loading page

Cross-Layer Reliability Modeling of Dual-Port FeFET: Device-Algorithm Interaction
  • +2
  • shubham kumar ,
  • swetaki Chatterjee ,
  • simon thomann ,
  • Yogesh Singh Chauhan ,
  • Hussam Amrouch
shubham kumar
University of Stuttgarrt

Corresponding Author:[email protected]

Author Profile
swetaki Chatterjee
Author Profile
simon thomann
Author Profile
Yogesh Singh Chauhan
Author Profile
Hussam Amrouch
Author Profile

Abstract

Today’s data-centric applications are incompatible with the predominant compute-centric computer architectures. The small on-chip memories of compute-centric computer architecture demand many energy-costly data transfers exposing the von-Neumann bottleneck. The Ferroelectric Field-Effect Transistor (FeFET) is an emerging Non-Volatile Memory technology enabling novel data-centric architectures that go far beyond von-Nuemann principles. FeFETs are very promising for a wide range of applications starting from on-chip memories to in-memory computing and even neuromorphic computing. Nevertheless, FeFET devices exhibit significant variations that can severely restrict their applicability. Temperature further exacerbates variation effects because it degrades ferroelectric parameters. Hence, it is indispensable to investigate and model design-time variations, run-time variations, and stochastic variations due to spatial fluctuation of ferroelectric domains under different temperatures. Dual-port FeFET has been recently proposed and demonstrated as novel structure that offers for the first time disturb-free read operation along with larger memory window (MW) compared to conventional FeFETs. However, all of the before-mentioned types of variations are amplified in such a new structure. In this work, the impact of temperature variation is analyzed for dual-port FeFETs for the first time in a cross-layer manner starting from the device level to the circuit/system levels and compared to conventional FeFET. We focus in our analysis on Hyperdimensional Computing (HDC), which is an emerging type of machine learning algorithm, that is being executed on top of FeFET-based in-memory circuits that perform efficient Hamming distance (i.e., similarity) computations. Through our cross-layer framework, we demonstrate the serious impact of variation on FeFET reliability despite the significant increase in the MW that dual-port FeFET offers. Even HDC is affected, despite its remarkable robustness against errors. All in all, our work reveals that a larger MW at the device level does not necessarily translate to benefits at the application level. Hence, investigating and modeling variability effects in a cross-layer manner is indispensable.
Jul 2023Published in IEEE Transactions on Circuits and Systems I: Regular Papers volume 70 issue 7 on pages 2891-2903. 10.1109/TCSI.2023.3265427