SeoulTech Scientist Develops Artificial Synapses That Mimic Human Brain Function for Next-Gen AI Chips

The innovative organic materials proposed by Dr. Eunho Lee and his team bring us closer to brain-like computer processors

In the Materials Horizons Emerging Investigator Series, Dr. Eunho Lee from SeoulTech's research highlights how designing organic semiconductors with glycol side chains can improve artificial synapses by enabling more efficient, bulk-mediated ion transport. This work offers a new approach for developing advanced neuromorphic computing hardware.

SEOUL, South Korea, Sept. 3, 2025 /PRNewswire/ -- The Emerging Investigator Series by the journal Materials Horizons features outstanding work by young researchers in the field of materials science. In the latest Editorial article of the Series, Dr. Eunho Lee, an Assistant Professor of Chemical and Biomolecular Engineering at Seoul National University of Science and Technology, Republic of Korea, where he leads the Functional Semiconductors and Devices Lab, discusses and elaborates upon his Emerging Investigator Series research paper titled "Improving ion uptake in artificial synapses through facilitated diffusion mechanisms" (10.1039/D5MH00005J). The interview was published in Issue 14 of Materials Horizons on 16 June 2025.

Dr. Lee explains: "Our research shows a simple way to make the next wave of AI hardware more efficient by improving electrolyte-based organic transistors, which are soft, low-voltage devices that process signals with ions as well as electrons. A long-standing bottleneck has been doping efficiency: how effectively ions can enter and leave the polymer channel to switch the device. We addressed this by engineering the polymer's side chains so they actively attract and guide ions, like molecular 'handles' and 'lanes,' leading to faster and deeper ion uptake."

The materials developed by Dr. Lee and his team point to practical uses in both AI hardware and bio-based interfaces. On the AI side, electrolyte-based organic transistors with diffusion-driven doping can function as analog synapses for ultra-low-power co-processors in wearables, cameras, and IoT nodes, enabling always-on sensing, in-sensor preprocessing, and adaptive learning with minimal energy. The same design rules also support hybrid integration with CMOS so that compact analog memory arrays reduce data movement and latency.

On the biology side, the soft and ion-friendly operation is well suited to skin and tissue environments, suggesting stable bioelectronic interfaces for closed-loop therapies and electrochemical biosensors that not only detect biomarkers but also classify patterns locally. Beyond health, these devices could support environmental monitors for water quality and point-of-care diagnostics that combine electrochemical readout with small on-board learning. In short, controlling ion motion at the molecular level enables safer, lower-voltage systems that learn at the edge.

"The ability to control ion motion in soft semiconductors could reshape how and where we run AI," remarks Dr. Lee. This novel design strategy offers a general approach for energy-efficient, adaptive hardware that complements digital silicon, potentially reducing AI's energy footprint and keeping sensitive data local.

Reference
Title of original paper: Materials Horizons Emerging Investigator Series: Dr Eunho Lee, Seoul National University of Science and Technology, Korea (the Republic of)

Journal: Materials Horizons 

DOI: 10.1039/d5mh90071a

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