ZnO-SnO2/WO3-x heterojunction artificial synapse for realization and integration of multiple biological cognitive functions
In current memristor-based neuromorphic computing research, several studies face the challenge of realizing only a single function at a time or having isolated functions. This limitation is particularly evident when simulating biological cognition, as the overall synergy between multiple cognitive f...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | International Journal of Extreme Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2631-7990/addf1e |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In current memristor-based neuromorphic computing research, several studies face the challenge of realizing only a single function at a time or having isolated functions. This limitation is particularly evident when simulating biological cognition, as the overall synergy between multiple cognitive functions is difficult to represent. In this work, a high-performance heterojunction memristor is presented at first. The memristor-based neural network and functional circuit are further implemented to realize and integrate multiple cognitive functions. Specifically, the proposed photoelectric memristor has the structure of Ag/ZnO-SnO _2 /WO _3-x /ITO, it exhibits various synaptic behaviors under external modulations, which are characterized by good stability and repeatability. Based on this device, a neural network is built to realize the basic recognition function in biological cognition. The recognition results are translated into different labelled voltage signals and subsequently fed into a memristor-based functional circuit. By leveraging memory characteristics and tunable conductance of the memristor, and controlling the specific circuit functionalities, the input signals are processed to produce different outputs representing various cognitive functions. This methodology allows the realization and integration of recognition, memory, learning, association, relearning, and forgetting into one single system, thereby enabling a more comprehensive and authentic simulation of biological cognition. This work presents a novel memristor and a method for achieving and integrating multiple neuromorphic computing functions within a single system, providing a successful example for achieving complete biological function. |
|---|---|
| ISSN: | 2631-7990 |