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A synergistic approach for optimizing devices, circuits, and neural network architectures was used to abate junction-temperature-change-induced performance degradation of an Fe-FinFET-based artificial neural network. We demonstrated that the digital nature of the binarized neural network, with the &#x201C;0&#x201D; state programmed deep in the subthreshold and the &#x201C;1&#x201D; state in strong inversion, is crucial for robust deep neural network inference. The performance of a purely software-based binary neural network (BNN), with 96.1&#x0025; accuracy for Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition, was used as a baseline. The Fe-FinFET-based BNN (including device-to-device variation at 300 K) achieved 95.7&#x0025; inference accuracy on the MNIST dataset. Although substantial inference accuracy degradation with temperature change was observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as synaptic devices had excellent resistance to temperature change effects, and maintained a minimum inference accuracy of 95.2&#x0025; within a temperature range of &#x2212;40 to 125 &#x00B0;C after gate stack and bias optimization. However, reprogramming to adjust device conductance was necessary for temperatures higher than 125 &#x00B0;C.

Robust Binary Neural Network Operation From 233 K to 398 K via Gate Stack and Bias Optimization of Ferroelectric FinFET Synapses