AI-Based Reverse Design of Processing Conditions Extends Quantum Dot QLED Lifetime by 40 Times

- Improves next-generation display performance by reverse-designing optimal solvent properties for quantum dot processing

A technology has been developed that enables artificial intelligence to reverse-design the processing conditions for quantum dot light-emitting diode (QLED) devices after extensive trial and error.

When applied to actual devices, the technology doubled efficiency and improved lifetime by more than 40 times, raising expectations that it could accelerate the development of next-generation displays.

▲ (From left) Professor Jeonghun Kwak of the ECE Department at SNU, Professor Jaehoon Lim of Sungkyunkwan University, and Ph.D. candidate Beomsoo Chun of the ECE Department at SNU

Seoul National University’s College of Engineering announced that a joint research team led by Professor Jeonghun Kwak of SNU and Professor Jaehoon Lim of Sunkyunkwan University has developed an AI-based platform that reverse-designs the optimal solvent properties needed to arrange quantum dots uniformly and densely during the fabrication of quantum dot light-emitting diodes.

This research was supported by the Future Display Strategy Research Laboratory Support Program and the Nano and Material Technology Development Program, promoted by the Ministry of Science and ICT and the National Research Foundation of Korea. The findings were published online on July 15 in Reports on Progress in Physics, a leading international journal in physics published by the Institute of Physics (IOP) in the United Kingdom.

QLEDs are devices that use quantum dots, nanometer-scale semiconductor particles, as their light-emitting layer. They are considered a promising technology for next-generation displays.

• To realize high-performance QLEDs, quantum dot particles must be arranged uniformly and densely within a thin film, much like bricks in a wall.
• The challenge is that, in this type of solution process, the brightness and lifetime of the device can vary significantly depending on which solvent is used to form the thin film.
• Because it is difficult to predict how specific solvent conditions affect device performance, researchers have largely relied on experience and repeated experiments to identify optimal conditions, resulting in significant time and cost limitations.

To address this complexity, the research team trained AI to learn the relationship between the physical properties of solvents and the structure of quantum dot thin films.

• First, the team fabricated quantum dot thin films using five representative solvents and quantified how uniformly the surfaces were formed using atomic force microscopy (AFM).*
• They then trained a machine learning model on solvent properties, including vapor pressure, viscosity, density, and dielectric constant, as well as thin-film morphology data, enabling the model to inversely predict the solvent properties that could form the most uniform quantum dot thin film.
• Although no single solvent possessed all of the optimal properties proposed by the AI, the research team realized the AI-suggested conditions by combining multiple solvents.
• These were complex conditions that would have been difficult to identify through conventional repeated experiments alone. When applied to an actual QLED fabrication process, the optimized solvent system improved efficiency by approximately two times and operating lifetime by more than 40 times compared with conventional single-solvent processing.

Professor Kwak stated, “This research demonstrates that AI can be used to design display materials and processes in a data-driven manner. We expect this approach to be applicable to the development of various next-generation electronic devices, including OLEDs and solar cells.”

▲ Figure 1. Conceptual diagram of the AI-based QLED process design platform

(Left) In a conventional process, quantum dots are arranged unevenly, which interferes with charge transport and degrades device performance. (Right) The AI-based process design platform developed by the research team predicts the optimal solvent composition, enabling a more uniform arrangement of quantum dots. QLEDs fabricated using this platform achieved a twofold improvement in efficiency and a 40-fold improvement in lifetime.

[Reference]

- Paper/Journal: Machine-learning-enabled solvent engineering for uniform quantum dot packing in efficient and stable quantum-dot light-emitting diodes, Reports on Progress in Physics, 89, 078002

- DOI: https://doi.org/10.1088/1361-6633/ae8470

- Electronic Times (2026. 07. 15) : AI가 찾아낸 최적의 QLED 공정…효율 2배·수명 40배 향상

- DongA Science (2026. 07. 15) : AI로 공정 역설계…QLED 수명 40배 늘렸다

[Contact]

Professor Jeonghun Kwak / Advanced Opto & Nano Electronics Laboratory / 02-880-1781 / jkwak@snu.ac.kr

Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57895

Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr