How GPU-accelerated simulation enables applied deep learning for masks and wafers

Pang, Liang; Niewczas, Mariusz; Meyer, Mike; Pearman, Ryan; Shendre, Abhishek; Fujimura, Aki

doi:10.1117/12.2538244

Cited by 2 publications

(2 citation statements)

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“…For example, EUV lithography has enabled the fabrication of processors with ten's of billions of transistors that have, in turn, enabled supercomputers to be built at a small fraction of the cost of prior generations of supercomputers. 55) When supercomputers cost only $100k instead of millions of dollars, entirely new markets are opened (including some that involve computational lithography 56) ). Moreover, the value of scaling has been enhanced by the use of advanced packaging, where analog and input/output circuit elements are produced using low cost lithography, while logic and memory elements are fabricated with EUV lithography, and integration is accomplished at the package level.…”

Section: Quantum Phenomena and The Limits Of Euv Lithographymentioning

confidence: 99%

Lithography in a quantum world

Levinson¹

2023

Jpn. J. Appl. Phys.

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The conceptualization of the lithography process as captured in models was long based primarily on classical physics. It is now essential to model the lithography process at the quantum level. Photon shot noise and molecular inhomogeneity in resists lead to line-edge roughness (LER) and stochastic defects. To contain photon shot noise while maintaining good scanner throughput for EUV lithography requires very powerful light sources. Resists in the future will need to be single-component, and molecular building blocks will need to be much smaller than 0.5 nm. This precludes the long-term use of chemically amplified resists for EUV lithography, at least for minimum line/space patterns. Because the radiation chemistry of EUV resists is driven by photoelectrons and secondary electrons, the range and stochastics of these electrons needs to be considered in the design of future resists. Invention and innovation in patterning materials will be needed to a greater degree than before.

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Section: Quantum Phenomena and The Limits Of Euv Lithographymentioning

confidence: 99%

Lithography in a quantum world

Levinson¹

2023

Jpn. J. Appl. Phys.

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“…Digital twins can also be used in a chain: for a DL project that requires curvilinear ILT mask SEM images to do training, one can use curvilinear ILT digital twin to generate curvilinear ILT mask patterns, then use mask SEM digital twins to generate the mask SEM images. 142…”

Section: Applying Deep Learning To Iltmentioning

confidence: 99%

Inverse lithography technology: 30 years from concept to practical, full-chip reality

Pang

2021

J. Micro/Nanopattern. Mats. Metro.

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In lithography, optical proximity and process bias/effects need to be corrected to achieve the best wafer print. Efforts to correct for these effects started with a simple bias, adding a hammer head in line-ends to prevent line-end shortening. This first-generation correction was called rule-based optical proximity correction (OPC). Then, as chip feature sizes continued to shrink, OPC became more complicated and evolved to a model-based approach. Some extra patterns were added to masks, to improve the wafer process window, a measure of resilience to manufacturing variation. Around this time, the concept of inverse lithography technology (ILT), a mathematically rigorous inverse approach that determines the mask shapes that will produce the desired on-wafer results, was introduced. ILT has been explored and developed over the last three decades as the next generation of OPC, promising a solution to several challenges of advanced-node lithography, whether optical or extreme ultraviolet (EUV). Today, both OPC and ILT are part of an arsenal of lithography technologies called resolution enhancement technologies. Since OPC and ILT both involve computation, they are also considered as part of computational lithography. We explore the background and history of ILT and detail the significant milestones that have taken full-chip ILT from an academic concept to a practical production reality.

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How GPU-accelerated simulation enables applied deep learning for masks and wafers

Cited by 2 publications

References 2 publications

Lithography in a quantum world

Lithography in a quantum world

Inverse lithography technology: 30 years from concept to practical, full-chip reality

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