Scalable Independently Tunable MoS2 Memtransistor via Contact-Engineering for Cardiac Signal Processing

Abstract Reliable and scalable neuromorphic computing demands memristive devices with deterministic analog tunability. While vertical structures enable high integration density, planar architectures are essential for complementary metal-oxide-semiconductor (CMOS) compatible memtransistor platforms governed by lateral charge transport. However, resistive switching in widely reported memristive devices is dominated by nucleation or filament-driven conduction between electrodes, constraining analog tunability, reproducibility, and scalability. Here, we demonstrate charge-transfer based contact engineering strategy that enables device-level, independently tunable deterministic resistive switching through controlled interfacial charge trapping/detrapping. Dynamic modulation of the metal-semiconductor Schottky barrier by interfacial trap states enables device-independent, nucleation-free, and gate-free resistive switching with a ∼102 ratio. Temperature-dependent transport exhibits linear Arrhenius behavior of ln (ID/T3/2) vs. 1/T with bias dependent slopes, confirming thermally activated, interface-limited conduction. Under gate-free operation, continuous drain current modulation supports reliable synaptic functions, including long-term potentiation/depression and spike-time-dependent-plasticity. Cardiac signal processing is further validated through ECG arrhythmia classification using the MIT–BIH database.

Authors

Institutions

Publication Details

Journal
ACS Nano
Published
2026-10-03
DOI
https://doi.org/10.1021/acsnano.6c10356
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Scalable Independently Tunable MoS2 Memtransistor via Contact-Engineering for Cardiac Signal Processing

Masahiro Sakai, Kai Qi, Daisuke Kiriya, Norifumi Fujimura et al.
ACS Nano
Advanced Memory and Neural Computing
article

Scalable Independently Tunable MoS2 Memtransistor via Contact-Engineering for Cardiac Signal Processing

Masahiro Sakai, Kai Qi, Daisuke Kiriya, Norifumi Fujimura, Bang Lu, Satoru Takakusagi, Durgadevi Elamaran, Vincent Tung, Takeshi Yoshimura, Ryoichiro Naoi, Rafika Amalia Annur, Liu Huiqin, Takashi Kobayashi, Soma Sakota
article en

Abstract

Abstract Reliable and scalable neuromorphic computing demands memristive devices with deterministic analog tunability. While vertical structures enable high integration density, planar architectures are essential for complementary metal-oxide-semiconductor (CMOS) compatible memtransistor platforms governed by lateral charge transport. However, resistive switching in widely reported memristive devices is dominated by nucleation or filament-driven conduction between electrodes, constraining analog tunability, reproducibility, and scalability. Here, we demonstrate charge-transfer based contact engineering strategy that enables device-level, independently tunable deterministic resistive switching through controlled interfacial charge trapping/detrapping. Dynamic modulation of the metal-semiconductor Schottky barrier by interfacial trap states enables device-independent, nucleation-free, and gate-free resistive switching with a ∼102 ratio. Temperature-dependent transport exhibits linear Arrhenius behavior of ln (ID/T3/2) vs. 1/T with bias dependent slopes, confirming thermally activated, interface-limited conduction. Under gate-free operation, continuous drain current modulation supports reliable synaptic functions, including long-term potentiation/depression and spike-time-dependent-plasticity. Cardiac signal processing is further validated through ECG arrhythmia classification using the MIT–BIH database.

ACS Nano
Hokkaido University (JP), Osaka Metropolitan University (JP), The University of Tokyo (JP)
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.