Automated Photothermal Control of Block Copolymer Self‑Assembly and Metal Oxide Nanostructures Sintering

We report a microscope-based photothermal platform that combines structured laser light projection with machine-learning-optimized illumination to enable rapid, spatially selective thermal processing of block copolymer (BCP) films and their inorganic replicas. Two neural-network models were developed: optical-to-thermal (O2T), which predicts radial thermal profiles from optical patterns, and thermal-to-optical (T2O), which generates the optical pattern required to produce a target thermal field. Using these tools, we achieved near-top-hat temperature uniformity over a 1 mm diameter area and accessed temperatures from ∼100°C-350°C for BCP ordering and in excess of 500°C for local sintering/ashing of metal-oxide nanostructures. The lab-on-chip workflow permitted screening of more than 80 process conditions on only a handful of samples and in hours, reducing material and time needs by roughly a factor of 20 vs. conventional thermal annealing. We demonstrate controlled photothermal driving of self-assembly, rapid local removal of polymer templates, and post-processing sintering of In₂O₃ and Fe₂O₃ BCP-templated nanowires, with correlated changes in carbon residue and nanowire morphology as a function of temperature. The approach enables automated, high-throughput exploration of optical-thermal interactions and ordering kinetics, offering a scalable route to accelerate optimization of nanoscale materials and device-relevant processing.