Bayesian Inference via GeTe _<i>x</i> ‐OTS Based Stochastic Synapse for Uncertainty‐Aware Medical Diagnostics

ABSTRACT Neuromorphic architectures leveraging stochastic device physics present a transformative approach for implementing probabilistic computing paradigms capable of intrinsic uncertainty quantification. In this work, we present a germanium‐telluride‐based ovonic threshold switch (GeTe x ‐OTS) exhibiting inherent stochastic dynamics, integrated into a compact 1‐selector‐1‐transistor‐1‐resistor (1S1T1R) synaptic unit. The OTS devices are demonstrated within an 8K‐array, confirming the future scalability for neuromorphic systems. Experimental validation on the GeTe x devices shows stable Gaussian‐distributed threshold voltage fluctuations ( σ = 100 mV), enabling precise control of synaptic activation probability through input pulse modulation. Utilizing this intrinsic stochasticity, we implement mini hardware realization of a Monte Carlo Dropconnect (MC‐Dropconnect) neural network, directly demonstrating the feasibility of the system. Applied to COVID‐19 diagnosis using chest X‐ray images, our system achieves robust uncertainty quantification through predictive entropy, improving classification accuracy to 98.1%, compared to 96.0% with a deterministic baseline. This uncertainty‐aware hardware design strategy provides a scalable pathway for implementing energy‐efficient neuromorphic systems with native uncertainty estimation capabilities.