In this work, nanoscale wedge-structured silicon nitride (SiN _x )-based resistive-switching random-access memory with data non-volatility and conductance graduality has been designed, fabricated, and characterized for its application in the hardware neuromorphic system. The process integration with full Si-processing-compatibility for constructing the unique wedge structure by which the electrostatic effects in the synaptic device operations are maximized is demonstrated. The learning behaviors of the fabricated synaptic devices are shown. In the end, vector-matrix multiplication is experimentally verified in the array level for application in more energy-efficient hardware-driven neuromorphic systems.

在这项工作中，设计、制造和表征了基于纳米楔形结构氮化硅 (SiN _x ) 的具有数据非易失性和电导渐变性的电阻开关随机存取存储器，并对其在硬件神经形态系统中的应用进行了表征。展示了具有完全硅加工兼容性的工艺集成，用于构建独特的楔形结构，通过该结构使突触装置操作中的静电效应最大化。显示了制造的突触装置的学习行为。最后，向量矩阵乘法在阵列级别进行了实验验证，以应用于更节能的硬件驱动神经形态系统。