SK hynix Inc. and Gauss Labs announced today that they participated in the SPIE AL 2024, an international conference held in San Jose, California, and presented two papers based on the latest technology for AI-based metrology. SK hynix has been collaborating closely with Gauss Labs in various areas to increase semiconductor yield and productivity, and the results of this collaboration are published in these two papers.

In the paper "Model Aggregation for Virtual Metrology for High-Volume Manufacturing," Gauss Labs introduces "aggregated AOM", an algorithm that increases the prediction accuracy of its AI-based virtual metrology solution, Panoptes VM (Virtual Metrology). Since its adoption in December 2022, SK hynix used Panoptes VM to conduct virtual measurements on more than 50 million wafers so far, which translates to more than one wafer per second. The company was able to improve process variability by 29% thanks to this technology.The latest algorithm by Gauss Labs, the aggregated AOM, pools processing machines, chambers, etc. that share the same pattern and models them together. According to Gauss Labs, this solves the challenge of data shortage while further increasing the prediction accuracy. In its second paper, "Universal Denoiser to Improve Metrology Throughput," Gauss Labs introduces a "universal denoiser", which removes random variations (noise) from CD-SEM images. Measurements from CD-SEM images are taken at nanometer scale, so it is extremely important to remove noise in order to take measurements accurately.

Gauss Labs's universal denoiser uses AI to remove noise from various types of images at once. Through a series of extensive tests with SK hynix, Gauss Labs observed image acquisition time reduced to as much as ¼, compared to conventional technology. Gauss Labs states that this technology is expected to improve the productivity of metrology equipment by 42%.

Mike Kim, CEO of Gauss Labs, said that his company is working on research and development for applications of industrial AI in real-world semiconductor manufacturing fabs. "We will continue to launch innovative AI-based solutions to revolutionize manufacturing."

Authors
1. Minsuk Shina, Minju Junga, Simon Zabrockia, Doh-Hyung Rob, Hyeon-Kyeong Jeongb, Dongkyun Yim a, "Model aggregation for virtual metrology in high-volume manufacturing," SPIE Advanced Lithography + Patterning (2024)

2. Yonghyun Kima, Seyun Kima, Hoon Byuna, Sang-Gil Parka, Tae Jong Leeb, Seong Il Leeb, Min Woo Kangb, Il Koo Kima, "Universal denoiser to improve metrology throughput," SPIE Advanced Lithography + Patterning (2024)