# Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network

arXiv: https://arxiv.org/abs/2103.03111 · HTML: https://mlsanity.com/paper/2103.03111/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories](https://mlsanity.com/paper/2011.07095/) (2020)
- [Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization](https://mlsanity.com/paper/2007.13489/) (2020)

## Built on by (most cited first, 1 of 1)

- [Variation Aware Training of Hybrid Precision Neural Networks with 28nm HKMG FeFET Based Synaptic Core](https://mlsanity.com/paper/2202.10912/) (2022)

## Abstract (arXiv)

This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (N.N.) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, a compact model captured the conductance drift of a programmed cell over a wide range of gate biases. We observed a significant inference accuracy degradation in the analog neural network at 233 K for an N.N. trained at 300 K. Finally, we deployed binary neural networks with "read voltage" optimization to ensure immunity of N.N. to accuracy degradation under temperature variation, maintaining an inference accuracy of 96%. Keywords: Ferroelectric memories