Sub-2 nm Equivalent-Oxide-Thickness Ferroelectric transistors for cryogenic memory and computing
Source
ACS Nano
ISSN
1936-0851
Date Issued
2026-03-01
Author(s)
Das, Apu
Senapati, Asim
Kumar, Gautham
Lou, Zhao-Feng
M�ller, Jonas
Maskeen, Jaskirat Singh
Chang, Yii-Tay
Tewari, Mohit
Agarwal, Ankit
Paul, Agniva
Raffel, Yannick
Maikap, Siddheswar
Kao, Kuo-Hsing
Lu, Darsen
Larrieu, Guilhem
Lee, Min-Hung
De, Sourav
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.
Subjects
FeFETs
HZO
Cryogenic Electronics
Nonvolatile Memory
Neuromorphic Computing
4D-STEM
XPS
