The Korea Institute of Machinery and Materials (KIMM) said Wednesday that a team led by senior researcher Kim Hyeong-woo had combined low-temperature plasma processing with machine-learning analysis to synthesize and etch six-inch wafers of 2D semiconductors such as molybdenum disulfide and tungsten disulfide.
Conventional 2D chip fabrication relies on high-temperature methods that strain substrates, limit compatibility with existing production lines and make uniform large-area output difficult.
KIMM's approach operates at about 150 degrees Celsius, minimizing thermal damage while enabling single-step, atomic-layer etching that lifts both efficiency and yield.
The team captured real-time optical and gas-mass data using diagnostic tools including optical emission spectroscopy and time-of-flight mass spectrometry, then fed the readings into machine-learning models to predict film thickness down to the atomic layer.
The sensors tap into existing equipment view ports, allowing the technology to be deployed without modifying production machinery.
"This research is meaningful in that it implemented a six-inch wafer-scale 2D semiconductor process at the atomic-layer level in a low-temperature environment," Kim said, adding that the data-driven method had markedly improved process reproducibility and productivity.
The institute said the technology could be applied across AI chips, next-generation electronic devices and displays, and may evolve into a core tool for autonomous, intelligent semiconductor manufacturing as process data accumulates.
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