For a wearable device to distinguish walking from a brief swing of the arm, it must recognize how a movement changes over time. Korean researchers have developed a technology that stacks semiconductor devices with different response speeds, allowing them to recognize both a change that has just occurred and the changes before it. The technology could be applied to small devices that are worn on or attached to the body to analyze movement and physiological signals.

KAIST (President Choongsik Bae) announced on September 29 that a research team led by Professor Jimin Kwon from the Department of AI Systems, together with researchers from UNIST (President Chong Rae Park) and POSTECH (President Seong Keun Kim), has made a solid thin film from a material that contains ions and used it to stack semiconductor devices with different response speeds in multiple layers.

In these devices, applying a voltage moves the ions within the material and changes the flow of current. When the voltage is removed, the ions require time to return to their original state. As a result, the effect of previous signals remains in the device for a short time. This slow movement has generally been considered a disadvantage, but the research team used it to identify earlier inputs.

The main challenge was controlling how long this effect lasts. Until now, such materials have mostly been liquids or soft gels, which made it difficult to fabricate the devices precisely or to stack them in multiple layers.

To address this challenge, the team made the material into a solid thin film, known as a solid-state ionogel, and adjusted the amount of ions in the film as well as its thickness. This allowed them to create devices that respond to signals either quickly or slowly. When the two types were stacked, one layer responded to the most recent signal, while the other responded to signals that had accumulated over a longer period.

The researchers confirmed that the devices could distinguish all 16 patterns created when four consecutive input signals are turned on or off in different combinations. Using the responses measured from the devices, they also built a simulation model of a system that classifies videos of moving handwritten digits. The model achieved validation accuracies above 90% for sequences of moving images played at different speeds.

The team fabricated the devices on both 4-inch wafers and flexible substrates. Their electrical characteristics remained stable for 55 months after fabrication.

The results could be used to develop technologies for processing information that changes over time, such as movement and signals from the body, directly on small devices. Further validation is needed to determine how the technology performs, and how much power it saves, in actual smartwatches.

"These devices can be fabricated on large-area substrates using existing thin-film semiconductor processes, and they can also be stacked in multiple layers," said Professor Kwon. He added that the technology could be developed into AI chips that analyze movement and physiological signals for devices that must operate with low power consumption, such as smartwatches.

Research Professor Haksoon Jung from KAIST is the first author of the study, and Professor Yong-Young Noh of POSTECH and Professor Jimin Kwon of KAIST are the co-corresponding authors. The findings were published in Advanced Materials on June 30.

※ Paper title: "Monolithic 3D-Integrated All-Solid Ion-Gated Carbon Nanotube Transistors With Tunable Ionic Conductance for Multi-Timescale Reservoir Computing," DOI: 10.1002/adma.202523703

This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF), funded by the Korean government (Ministry of Science and ICT, MSIT) (RS-2023-00281195 and RS-2024-00355384), and by the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) (RS-2024-00417909).