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
- Masahiro Sakai (ORCID: https://orcid.org/0000-0003-4484-8516)
- Kai Qi
- Daisuke Kiriya (ORCID: https://orcid.org/0000-0003-0270-3888)
- Norifumi Fujimura (ORCID: https://orcid.org/0000-0002-9204-5024)
- Bang Lu (ORCID: https://orcid.org/0009-0005-4673-5699)
- Satoru Takakusagi (ORCID: https://orcid.org/0000-0002-4095-172X)
- Durgadevi Elamaran
- Vincent Tung (ORCID: https://orcid.org/0000-0003-3230-0932)
- Takeshi Yoshimura (ORCID: https://orcid.org/0000-0002-7147-4225)
- Ryoichiro Naoi
- Rafika Amalia Annur (ORCID: https://orcid.org/0009-0008-7016-5448)
- Liu Huiqin
- Takashi Kobayashi
- Soma Sakota
Institutions
- Hokkaido University (JP)
- Osaka Metropolitan University (JP)
- The University of Tokyo (JP)
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