Details

Autor(en) / Beteiligte

• Yousuf, Osama
• Hossen, Imtiaz
• Glasmann, Andreu
• Najmaei, Sina
• Adam, Gina C.
• Friedman, Joseph S.
• Liu, Siting
• Eshraghian, Jason
• Kemp, Neil
• Chang, Meng-Fan (Marvin)
• Strachan, John Paul
• Mikolajick, Thomas
• Messaris, Ioannis
• Ziegler, Martin
• Koitzsch, Christian
• Ascoli, Alon
• Corinto, Fernando
• Ntinas, Vasileios
• Mögel, Andreas
• Tahoori, Mehdi B.
• Menzel, Stephan
• Tetzlaff, Ronald

Titel

Neural Network Modeling Bias for Hafnia-based FeFETs

Ist Teil von

• Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, 2023, p.1-5

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2023

Link zum Volltext

Quelle

ACM Digital Library

Beschreibungen/Notizen

• Modeling bias – the difference between the test accuracy obtained by a reference network prototype and a simulated model of that prototype – is explored in the context of hafnia-based ferroelectric field effect transistor (FeFET) devices. Device operating conditions are investigated as a parameter for mitigating the impact of device-to-device variability on the underlying network performance. The computational framework includes a physics-based compact model with artificial variance to sample device data and a multivariate Kriging model to create jump table device models; this framework is a fast and efficient technique to model device populations for realistic neural network simulations. Neural network simulations elucidate optimal operating conditions for practical implementation of FeFET neuromorphic circuits. Future work will include experimental verification and performance comparison against other neuromorphic emerging-memory technologies such as resistive random-access memory (ReRAM) in terms of neural network performance.