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

Area of Science:

• Materials Science
• Nanotechnology
• Optics

Background:

• Block copolymer (BCP) films are crucial for creating nanoscale patterns.
• Conventional thermal annealing for BCP ordering and material processing is time-consuming and material-intensive.
• Spatially selective thermal processing is challenging for complex nanostructures.

Purpose of the Study:

• To develop a rapid, spatially selective photothermal platform for processing BCP films and inorganic replicas.
• To leverage machine learning for optimizing optical illumination patterns and predicting thermal profiles.
• To significantly reduce the time and material requirements for nanoscale material optimization.

Main Methods:

• A microscope-based photothermal platform combining structured laser light projection and machine learning was developed.
• Two neural network models, optical-to-thermal (O2T) and thermal-to-optical (T2O), were created for precise thermal control.
• High-throughput screening of over 80 process conditions was performed on BCP films and metal-oxide nanostructures.

Main Results:

• Achieved near-top-hat temperature uniformity over a 1 mm diameter area.
• Enabled temperatures from ~100°C-350°C for BCP ordering and >500°C for sintering/ashing.
• Reduced material and time needs by approximately 20-fold compared to conventional methods.

Conclusions:

• The developed platform enables automated, high-throughput exploration of optical-thermal interactions and ordering kinetics.
• Demonstrated controlled photothermal driving of self-assembly and post-processing of BCP-templated nanowires.
• Offers a scalable route to accelerate the optimization of nanoscale materials and device fabrication.