Probing switching mechanism of memristor for neuromorphic computing
Zhe Yang,
Zirui Zhang,
Ce Li,
Dongliang Yang,
Fei Hui,
Linfeng Sun
Affiliations
Zhe Yang
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology , Beijing 100081, People’s Republic of China
Zirui Zhang
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology , Beijing 100081, People’s Republic of China
Ce Li
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology , Beijing 100081, People’s Republic of China
Dongliang Yang
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology , Beijing 100081, People’s Republic of China
Fei Hui
School of Material Science and Engineering, Zhengzhou University , Zhengzhou 450001, People’s Republic of China
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology , Beijing 100081, People’s Republic of China; Yangtze Delta Region Academy of Beijing Institute of Technology , Jiaxing 314019, People’s Republic of China
In recent, neuromorphic computing has been proposed to simulate the human brain system to overcome bottlenecks of the von Neumann architecture. Memristors, considered emerging memory devices, can be used to simulate synapses and neurons, which are the key components of neuromorphic computing systems. To observe the resistive switching (RS) behavior microscopically and probe the local conductive filaments (CFs) of the memristors, conductive atomic force microscopy (CAFM) with the ultra-high resolution has been investigated, which could be helpful to understand the dynamic processes of synaptic plasticity and the firing of neurons. This review presents the basic working principle of CAFM and discusses the observation methods using CAFM. Based on this, CAFM reveals the internal mechanism of memristors, which is used to observe the switching behavior of memristors. We then summarize the synaptic and neuronal functions assisted by CAFM for neuromorphic computing. Finally, we provide insights into discussing the challenges of CAFM used in the neuromorphic computing system, benefiting the expansion of CAFM in studying neuromorphic computing-based devices.
