Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing. This study presents ZnO-based artificial synapses with peptide insulators for the electrical emulation of biological synapses. We demonstrated the dynamic responses of the device under various environmental conditions. The proton-conducting property of the tyrosine-rich peptide enables time-dependent responses under ambient conditions such that various aspects of synaptic behaviors are emulated by the devices. The transition from short-term memory to long-term memory is achieved via electrochemical doping of ZnO by protons. Furthermore, we demonstrate an image classification simulation using a multi-layer perceptron model to evaluate the potential of the device for use in neuromorphic computing. The neural network based on our device achieved a recognition accuracy of 87.47% for the MNIST handwritten digit images. This work proposes a novel device platform inspired by biosystems for brain-mimetic hardware systems.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A2C2004864 ).
All Science Journal Classification (ASJC) codes
- Ceramics and Composites
- Mechanics of Materials
- Mechanical Engineering
- Polymers and Plastics
- Metals and Alloys
- Materials Chemistry