AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data that changes at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots and wearable devices.
A research team led by Chair Professor Shinhyun Choi from the School of Electrical Engineering and the Graduate School of Semiconductor Technology has developed a programmable dynamic memtransistor (PDM), a semiconductor device whose time-response characteristics can be set to and retain multiple states, as well as an integrated array based on the device.
The research is published in the journal Nature Communications.
A memtransistor is a next-generation semiconductor device that combines the information storage function of memory with the computing function of a transistor. In the PDM, the ability to process data while retaining previous information allows its response characteristics to be adjusted and retained for incoming data.
Today’s computers and smartphones require complex software processing to analyze data that changes over time, resulting in large computational loads and high power consumption. To address this, researchers have been studying technologies that allow semiconductor hardware itself to process data directly. However, conventional devices have fixed response speeds that cannot be changed once the device is fabricated.
The research team overcame this limitation by introducing a dual-layer structure inside the transistor, combining a charge storage layer that accumulates and processes data with an electron trapping layer that controls the response speed in a nonvolatile manner.
In the PDM developed by the research team, incoming data is processed in the charge storage layer, while the electron trapping layer controls, across multiple levels, the recovery speed at which the semiconductor returns to its original state. In experiments, the team succeeded in tuning the current recovery time over an approximately 5-fold range and the characteristic frequency over a range of more than 10-fold.
In particular, in experiments involving the prediction of data in which fast and slow changes are intricately mixed, the PDM reduced prediction errors by as much as 40-fold compared with conventional fixed-response semiconductor devices.
The PDM enables accurate information processing even when handwriting or object movement speeds vary by using response characteristics configured to match different input timescales. Once the response characteristics are set, the device remembers them without requiring a continuous external power supply, and it does not require complex preprocessing of input data. Because it is fully compatible with materials used in widely adopted commercial semiconductor processes, it is also highly advantageous for mass production and commercialization.
The research team, from left: Dae-won Kim, the first author, KAIST Graduate School of Semiconductor Technology Ph.D. candidate; Chair Professor Shinhyun Choi served, the corresponding author. Credit: KAIST
The research team fabricated a PDM array and used it to predict complex data, confirming that it achieved accuracy comparable to that of conventional software-based systems while consuming far less energy.
“This study demonstrates an AI semiconductor whose response characteristics can be programmed to efficiently process data changing at different speeds,” said Choi. “We expect it to become a core technology that improves the performance of AI devices such as autonomous vehicles, robots and wearables while reducing their power consumption.” https://techxplore.com/news/2026-08-chameleon-chip-errors.html
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