Artificial Synapse Emulated by Charge Trapping‐Based Resistive Switching Device. Issue 2 (9th October 2018)

• Record Type: Journal Article
• Authors: Zhang, Shi‐Rui; Zhou, Li; Mao, Jing‐Yu; Ren, Yi; Yang, Jia‐Qin; Yang, Guang‐Hu; Zhu, Xin; Han, Su‐Ting; Roy, Vellaisamy A. L.; Zhou, Ye
• Is Part Of: Advanced materials technologies. Volume 4:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-09
• Subjects: artificial synapse -- charge trapping -- hybrid materials -- resistive random access memory -- solution process; Materials science -- Periodicals; Technological innovations -- Periodicals; Materials science; Technological innovations; Periodicals; 620.1105
• DOI: 10.1002/admt.201800342
• Languages: English
• ISSNs: 2365-709X

Abstract

Abstract: The traditional Von Neumann architecture‐based computers are considered to be inadequate in the coming artificial intelligence era due to increasing computation complexity and rising power consumption. Neuromorphic computing may be the key role to emulate the human brain functions and eliminate the Von Neumann bottleneck. As a basic unit in the nervous system, a synapse is responsible for transmitting information between neurons. Resistive random access memory (RRAM) is able to imitate the synaptic functions because of its tunable resistive switching behavior. Here, an artificial synapse based on solution processed polyvinylpyrrolidone (PVPy)–Au nanoparticle (NP) hybrid is fabricated, various synaptic functions including paired‐pulse facilitation (PPF), posttetanic potentiation (PTP), transformation from short‐term plasticity (STP) to long‐term plasticity (LTP) and learning‐forgetting‐relearning process are emulated, making the polymer–metal NPs hybrid system valuable candidates for the design of novel artificial neural architectures. Abstract : An artificial synapse based on the polyvinylpyrrolidone (PVPy)–Au NPs hybrid materials through solution process is reported . Synaptic functions such as paired‐pulse facilitation, post‐tetanic potentiation, the transformation from short‐term plasticity to long‐term plasticity, as well as the learning‐forgetting‐relearning process are emulated, making the polymer–metal nanoparticles hybrid system valuable candidates for the design of novel artificial neural architectures.