Ohmic, memristive synaptic weights are fabricated with a back-end-of-line compatible process, based on a 3.5 nm HfZrO4 thin film crystallized in the ferroelectric phase at only 400 °C. The current density is increased by three orders of magnitude compared to the state-of-the-art. The use of a metallic oxide interlayer, WOx, allows excellent retention (only 6% decay after 106 s) and endurance (1010 full switching cycles). The On/Off of 7 and the small device-to-device variability (<5%) make them promising candidates for neural networks inference. The synaptic functionality for online learning is also demonstrated: using pulses of increasing (resp. constant) amplitude and constant (resp. increasing) duration, emulating spike-timing (resp. spike-rate) dependent plasticity. Writing with 20 ns pulses only dissipate femtojoules. The cycle-to-cycle variation is below 2%. The training accuracy (MNIST) of a neural network is estimated to reach 92% after 36 epochs. Temperature-dependent experiments reveal the presence of allowed states for charge carriers within the bandgap of hafnium zirconate. Upon polarization switching, the screening of the polarization by mobile charges (that can be associated with oxygen vacancies and/or ions) within the ferroelectric layer modifies the energy profile of the conduction band and the bulk transport properties.

Scaled, Ferroelectric Memristive Synapse for Back-End-of-Line Integration with Neuromorphic Hardware / Begon-Lours, L.; Halter, M.; Puglisi, F. M.; Benatti, L.; Falcone, D. F.; Popoff, Y.; Davila Pineda, D.; Sousa, M.; Offrein, B. J.. - In: ADVANCED ELECTRONIC MATERIALS. - ISSN 2199-160X. - (2022), pp. 1-9. [10.1002/aelm.202101395]

Scaled, Ferroelectric Memristive Synapse for Back-End-of-Line Integration with Neuromorphic Hardware

Puglisi F. M.;Benatti L.;
2022

Abstract

Ohmic, memristive synaptic weights are fabricated with a back-end-of-line compatible process, based on a 3.5 nm HfZrO4 thin film crystallized in the ferroelectric phase at only 400 °C. The current density is increased by three orders of magnitude compared to the state-of-the-art. The use of a metallic oxide interlayer, WOx, allows excellent retention (only 6% decay after 106 s) and endurance (1010 full switching cycles). The On/Off of 7 and the small device-to-device variability (<5%) make them promising candidates for neural networks inference. The synaptic functionality for online learning is also demonstrated: using pulses of increasing (resp. constant) amplitude and constant (resp. increasing) duration, emulating spike-timing (resp. spike-rate) dependent plasticity. Writing with 20 ns pulses only dissipate femtojoules. The cycle-to-cycle variation is below 2%. The training accuracy (MNIST) of a neural network is estimated to reach 92% after 36 epochs. Temperature-dependent experiments reveal the presence of allowed states for charge carriers within the bandgap of hafnium zirconate. Upon polarization switching, the screening of the polarization by mobile charges (that can be associated with oxygen vacancies and/or ions) within the ferroelectric layer modifies the energy profile of the conduction band and the bulk transport properties.
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Scaled, Ferroelectric Memristive Synapse for Back-End-of-Line Integration with Neuromorphic Hardware / Begon-Lours, L.; Halter, M.; Puglisi, F. M.; Benatti, L.; Falcone, D. F.; Popoff, Y.; Davila Pineda, D.; Sousa, M.; Offrein, B. J.. - In: ADVANCED ELECTRONIC MATERIALS. - ISSN 2199-160X. - (2022), pp. 1-9. [10.1002/aelm.202101395]
Begon-Lours, L.; Halter, M.; Puglisi, F. M.; Benatti, L.; Falcone, D. F.; Popoff, Y.; Davila Pineda, D.; Sousa, M.; Offrein, B. J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1274597
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