Defect-Driven Neuromorphic Plasticity in Planar ZnO Optoelectronic Synapses.
Zhiyuan Ren1, Shan Wang1, Bingheng Meng1,2
1Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, P. R. China.
ACS Applied Materials & Interfaces
|January 13, 2026
Summary
This study links atomic defects in zinc oxide (ZnO) synapses to their neuromorphic performance. Engineering these defects optimizes synaptic weight for applications like artificial vision hardware.
Area of Science:
- Materials Science
- Neuroscience
- Optoelectronics
Background:
- Designing oxide-based optoelectronic synapses requires understanding atomic-scale defect dynamics.
- Defect behavior directly impacts system-level neuromorphic functions.
Purpose of the Study:
- To establish a link between defect dynamics and synaptic plasticity in a planar ZnO synapse.
- To develop a framework for tuning synaptic weight through defect engineering.
Main Methods:
- Combined steady-state and time-resolved spectroscopies with electrical measurements.
- Investigated nanosecond-scale oxygen-vacancy carrier lifetime and its effect on persistent photoconductivity (PPC).
Main Results:
- A dynamic framework spanning multiple timescales was developed, linking defect states to PPC decay.
- Long-lived defects were shown to regulate paired-pulse facilitation retention and plasticity transitions.
- An optimized ZnO synapse achieved 90.8% handwritten digit recognition accuracy.
Conclusions:
- A cross-time scale design strategy bridges atomic-level defect engineering with neuromorphic performance.
- This approach enables predictive tuning of synaptic weight for artificial vision hardware.
- The study paves the way for advanced artificial vision systems.
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