Analog Weight Update Rule in Ferroelectric Hafnia, using pico-Joule Programming Pulses
Alexandre Baigol, Nikhil Garg, Matteo Mazza, Yanming Zhang, Elisa Zaccaria, Wooseok Choi, Bert Jan Offrein, Laura B\'egon-Lours

TL;DR
This study demonstrates ultra-low energy ferroelectric resistive weights using hafnia/zirconia nanolaminates, enabling 20 ns programming pulses with picojoule energy, and investigates their weight update behavior.
Contribution
It introduces a CMOS-compatible ferroelectric weight update method with picojoule energy pulses and experimentally characterizes its behavior across conductance states.
Findings
20 ns programming pulses achieve 3 pJ energy per update.
Final weight depends on pulse amplitude, not initial conductance.
Device scaling enables fast, low-energy weight updates.
Abstract
In an effort to compete with the brain's efficiency at processing information, neuromorphic hardware combines artificial synapses and neurons using mixed-signal circuits and emerging memories. In ferroelectric resistive weights, the strength of the synaptic connection between two neurons is stored in the device conductance. During learning, programming pulses are applied to the synaptic weight, which reconfigures the ferroelectric domains and adjusts the conductance. One strategy to lower the energy cost during the training phase is to lower the duration of the programming pulses. However, the latter cannot be shorter than the self-loading time of the resistive weights, limited by intrinsic parasitics in the circuits. In this work, ferroelectric resistive weights are fabricated using a process compatible with CMOS Back-End-Of-Line integration, based on hafnia/zirconia nanolaminates. By…
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