Researchers from MIT and the Chinese University of Hong Kong have developed a technique called neural lithography, using real-world data to build a photolithography simulator that can more accurately model the manufacturing process of optical devices. This approach could lead to the creation of more efficient optical devices for various applications.
Closing the Design-to-Manufacturing Gap for Optical Devices with AI
Photolithography, a crucial process for fabricating computer chips and optical devices, often falls short due to tiny deviations during manufacturing. Researchers from MIT and the Chinese University of Hong Kong have utilized machine learning to address this gap.
Neural Lithography: Bridging the Gap
Their technique, called neural lithography, involves building a digital simulator using physics-based equations as a base and incorporating a neural network trained on real data from a photolithography system. This approach enables the creation of more accurate and efficient optical devices for various applications.
Dual Simulators for Optimal Performance
The digital lithography simulator consists of two components: an optics model and a resist model. These simulators work together to help users achieve the desired outcomes for their devices.
Practical Applications and Future Enhancements
The researchers have successfully tested their technique by fabricating elements like holographic images and diffraction lenses, demonstrating significant improvements over traditional methods. They aim to further enhance their algorithms and expand their approach to different types of photolithography systems in the future.
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