Skip to main navigation Skip to search Skip to main content

Sub-2 nm Equivalent-Oxide-Thickness Ferroelectric Transistors for Cryogenic Memory and Computing

  • Apu Das
  • , Asim Senapati
  • , Gautham Kumar
  • , Zhao Feng Lou
  • , Jonas Müller
  • , Jaskirat Singh Maskeen
  • , Yii Tay Chang
  • , Mohit Tewari
  • , Ankit Agarwal
  • , Agniva Paul
  • , Yannick Raffel
  • , Siddheswar Maikap
  • , Kuo Hsing Kao
  • , Tarun Agarwal
  • , Sandip Lashkare
  • , Darsen Lu*
  • , Guilhem Larrieu*
  • , Min Hung Lee*
  • , Sourav De*
  • *Corresponding author for this work
  • National Tsing Hua University
  • National Taiwan University
  • Toulouse University
  • Indian Institute of Technology Gandhinagar
  • National Cheng Kung University
  • Fraunhofer Institute for Photonic Microsystems

Research output: Contribution to journalJournal Article peer-review

Abstract

Ferroelectric hafnia-based field-effect transistors are promising candidates for nonvolatile memory and in-memory computing. However, their operation principle under deep-cryogenic conditions at aggressively scaled gate stacks remains underexplored, especially for bulk silicon technology. This work presents an experimental demonstration of front-end-of-line bulk silicon-channel ferroelectric field-effect transistors featuring sub-2 nm equivalent-oxide-thickness gate stacks with ≃5 nm hafnium–zirconium oxide, exhibiting robust switching at 10 K. Key metrics include memory windows exceeding 1 V, tightly distributed threshold voltages (standard deviation ≲ 40 mV), endurance surpassing 107 cycles, and retention projections consistent with decade-scale stability. Correlative four-dimensional scanning transmission electron microscopy phase mapping reveals an increased orthorhombic ferroelectric fraction following electrical wake-up at cryogenic temperatures, correlated with enhanced polarization stability and strengthened oxygen–metal coordination. We hypothesize that suppressed trapping-related instability, along with a higher orthorhombic phase, jointly contribute to this effect. Current–voltage sweeps define an operational design window, with memory-window saturation beyond ±5 V programming voltages and ≳900 ns pulse widths, consistent with nucleation-limited reversal kinetics in ultrathin films. A spiking neural network implemented at 10 K achieves >92% classification accuracy on MNIST and 73.8% accuracy on NMNIST data sets, demonstrating practical utility. These findings provide materials- and device-level insights into scaled hafnia FeFETs for energy-efficient cryogenic applications, including potential integration in quantum–classical systems.

Original languageEnglish
Pages (from-to)10905-10918
Number of pages14
JournalACS Nano
Volume20
Issue number14
DOIs
StatePublished - 14 04 2026

Bibliographical note

Publisher Copyright:
© 2026 The Authors. Published by American Chemical Society

Keywords

  • 4D-STEM
  • FeFETs
  • HZO
  • XPS
  • cryogenic electronics
  • neuromorphic computing
  • nonvolatile memory

Fingerprint

Dive into the research topics of 'Sub-2 nm Equivalent-Oxide-Thickness Ferroelectric Transistors for Cryogenic Memory and Computing'. Together they form a unique fingerprint.

Cite this