A research team from NUS has made a significant breakthrough in neuromorphic hardware by creating ultra-thin 2D memtransistor arrays with unprecedented uniformity and precision, paving the way for scalable, energy-efficient AI accelerators.
Figure shows (a) Schematic of the MoS2 memtransistor structure, highlighting the selectively treated and untreated areas for Schottky barrier modulation. (b) Optical images of the fabricated memtransistor chip, demonstrating large-scale integration with 12 × 6 arrays. (c) Optical images of an array with a channel length and electrode width of 500 nm. Image Credit: National University of Singapore
Researchers at the National University of Singapore (NUS) have fabricated ultra-thin memtransistor arrays from two-dimensional (2D) transition metal dichalcogenide (TMDC) with controllable Schottky barriers. These arrays exhibit high uniformity, demonstrating low device-to-device variation and enabling high performance for image recognition tasks.
Memtransistors enable neuromorphic computing
Memtransistors are electronic devices that integrate data storage and signal processing within a single unit, making them attractive for compact, energy-efficient neuromorphic computing. However, practical artificial neural networks (ANNs) require large arrays of these devices, and achieving a high resistive switching ratio, consistent behaviour across devices, and scalability remains challenging. In 2D TMDC materials, atomic imperfections in the crystal tend to form randomly, affecting the device uniformity and scalability. Additionally, the non-uniform vacancy migration behaviour further exacerbates significant device-to-device variability and results in poor fabrication yield. These effects obscure the underlying switching mechanisms and introduce significant performance variability across the array.
Precise fabrication using oxygen-assisted vacancy control
A research team led by Professor CHEN Wei from the NUS Department of Physics and the Department of Chemistry, fabricated ultra-thin molybdenum disulfide (MoS2) memtransistor arrays with a 500 nm channel length and demonstrated precise Schottky barrier modulation. By carefully exposing selected areas to oxygen, they created and controlled a small number of sulfur vacancies, which allowed them to fine-tune the contact barriers so that electricity flows through the devices in a precise and predictable way. This research work was carried out in collaboration with Dr JIN Tengyu from Shanghai University, China.
Published in Nature Communications
The research findings were published in the scientific journal Nature Communications.
Exceptional switching performance and device uniformity
The fabricated memtransistors switch very clearly between the "on" and "off" states, changing the electrical current by about 10,000 times, and up to 100,000 times with gate modulation. The array is also extremely small, with each device having a 500 nm long channel length and a cell size as small as 4.65 F2. The devices performed consistently across the array, with less than 6.8% variation from one device to another. The fabrication yield can reach 100%, indicating high uniformity and reliability. When these chips are used to build an artificial neural network for image recognition tasks, they achieve over 98 percent accuracy in recognising and classifying pictures.
Versatile fabrication strategy for 2D materials
One of the authors, Dr HOU Xiangyu, said, "This memtransistor fabrication approach is also applicable to mechanically exfoliated molybdenum disulfide and molybdenum ditelluride, indicating a versatile fabrication strategy for building 2D TMDC memtransistors."
Future integration with advanced AI architectures
"Looking forward, integrating this approach with advanced fabrication techniques, multi-layer stacking, or hybrid CMOS-2D architectures could further enhance device performance and enable large-scale, energy-efficient AI accelerators," added Prof CHEN.
Source:
Journal reference:
- Hou, X., Zhang, W., Duan, S., Jin, T., Geng, X., Lin, M., Cai, Y., Mao, J., Luo, Y., Zhu, J., Lin, J., & Chen, W. (2025). Scalable transition metal dichalcogenide memtransistor arrays with Schottky-barrier control for energy-efficient artificial neural networks. Nature Communications, 16(1), 1-12. DOI: 10.1038/s41467-025-64579-5, https://www.nature.com/articles/s41467-025-64579-5
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