Lorentzian Switching Dynamics in HZO-based FeMEMS Synapses for Neuromorphic Weight Storage

TL;DR

This paper presents a novel ferroelectric MEMS-based synapse using HZO that stores analog weights via piezoelectric modulation, enabling high-precision, non-destructive readout suitable for neuromorphic computing.

Contribution

It introduces a mechanically read ferroelectric synapse with Lorentzian switching dynamics, linking electrical switching kinetics to mechanical weight modulation.

Findings

Achieves over 7-bit programming levels in the synapse.

Demonstrates Lorentzian distribution of switching consistent with nucleation-limited switching.

Establishes a quantitative relationship between mechanical weights and electrical switching behavior.

Abstract

Neuromorphic computing demands synaptic elements that can store and update weights with high precision while being read non-destructively. Conventional ferroelectric synapses store weights in remnant polarization states and might require destructive electrical readout, limiting endurance and reliability. We demonstrate a ferroelectric MEMS (FeMEMS) based synapse in which analog weights are stored in the piezoelectric coefficient $d_{31,eff}$ of a released Hf$_{0.5}$Zr$_{0.5}$O$_2$ (HZO) MEMS unimorph. Partial switching of ferroelectric domains modulates $d_{31,eff}$, and a low-amplitude mechanical drive reads out the weight without read-disturb in the device yielding more than 7-bit of programming levels. The mechanical switching distribution function follows a Lorentzian distribution as a logarithmic function of partial poling voltage ($V_p$) consistent with nucleation-limited switching (NLS), and the median threshold extracted from electromechanical data obeys a Merz-type field-time law with a dimensionless exponent $\alpha = 3.62$. These relationships establish a quantitative link between mechanical weights and electrical switching kinetics. This mechanically read synapse avoids depolarization and charge-injection effects, provides bipolar weights (well suited for excitatory and inhibitory synapses), directly reveals partial domain populations, and offers a robust, energy-efficient route toward high-bit neuromorphic hardware.