Kavli Affiliate: Grace Xing
| First 5 Authors: Shubham Jadhav, Shubham Jadhav, , ,
| Summary:
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.
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