Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient
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2026
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| author | Mukhopadhyay, Saumyadip Yang, Kiho Vasudevan, Kasyap Thottasserymana Divvela, Mounica Jyothi Dogru, Selim Krishnamurthy, Dilip Treska, Fergo Gillijns, Werner Kim, Ryan Ryoung han Sastry, Kumara Singh, Vivek |
| author_facet | Mukhopadhyay, Saumyadip Yang, Kiho Vasudevan, Kasyap Thottasserymana Divvela, Mounica Jyothi Dogru, Selim Krishnamurthy, Dilip Treska, Fergo Gillijns, Werner Kim, Ryan Ryoung han Sastry, Kumara Singh, Vivek |
| contents | From climate science to drug discovery, scientific computing demands have surged dramatically in recent years -- driven by larger datasets, more sophisticated models, and higher simulation fidelity. This growth rate far outpaces transistor scaling, leading to unsustainably rising costs, energy consumption, and emissions. Semiconductor manufacturing is no exception. Computational lithography -- involving transferring circuitry to silicon in diffraction-limited conditions -- is the largest workload in semiconductor manufacturing. It has also grown exceptionally complex as miniaturization has advanced in the angstrom-era, requiring more accurate modeling, intricate corrections, and broader solution-space exploration. Accelerated computing (AC) offers a solution by dramatically freeing up the compute and power envelope. AI augments these gains by serving as high-fidelity surrogates for compute-intensive steps. Together, they present a sustainable, next-generation computing platform for scientific workloads. This new paradigm needs a fundamental redesign of the software stack. For computational lithography, NVIDIA cuLitho reinvents the core primitives -- diffractive optics, computational geometry, multi-variant optimization, data processing -- to achieve a transformative 57X end-to-end acceleration. Beyond dramatically faster cycles, this expanded compute envelope enables more rigorous solutions, including curvilinear masks, high-numerical aperture extreme ultraviolet (high-NA EUV) lithography, and subatomic modeling. We reinvest a small fraction of the freed-up compute to include through-focus correction for better process resilience. Silicon experiments at IMEC show significant benefits compared to conventional methods -- 35% better process window and 19% better edge placement error. This is the first quantified chip-scale demonstration of the lithography benefits of AC and AI in silicon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15036 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient Mukhopadhyay, Saumyadip Yang, Kiho Vasudevan, Kasyap Thottasserymana Divvela, Mounica Jyothi Dogru, Selim Krishnamurthy, Dilip Treska, Fergo Gillijns, Werner Kim, Ryan Ryoung han Sastry, Kumara Singh, Vivek Signal Processing Artificial Intelligence Applied Physics From climate science to drug discovery, scientific computing demands have surged dramatically in recent years -- driven by larger datasets, more sophisticated models, and higher simulation fidelity. This growth rate far outpaces transistor scaling, leading to unsustainably rising costs, energy consumption, and emissions. Semiconductor manufacturing is no exception. Computational lithography -- involving transferring circuitry to silicon in diffraction-limited conditions -- is the largest workload in semiconductor manufacturing. It has also grown exceptionally complex as miniaturization has advanced in the angstrom-era, requiring more accurate modeling, intricate corrections, and broader solution-space exploration. Accelerated computing (AC) offers a solution by dramatically freeing up the compute and power envelope. AI augments these gains by serving as high-fidelity surrogates for compute-intensive steps. Together, they present a sustainable, next-generation computing platform for scientific workloads. This new paradigm needs a fundamental redesign of the software stack. For computational lithography, NVIDIA cuLitho reinvents the core primitives -- diffractive optics, computational geometry, multi-variant optimization, data processing -- to achieve a transformative 57X end-to-end acceleration. Beyond dramatically faster cycles, this expanded compute envelope enables more rigorous solutions, including curvilinear masks, high-numerical aperture extreme ultraviolet (high-NA EUV) lithography, and subatomic modeling. We reinvest a small fraction of the freed-up compute to include through-focus correction for better process resilience. Silicon experiments at IMEC show significant benefits compared to conventional methods -- 35% better process window and 19% better edge placement error. This is the first quantified chip-scale demonstration of the lithography benefits of AC and AI in silicon. |
| title | Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient |
| topic | Signal Processing Artificial Intelligence Applied Physics |
| url | https://arxiv.org/abs/2602.15036 |