Photolithography includes manipulating gentle to exactly etch options onto a floor, and is often used to manufacture laptop chips and optical devices like lenses. But tiny deviations throughout the manufacturing course of usually trigger these devices to fall wanting their designers’ intentions.
To assist shut this design-to-manufacturing gap, researchers from MIT and the Chinese University of Hong Kong used machine studying to construct a digital simulator that mimics a particular photolithography manufacturing course of. Their approach makes use of actual knowledge gathered from the photolithography system, so it could extra precisely mannequin how the system would fabricate a design.
The researchers combine this simulator right into a design framework, together with one other digital simulator that emulates the efficiency of the fabricated system in downstream duties, reminiscent of producing photographs with computational cameras. These linked simulators allow a consumer to provide an optical system that higher matches its design and reaches the greatest activity efficiency.
This approach may assist scientists and engineers create extra correct and environment friendly optical devices for purposes like cellular cameras, augmented actuality, medical imaging, leisure, and telecommunications. And as a result of the pipeline of studying the digital simulator makes use of real-world knowledge, it may be utilized to a variety of photolithography programs.
“This idea sounds simple, but the reasons people haven’t tried this before are that real data can be expensive and there are no precedents for how to effectively coordinate the software and hardware to build a high-fidelity dataset,” says Cheng Zheng, a mechanical engineering graduate scholar who’s co-lead creator of an open-access paper describing the work. “We have taken risks and done extensive exploration, for example, developing and trying characterization tools and data-exploration strategies, to determine a working scheme. The result is surprisingly good, showing that real data work much more efficiently and precisely than data generated by simulators composed of analytical equations. Even though it can be expensive and one can feel clueless at the beginning, it is worth doing.”
Zheng wrote the paper with co-lead creator Guangyuan Zhao, a graduate scholar at the Chinese University of Hong Kong; and her advisor, Peter T. So, a professor of mechanical engineering and organic engineering at MIT. The analysis can be introduced at the SIGGRAPH Asia Conference.
Printing with gentle
Photolithography includes projecting a sample of sunshine onto a floor, which causes a chemical response that etches options into the substrate. However, the fabricated system finally ends up with a barely totally different sample due to miniscule deviations in the gentle’s diffraction and tiny variations in the chemical response.
Because photolithography is advanced and onerous to mannequin, many present design approaches depend on equations derived from physics. These basic equations give some sense of the fabrication course of however can’t seize all deviations particular to a photolithography system. This may cause devices to underperform in the actual world.
For their approach, which they name neural lithography, the MIT researchers construct their photolithography simulator utilizing physics-based equations as a base, after which incorporate a neural community educated on actual, experimental knowledge from a consumer’s photolithography system. This neural community, a kind of machine-learning mannequin loosely primarily based on the human mind, learns to compensate for lots of the system’s particular deviations.
The researchers collect knowledge for their technique by producing many designs that cowl a variety of function configurations and dimensions, which they fabricate utilizing the photolithography system. They measure the ultimate constructions and examine them with design specs, pairing these knowledge and utilizing them to coach a neural community for their digital simulator.
“The performance of learned simulators depends on the data fed in, and data artificially generated from equations can’t cover real-world deviations, which is why it is important to have real-world data,” Zheng says.
Dual simulators
The digital lithography simulator consists of two separate elements: an optics mannequin that captures how gentle is projected on the floor of the system, and a resist mannequin that reveals how the photochemical response happens to provide options on the floor.
In a downstream activity, they join this discovered photolithography simulator to a physics-based simulator that predicts how the fabricated system will carry out on this activity, reminiscent of how a diffractive lens will diffract the gentle that strikes it.
The consumer specifies the outcomes they need a tool to realize. Then these two simulators work collectively inside a bigger framework that reveals the consumer how one can make a design that may attain these efficiency objectives.
“With our simulator, the fabricated object can get the best possible performance on a downstream task, like the computational cameras, a promising technology to make future cameras miniaturized and more powerful. We show that, even if you use post-calibration to try and get a better result, it will still not be as good as having our photolithography model in the loop,” Zhao provides.
They examined this system by fabricating a holographic component that generates a butterfly picture when gentle shines on it. When in comparison with devices designed utilizing different strategies, their holographic component produced a near-perfect butterfly that extra intently matched the design. They additionally produced a multilevel diffraction lens, which had higher picture high quality than different devices.
In the future, the researchers wish to improve their algorithms to mannequin extra sophisticated devices, and likewise take a look at the system utilizing client cameras. In addition, they wish to develop their strategy so it may be used with various kinds of photolithography programs, reminiscent of programs that use deep or excessive ultraviolet gentle.
This analysis is supported, partly, by the U.S. National Institutes of Health, Fujikura Limited, and the Hong Kong Innovation and Technology Fund.