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Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing
Shengliang Cheng1,2, Zhen Fan1,2, Jingjing Rao1
1Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
Iscience
|December 21, 2020
Summary
Ferroelectric photovoltaic (FePV) synapses offer a new approach for neuromorphic computing by using photocurrent for analog conductance. This method enhances controllability and silicon compatibility for advanced artificial intelligence applications.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Neuroscience
Background:
- Ferroelectric synapses utilize polarization switching for analog conductance, a purely electronic process.
- Existing ferroelectric synapses face limitations in material choice and thickness, impacting fabrication and performance.
- Depolarization effects can reduce polarization controllability in traditional ferroelectric devices.
Purpose of the Study:
- To propose and demonstrate ferroelectric photovoltaic (FePV) synapses.
- To leverage polarization-controlled photocurrent as a readout mechanism for synaptic weight.
- To overcome limitations of existing ferroelectric synapses and enhance controllability.
Main Methods:
- Fabrication of a Pt/Pb(Zr0.2Ti0.8)O3/LaNiO3 FePV synapse on a silicon substrate.
- Utilizing gradual polarization switching to modulate photovoltaic response.
- Implementing the photovoltaic response as the synaptic weight for device function.
Main Results:
- Demonstrated continuous photovoltaic response modulation with high controllability (low nonlinearity and write noise).
- Exhibited versatile synaptic functions: long-term potentiation/depression and spike-timing-dependent plasticity.
- Achieved high accuracies (>93%) in image recognition using a simulated FePV synapse-based neural network.
Conclusions:
- FePV synapses offer a fabrication-friendly and highly controllable alternative for neuromorphic computing.
- The use of photocurrent readout eliminates material limitations and reduces depolarization effects.
- This technology paves the way for silicon-compatible, advanced artificial intelligence hardware.
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