Pick the winning EUV photoresist before the fab
Resist candidates ranked before any were made, so only the lead goes to synthesis and wafer testing. Award winner, 2nd place worldwide, GIC 2026 Advanced Materials track (Mitsubishi Chemical Group × AIST).
Impact
The problem
Choosing which photoresist formulation to take to the fab means synthesising and testing each candidate: weeks of lab and wafer time, and cost, per option. The published chemistry method for this class of material also needed 173 GPU-hours of training before it could run.
Our approach
We modelled an industrial tin-oxo resist cluster and its counter-anion variants with our methods, trained the chemistry model on one GPU, and ranked the candidates on IBM quantum hardware with the protocol frozen before the run and a null control in the same job.
The evidence
- Formulation ranking: 4 of 4 in exact order, rank correlation 1.0, every energy difference within 0.3% (IBM Heron, protocol frozen before the run).
- Three counter-anion variants ranked on 120 qubits in one job; measured order equal to exact order.
- AI-driven quantum chemistry on the tin-oxo cluster at 40 qubits, every step within chemical accuracy of its exact reference (GPU computation plus hardware sampling).
- Award winner, 2nd place worldwide, GIC 2026 Advanced Materials track (Mitsubishi Chemical Group × AIST). A judged result; it does not imply endorsement of any product.
- Not claimed: that the material will perform in the fab, or that the material has been solved completely.
Status: Quantum hardware + Simulation. Ranking measured on quantum hardware; training cost and the 40-qubit chemistry are classical GPU computation with hardware sampling.