Tackling Multi-Physics Nano-Scale Phenomena in Capillary Force Lithography with Small Data by Hybrid Intelligence.

Area of Science:

• Materials Science
• Nanotechnology
• Physics

Background:

• Developing nature-inspired nanostructures is a key scientific goal.
• Controlling nanostructure height during fabrication remains a significant challenge.
• Nanoscale capillary force lithography (CFL) offers a method for altering photopolymer properties via UV exposure, but its mechanism is not fully understood.

Purpose of the Study:

• To investigate the underlying physical principles of the "sudden jump phenomenon" observed in CFL.
• To explain the abrupt changes in photopolymer height based on UV exposure and nano-grating dimensions.
• To bridge the gap in understanding CFL's working mechanism using first principles and AI.

Main Methods:

• Utilized known physical principles in conjunction with artificial intelligence (AI).
• Analyzed experimental data on photopolymer height variations in relation to UV exposure time and nano-grating height.
• Employed AI to identify and quantify previously unknown physical factors governing the phenomenon.

Main Results:

• Identified air diffusivity as a critical factor in the sudden jump phenomenon.
• Determined dynamic viscosity, surface tension, and electric potential as contributing physical principles.
• Demonstrated that these factors collectively explain the observed abrupt changes in nanostructure height.

Conclusions:

• The study successfully elucidated the unknown physical principles behind the sudden jump phenomenon in CFL.
• Provides a foundational understanding for precise height control in nanostructure fabrication using CFL.
• Highlights the potential of integrating AI with physical principles for scientific discovery in nanotechnology.