TY - JOUR
T1 - Sub-2 nm Equivalent-Oxide-Thickness Ferroelectric Transistors for Cryogenic Memory and Computing
AU - Das, Apu
AU - Senapati, Asim
AU - Kumar, Gautham
AU - Lou, Zhao Feng
AU - Müller, Jonas
AU - Maskeen, Jaskirat Singh
AU - Chang, Yii Tay
AU - Tewari, Mohit
AU - Agarwal, Ankit
AU - Paul, Agniva
AU - Raffel, Yannick
AU - Maikap, Siddheswar
AU - Kao, Kuo Hsing
AU - Agarwal, Tarun
AU - Lashkare, Sandip
AU - Lu, Darsen
AU - Larrieu, Guilhem
AU - Lee, Min Hung
AU - De, Sourav
N1 - Publisher Copyright:
© 2026 The Authors. Published by American Chemical Society
PY - 2026/4/14
Y1 - 2026/4/14
N2 - 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.
AB - 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.
KW - 4D-STEM
KW - FeFETs
KW - HZO
KW - XPS
KW - cryogenic electronics
KW - neuromorphic computing
KW - nonvolatile memory
UR - https://www.scopus.com/pages/publications/105035697477
UR - https://www.scopus.com/pages/publications/105035697477#tab=citedBy
U2 - 10.1021/acsnano.5c16255
DO - 10.1021/acsnano.5c16255
M3 - Article
C2 - 41914654
AN - SCOPUS:105035697477
SN - 1936-0851
VL - 20
SP - 10905
EP - 10918
JO - ACS Nano
JF - ACS Nano
IS - 14
ER -