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Impact of HKMG and FDSOI FeFET drain current variation in processing-in-memory architectures

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In this study, we analyze the impact of drain current (\( I_{{{\text{DS}}}} \)) variation in 28 nm high-K metal-gate and 22 nm fully-depleted silicon-on-insulator Ferroelectric FET devices on processing-in-memory (PIM) deep neural network (DNN) accelerators. When performing repeated read operations on several devices at various read frequencies and under various biasing and programming conditions, non-Normal variation in \( I_{{{\text{DS}}}} \) is observed. Device-circuit co-analysis is used to emulate PIM performance subject to noise when classifying images. Marginal degradation is observed in Fashion-MNIST classification accuracy using LeNet-5, and more significant degradation is observed in CIFAR-10 classification accuracy using MobileNetV2. Variation-aware training is shown to fully recover minor drops in LeNet-5 accuracy but becomes difficult for large workloads like MobileNetV2. We demonstrate that \( I_{{{\text{DS}}}} \) variation in individual FeFETs over many read cycles is not prohibitive to designing DNN accelerators with small workloads, but advanced design techniques are required to mitigate error for larger workloads.

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Data availability

The datasets generated and analyzed in this study, as well as the code which produced them, are available from the corresponding author upon reasonable request.


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A.I.K. thanks GLOBALFOUNDRIES for providing FeFET technology wafers.


This material is based on work supported by National Science Foundation (1810005).

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FeFET measurement data was collected by Nathan Eli Miller and Zheng Wang under the advisement of Asif Islam Khan. Device-circuit co-simulation was performed by Saurabh Dash and Nathan Eli Miller. Data analysis was performed primarily by Nathan Eli Miller. Saibal Mukhopadhyay advised the study and provided direction. The original manuscript was written by Nathan Eli Miller and read, reviewed and approved by all authors.

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Correspondence to Nathan Eli Miller.

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Miller, N.E., Wang, Z., Dash, S. et al. Impact of HKMG and FDSOI FeFET drain current variation in processing-in-memory architectures. Journal of Materials Research 36, 4379–4393 (2021).

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