Tunable Non-Volatile Gate-to-Source/Drain Capacitance of FeFET for Capacitive Synapse

Tae Hyeon Kim, Omkar Phadke, Yuan Chun Luo, Halid Mulaosmanovic, Johannes Mueller, Stefan Duenkel, Sven Beyer, Asif Islam Khan, Suman Datta, Shimeng Yu

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Using 'capacitive' crossbar arrays for compute-in-memory (CIM) offers higher energy efficiency compared to 'resistive' crossbar arrays. The non-volatile capacitive (nvCap) synapse has been recently reported in MFM and MFS stacks with asymmetric electrode interfaces. In this work, a foundry FeFET is leveraged as the capacitive synapse for the first time, where the tunable gate-to-drain and gate-to-source capacitances (Cgd and Cgs) are exploited as the capacitive memory states. High capacitance on/off ratio (25) is obtained by relatively low program/erase voltages (±3.5V). Furthermore, it is demonstrated by TCAD simulation that the physical origins of on-state and off-state capacitance are dominated by the inversion capacitance and the overlap capacitance, respectively. Based on these results, design guidelines are presented to further increase the on/off ratio.

Original languageEnglish
Pages (from-to)1628-1631
Number of pages4
JournalIEEE Electron Device Letters
Volume44
Issue number10
DOIs
StatePublished - 1 Oct 2023

Keywords

  • compute-in-memory
  • ferroelectric field-effect transistor
  • Non-volatile capacitive synapse

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