Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors
Brain-inspired computing, with its potential for energy-efficient spatio-temporal data processing, has spurred significant interest in spiking neural networks and their hardware implementations. Leveraging their non-volatile memory and analog tunability, Ferroelectric field-effect transistors have e...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10947015/ |
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| Summary: | Brain-inspired computing, with its potential for energy-efficient spatio-temporal data processing, has spurred significant interest in spiking neural networks and their hardware implementations. Leveraging their non-volatile memory and analog tunability, Ferroelectric field-effect transistors have emerged as promising candidates for realizing low-power synaptic devices within spiking neural networks. However, previous ferroelectric field-effect transistor-based implementations of spike-timing-dependent plasticity, a crucial learning mechanism in spiking neural networks, have often relied on complex circuit topologies or suffered from high energy consumption. Here, we report a comprehensive study of spike-timing-dependent plasticity learning dynamics in silicon-doped hafnium oxide-based ferroelectric field effect transistors, demonstrating precise control of synaptic weight modulation using various spike shapes and timings. We investigate the impact of different spike waveforms on energy consumption and find that triangular spikes achieve a 20% reduction in energy consumption compared to rectangular spikes, a significant improvement for large-scale spiking neural network implementations. Our results highlight the potential of single-device ferroelectric field-effect transistor synapses for realizing energy-efficient and scalable spiking neural networks, paving the way for next-generation neuromorphic computing. |
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| ISSN: | 2168-6734 |