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[2602.15036] Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient
Summary
This article discusses the integration of accelerated computing (AC) and artificial intelligence (AI) in computational lithography, highlighting significant advancements in speed, accuracy, and energy efficiency for semiconductor manufacturing.
Why It Matters
The rapid growth in scientific computing demands innovative solutions to manage costs and energy consumption in semiconductor manufacturing. This research demonstrates how AC and AI can transform computational lithography, addressing challenges in miniaturization and accuracy, which are critical for the future of technology and sustainability.
Key Takeaways
- Accelerated computing and AI can enhance computational lithography efficiency.
- The NVIDIA cuLitho platform achieves a 57X acceleration in lithography processes.
- Silicon experiments show a 35% improvement in process window and 19% in edge placement error.
- This research represents a significant step towards sustainable semiconductor manufacturing.
- A redesign of the software stack is essential for implementing these advancements.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.15036 (eess) [Submitted on 27 Jan 2026] Title:Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient Authors:Saumyadip Mukhopadhyay, Kiho Yang, Kasyap Thottasserymana Vasudevan, Mounica Jyothi Divvela, Selim Dogru, Dilip Krishnamurthy, Fergo Treska, Werner Gillijns, Ryan Ryoung han Kim, Kumara Sastry, Vivek Singh View a PDF of the paper titled Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient, by Saumyadip Mukhopadhyay and 10 other authors View PDF Abstract: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 a...