English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

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