Collaborative Research: DMREF: AI-Informed, Closed-Loop Design of Negative Resists for High-Volume EUV Lithography

About This Grant

Non-technical Description: The mass production of integrated circuits (commonly known as ‘microchips’ or simply ‘chips’) is a key driver for modern computational advances. Chip manufacturing requires a process called photolithography to template the intricate patterns of electronic components. This process uses patterns of light to selectively pattern a material known as a photoresist. New extreme ultraviolet (EUV) based lithography methods are poised to enable more powerful chips than ever before by packing higher volumes of smaller electronic components onto a single chip, making new photoresists essential to reaching the desired small features sizes. This Designing Materials to Revolutionize Our Future (DMREF) project combines chemistry, processing, and computation to design new photoresists to enable high-volume EUV lithography for chip manufacturing. This will be achieved by understanding how the local molecular structure of polymer-based photoresists defines the patterning at nanoscale dimensions, and how this translates to manufacturing outcomes. This interdisciplinary effort will bring together scientists and engineers from academia and the Air Force Research Lab with expertise in synthesizing materials, characterizing their physical properties, modeling their behavior with simulations, and predicting new materials with improved properties using AI. The new materials and patterning methodologies developed in this project will broadly benefit the US by enabling advanced manufacturing of next-generation computer chips with applications ranging from personal electronics and health care monitoring to supercomputers and generative AI. This research will further be combined with K-12 outreach and student training to prepare the next generation STEM workforce. Technical Description: This project will integrate combined expertise in polymer chemistry, physics, computation, and advanced manufacturing into a closed loop process to enable the design and implementation of crosslinkable polymeric photoresists for EUV lithography. Theory, molecular simulation, and data science will be combined with polymer chemistry and advanced metrology to understand how the sequence-specific molecular structure of copolymers translates to local patterning heterogeneity. Additionally, this effort will be combined with data science-enabled proxy measurements to rapidly and efficiently traverse an enormous chemical space for materials discovery. The ultimate goal of this work is to develop candidate chemistries that produce patterns with appropriate dimensions and fidelity under industrially-relevant EUV exposures. More broadly, workflows to aid the discovery-to-translation timeline of EUV lithography resists will be developed. Additionally, this research will be integrated with education and workforce development efforts to train students who can effectively communicate across the materials development continuum and contribute to the semiconductor industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.