期刊
IEEE TRANSACTIONS ON ELECTRON DEVICES
卷 69, 期 5, 页码 2346-2352出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TED.2022.3159270
关键词
Artificial spiking afferent neuron (ASAN); Mott; neuromorphic computing; VO2
资金
- National Natural Science Foundation of China [62174130, 61704137]
- Key Research and Development Plan of Shaanxi Province [2020GY-021]
- Fundamental Research Funds for the Central Universities [xjh012020009]
This work presents an ASAN system with a simple structure using a new architecture of one VO2 Mott memristor and one resistive sensor (1M1S). By incorporating a flexible pressure sensor into the 1M1S architecture, a tactile ASAN system is realized with pressure stimuli converted into rate-coded spikes. Using a 3 x 3 array of the tactile ASAN systems, different pressure stimulus patterns can be recognized effectively.
Neuromorphic computing based on spiking neural networks (SNNs) has attracted significant research interest due to its low energy consumption and high similarity to biological neural systems. The artificial spiking afferent neuron (ASAN) system is the essential component of neuromorphic computing system to interact with the environment. This work presents an ASAN system with simple structure by employing a new architecture of one VO2 Mott memristor and one resistive sensor (1M1S). The Mott memristors show the bidirectional Mott transition, good endurance (> 1.3 x 10(9)), and high uniformity. By incorporating a flexible pressure sensor into the 1M1S architecture, a tactile ASAN system is realized with the pressure stimuli converted into rate-coded spikes. Using a 3 x 3 array of the tactile ASAN systems, different pressure stimulus patterns can be well recognized. The strong adaptability of the proposed system will enable it to convert lots of environmental stimuli through the widely used resistive sensors into ratecoded spikes as the inputs of neuromorphic computing based on SNNs.
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