为推动三元计算系统发展的关键。在此,我们展示了一种具有三种平坦电导状态的二维范德华垂直异质结晶体管(V-HTR),它可以作为三元电路中的基本单元来执行三元处理和计算,而无需额外的器件。基于V-HTR展示了一个三元神经网络(TNN)和一个三元反相器。通过构建三元量化函数,TNN可以消除模糊数据并仅输出清晰数据。通过在同一器件架构上展示三元逻辑和TNN,二维V-HTR显示出作为未来三元计算系统基本硬件单元的潜力。