金属离子通道表面优化增强ECM电子突触电导可控性

增强电子突触的电导可控性（电导细分等级和再现能力）可保证基于神经形态硬件系统的算法快速、准确、稳定运行，具有重要意义。电化学金属化（ECM）电子突触的电导由纳米金属丝形貌决定，通过电激励控制金属丝形貌而精确调控电导极具挑战。本项目针对纳米金属丝形貌演变中的关键影响因素，提出“金属离子通道表面优化-纳米金属丝形貌可控-电导可控”的研究思路，围绕通道表面对金属离子迁移特性、纳米金属丝热力学稳定性及其生长-断裂动力学特性的影响这一关键科学问题，拟设计制备金属离子通道，对通道表面进行化学改性以获得具有优异电导可控性的ECM电子突触，揭示通道表面化学基团对金属离子迁移特性、纳米金属丝热力学稳定性、纳米金属丝生长-断裂过程的影响机制，建立纳米金属丝生长-断裂动力学特性对器件电导的调控模型，掌握具有高电导可控性ECM电子突触的关键制备技术，为基于ECM电子突触的大型神经网络设计制备提供理论和技术参考。

Improving the conductance-controllability of electronic synapses (including the subdividable level of conductance and the reproducibility of conductance) can ensure the fast, accurate, and stable operation of artificial intelligence algorithms, which is of great significance. Conductance of electrochemical metallization (ECM) electronic synapse is determined by the morphology of the metal filaments. How to control the morphology of the metal filaments through external electrical excitation to accurately regulate the device conductance is extremely challenging. Aiming at the key influencing factors in the evolution of metal filament morphology, research that is guided by the core idea of 'surface modification of metal ion channels-morphology control of metal filaments-conductivity control' will be conducted on the key scientific issues of the effects of channel surfaces on the transport characteristics of metal ions, the stability of filaments and the growth-fracture kinetics of filaments in ECM electronic synapses. Metal ion nano-channels will be designed and fabricated, and the channel surface will be chemically modified to obtain ECM electronic synapses with well conductance-controllability. The effect of chemical groups on the surface of the channel on the migration characteristics of metal ions, the thermodynamic stability of metal filaments, and the growth-fracture process of metal filaments will be revealed. The decisive role of metal ion migration characteristics on the growth-fracture process of filaments will be clarified. A model for regulating the growth-fracture kinetics of filaments on the device’s conductance will be established. The key preparation technology of ECM electronic synapses with high conductance-controllability will be mastered. This research can provide theoretical and technical references for the design and fabrication of large neural networks based on ECM electronic synapses.