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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IEEE
2025-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10947015/ |
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| author | Masud Rana Sk Apu Das Gautham Kumar Deepanshi Bhatnagar Sourodeep Roy Yannick Raffel Maximilian Lederer Konrad Seidel Sourav De Bhaswar Chakrabarti |
| author_facet | Masud Rana Sk Apu Das Gautham Kumar Deepanshi Bhatnagar Sourodeep Roy Yannick Raffel Maximilian Lederer Konrad Seidel Sourav De Bhaswar Chakrabarti |
| author_sort | Masud Rana Sk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cd52257ef5024a79acb7d1bf61998919 |
| institution | Kabale University |
| issn | 2168-6734 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of the Electron Devices Society |
| spelling | doaj-art-cd52257ef5024a79acb7d1bf619989192025-08-20T03:33:26ZengIEEEIEEE Journal of the Electron Devices Society2168-67342025-01-011376276810.1109/JEDS.2025.355667510947015Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect TransistorsMasud Rana Sk0https://orcid.org/0009-0002-1795-6394Apu Das1Gautham Kumar2https://orcid.org/0000-0002-5095-3331Deepanshi Bhatnagar3Sourodeep Roy4https://orcid.org/0000-0002-9321-1880Yannick Raffel5https://orcid.org/0000-0001-8629-5206Maximilian Lederer6https://orcid.org/0000-0002-1739-2747Konrad Seidel7Sourav De8https://orcid.org/0000-0002-1930-8799Bhaswar Chakrabarti9https://orcid.org/0000-0003-0623-3895Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, IndiaCollege of Semiconductor Research, National Tsing Hua University, Hsinchu, TaiwanCollege of Semiconductor Research, National Tsing Hua University, Hsinchu, TaiwanCollege of Semiconductor Research, National Tsing Hua University, Hsinchu, TaiwanDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai, IndiaFraunhofer-Institut für Photonische Mikrosysteme IPMS - Center Nanoelectronic Technologies, Dresden, GermanyFraunhofer-Institut für Photonische Mikrosysteme IPMS - Center Nanoelectronic Technologies, Dresden, GermanyFraunhofer-Institut für Photonische Mikrosysteme IPMS - Center Nanoelectronic Technologies, Dresden, GermanyCollege of Semiconductor Research, National Tsing Hua University, Hsinchu, TaiwanDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai, IndiaBrain-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.https://ieeexplore.ieee.org/document/10947015/FerroelectricsFeFETHSOspike-time-dependent plasticitySNN |
| spellingShingle | Masud Rana Sk Apu Das Gautham Kumar Deepanshi Bhatnagar Sourodeep Roy Yannick Raffel Maximilian Lederer Konrad Seidel Sourav De Bhaswar Chakrabarti Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors IEEE Journal of the Electron Devices Society Ferroelectrics FeFET HSO spike-time-dependent plasticity SNN |
| title | Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors |
| title_full | Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors |
| title_fullStr | Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors |
| title_full_unstemmed | Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors |
| title_short | Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors |
| title_sort | spike timing dependent learning dynamics in silicon doped hafnium oxide based ferroelectric field effect transistors |
| topic | Ferroelectrics FeFET HSO spike-time-dependent plasticity SNN |
| url | https://ieeexplore.ieee.org/document/10947015/ |
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