Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications
This work presents 2-bits/cell operation in deeply scaled ferroelectric finFETs (Fe-finFET) with a 1 µs write pulse of maximum ±5 V amplitude and WRITE endurance above 109 cycles. Fe-finFET devices with single and multiple fins have been fabricated on an SOI wafer using a gate first process, with ga...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Nanotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnano.2021.826232/full |
| _version_ | 1828143649236451328 |
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| author | Sourav De Sourav De Md. Aftab Baig Bo-Han Qiu Franz Müller Hoang-Hiep Le Maximilian Lederer Thomas Kämpfe Tarek Ali Po-Jung Sung Chun-Jung Su Yao-Jen Lee Darsen D. Lu |
| author_facet | Sourav De Sourav De Md. Aftab Baig Bo-Han Qiu Franz Müller Hoang-Hiep Le Maximilian Lederer Thomas Kämpfe Tarek Ali Po-Jung Sung Chun-Jung Su Yao-Jen Lee Darsen D. Lu |
| author_sort | Sourav De |
| collection | DOAJ |
| description | This work presents 2-bits/cell operation in deeply scaled ferroelectric finFETs (Fe-finFET) with a 1 µs write pulse of maximum ±5 V amplitude and WRITE endurance above 109 cycles. Fe-finFET devices with single and multiple fins have been fabricated on an SOI wafer using a gate first process, with gate lengths down to 70 nm and fin width 20 nm. Extrapolated retention above 10 years also ensures stable inference operation for 10 years without any need for re-training. Statistical modeling of device-to-device and cycle-to-cycle variation is performed based on measured data and applied to neural network simulations using the CIMulator software platform. Stochastic device-to-device variation is mainly compensated during online training and has virtually no impact on training accuracy. On the other hand, stochastic cycle-to-cycle threshold voltage variation up to 400 mV can be tolerated for MNIST handwritten digits recognition. A substantial inference accuracy drop with systematic retention degradation was observed in analog neural networks. However, quaternary neural networks (QNNs) and binary neural networks (BNNs) with Fe-finFETs as synaptic devices demonstrated excellent immunity toward the cumulative impact of stochastic and systematic variations. |
| first_indexed | 2024-04-11T20:03:01Z |
| format | Article |
| id | doaj.art-e00d78ed90be40e29e7c428dbb80f3f5 |
| institution | Directory Open Access Journal |
| issn | 2673-3013 |
| language | English |
| last_indexed | 2024-04-11T20:03:01Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nanotechnology |
| spelling | doaj.art-e00d78ed90be40e29e7c428dbb80f3f52022-12-22T04:05:32ZengFrontiers Media S.A.Frontiers in Nanotechnology2673-30132022-01-01310.3389/fnano.2021.826232826232Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit ImplicationsSourav De0Sourav De1Md. Aftab Baig2Bo-Han Qiu3Franz Müller4Hoang-Hiep Le5Maximilian Lederer6Thomas Kämpfe7Tarek Ali8Po-Jung Sung9Chun-Jung Su10Yao-Jen Lee11Darsen D. Lu12Institute of Microelectronics, National Cheng Kung University, Tainan, TaiwanCenter for Nanotechnology, Fraunhofer IPMS, Dresden, GermanyInstitute of Microelectronics, National Cheng Kung University, Tainan, TaiwanInstitute of Microelectronics, National Cheng Kung University, Tainan, TaiwanCenter for Nanotechnology, Fraunhofer IPMS, Dresden, GermanyInstitute of Microelectronics, National Cheng Kung University, Tainan, TaiwanCenter for Nanotechnology, Fraunhofer IPMS, Dresden, GermanyCenter for Nanotechnology, Fraunhofer IPMS, Dresden, GermanyCenter for Nanotechnology, Fraunhofer IPMS, Dresden, GermanyTaiwan Semiconductor Research Institute, Hsinchu, TaiwanTaiwan Semiconductor Research Institute, Hsinchu, TaiwanTaiwan Semiconductor Research Institute, Hsinchu, TaiwanInstitute of Microelectronics, National Cheng Kung University, Tainan, TaiwanThis work presents 2-bits/cell operation in deeply scaled ferroelectric finFETs (Fe-finFET) with a 1 µs write pulse of maximum ±5 V amplitude and WRITE endurance above 109 cycles. Fe-finFET devices with single and multiple fins have been fabricated on an SOI wafer using a gate first process, with gate lengths down to 70 nm and fin width 20 nm. Extrapolated retention above 10 years also ensures stable inference operation for 10 years without any need for re-training. Statistical modeling of device-to-device and cycle-to-cycle variation is performed based on measured data and applied to neural network simulations using the CIMulator software platform. Stochastic device-to-device variation is mainly compensated during online training and has virtually no impact on training accuracy. On the other hand, stochastic cycle-to-cycle threshold voltage variation up to 400 mV can be tolerated for MNIST handwritten digits recognition. A substantial inference accuracy drop with systematic retention degradation was observed in analog neural networks. However, quaternary neural networks (QNNs) and binary neural networks (BNNs) with Fe-finFETs as synaptic devices demonstrated excellent immunity toward the cumulative impact of stochastic and systematic variations.https://www.frontiersin.org/articles/10.3389/fnano.2021.826232/fullhafnium oxideferroelectric finFETnon-volatile memoryvariationneural networks |
| spellingShingle | Sourav De Sourav De Md. Aftab Baig Bo-Han Qiu Franz Müller Hoang-Hiep Le Maximilian Lederer Thomas Kämpfe Tarek Ali Po-Jung Sung Chun-Jung Su Yao-Jen Lee Darsen D. Lu Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications Frontiers in Nanotechnology hafnium oxide ferroelectric finFET non-volatile memory variation neural networks |
| title | Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications |
| title_full | Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications |
| title_fullStr | Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications |
| title_full_unstemmed | Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications |
| title_short | Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications |
| title_sort | random and systematic variation in nanoscale hf0 5zr0 5o2 ferroelectric finfets physical origin and neuromorphic circuit implications |
| topic | hafnium oxide ferroelectric finFET non-volatile memory variation neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fnano.2021.826232/full |
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