Micro/Nano Fabrication of Layered Semiconductor Devices for Hardware Implementation of Neuromorphic Computing

Abstract

Artificial intelligence (AI) has been extensively used in the routines of human life and shown superior efficiency in various tasks, including pattern classification, voice recognition, and language processing. However, to solve the complicated problems involving large amount of spatiotemporal data (e.g., controlling of dynamic systems), AI development is approaching the fundamental limitations of the current computing systems based on von Neumann’s architecture. To overcome such technological barriers, new neuromorphic computing techniques have been being actively explored, seeking to substantially improve the energy efficiency for computing tasks and extend AI capacities. In addition, such neuromorphic computing techniques mimic the architecture and functioning of the biological neural networks and they can enable in-memory computing architectures with parallel-processing capability. To specifically improve the energy- and time-efficiency of neuromorphic computing, a range of physical systems have been proposed and investigated to serve as the computing units dedicated to hardware-based neuromorphic systems. Among these physical systems, memristors, a new class of electronic devices, have attracted tremendous attention because of their great potential for enabling low-power in-memory computing processes. However, to ultimately realize the memristor-based neuromorphic computing devices and systems for practical applications, we need to address a series of important challenges: (i) Salable nanofabrication methods for producing high-quality memritive materials are still deficient; (ii) The device physics about the unique electronic properties of memristive materials is not fully understood; and (iii) system-level integration of memristor-based neuromorphic computing devices is not fully developed. The presented dissertation projects aim to address part of the aforementioned challenges and realize the following objectives: (1) Development of a scalable nanofabrication approach, termed rubbing-induced site-selective deposition (RISS), capable of producing patterned 2D-material-based memristive device structures without additional lithographic processes; (2) Systematical investigation of the switching characteristics and mechanisms of RISS-produced Bi2Se3 memristors for neuromorphic computing applications; (3) Development of Bi2Se3-based memristive devices capable of directly extracting spatiotemporal information from analogue video signals that could be utilized for computer vision applications ; (4) Creation of Bi2Se3 memristive networks that can realize hardware implementation of neuromorphic computing frameworks for robotic vehicle control.