December 13, 2023

Photolithography involves manipulating light to precisely etch features onto a surface, and is commonly used to fabricate computer chips and optical devices like lenses. But tiny deviations during the manufacturing process often cause these devices to fall short of their designers’ intentions.

To help close this design-to-manufacturing gap, researchers from MIT and the Chinese University of Hong Kong used machine learning to build a digital simulator that mimics a specific photolithography manufacturing process. Their technique utilizes real data gathered from the photolithography system, so it can more accurately model how the system would fabricate a design.

The researchers integrate this simulator into a design framework, along with another digital simulator that emulates the performance of the fabricated device in downstream tasks, such as producing images with computational cameras. These connected simulators enable a user to produce an optical device that better matches its design and reaches the best task performance.

This technique could help scientists and engineers create more accurate and efficient optical devices for applications like mobile cameras, augmented reality, medical imaging, entertainment, and telecommunications. And because the pipeline of learning the digital simulator utilizes real-world data, it can be applied to a wide range of photolithography systems.

Complete article from MIT News.