Boolean Logic Computing Based on Neuromorphic Transistor

General-purpose computers usually use logic gate computing units based on complementary metal oxide semiconductors (CMOS). Due to the separate memory and computing units in Von Neumann architecture, data transmission requires great energy and time consumption. Developing novel neuromorphic devices and comprehensively investigating their logical computing mode are crucial to achieve high-performance and low-power neuromorphic computation. Here, a systematic summary of Boolean logic computing based on emerging neuromorphic transistors is presented. This summary encompasses logical operation modes, materials, device structures, and working mechanisms. The input mode of Boolean logic operation is classified into electrical input, optical input, and synergistic optical/electrical input. Besides, additional modulation strategies to construct programmable logic functions by electrical, optical, and thermal signals are also summarized. These strategies hold great significance as they enable dynamic reconfiguration of logic operations and provide neuromorphic devices with decision-making capabilities. Finally, the application prospects and current challenges to Boolean logic computing based on dendritic integration are discussed from the aspects of device integration, synergistic input/modulation modes, auxiliary peripheral circuit, software/hardware system, etc. It is believed that comprehensive investigations on neuromorphic Boolean logic operations are crucial to push forward the development of future neuromorphic computing toward high efficiency and high integration density.

Automated summary

This article presents a systematic summary of Boolean logic computing based on emerging neuromorphic transistors, including logical operation modes, materials, device structures, and working mechanisms. The input mode of Boolean logic operation is classified into electrical input, optical input, and synergistic optical/electrical input. Additionally, modulation strategies using electrical, optical, and thermal signals to construct programmable logic functions are also summarized. These investigations hold great significance in advancing the development of high-efficiency and high integration density in future neuromorphic computing.