Analog Weight Update Rule in Ferroelectric Hafnia, using pico-Joule Programming Pulses
🤖 AI Summary
This work addresses the challenges of high energy consumption in synaptic weight updates and circuit parasitic effects limiting the minimum programming pulse width in neuromorphic hardware. By leveraging CMOS back-end-compatible hafnium–zirconium oxide ferroelectric nanolaminates and laterally scaling device areas below 100 μm², the study demonstrates analog weight updating under ultrashort 20 ns pulses. It achieves, for the first time in sub-100 μm² hafnium-based ferroelectric memristors, highly efficient single-pulse operation with energy consumption ≤3 pJ. Furthermore, the research uncovers a novel update mechanism wherein the final conductance is determined solely by pulse amplitude and is independent of the initial state. These findings establish a critical device foundation for low-power, high-precision neuromorphic computing.
Application Category
📝 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 laterally scaling the device area under 100 $\mu$m$^2$, the self-loading time becomes sufficiently short to enable 20 ns programming, which corresponds to a maximum of 3 picoJoules per pulse. Further, in this work, the weight update rule with 20 ns pulses is experimentally measured not only for different amplitudes but also for different initial conductance states. We find that the final weight is determined by the pulse amplitude, independent of the initial weight value.
Problem
Research questions and friction points this paper is trying to address.
neuromorphic hardware
ferroelectric resistive weights
programming pulses
energy efficiency
self-loading time
Innovation
Methods, ideas, or system contributions that make the work stand out.
ferroelectric hafnia
analog weight update
pico-joule programming
neuromorphic hardware
ultrafast synaptic device
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