Pursuit of hidden rules behind the irregularity of nano capillary lithography by hybrid intelligence

Abstract

Nature finds a way to leverage nanotextures to achieve desired functions. Recent advances in nanotechnologies endow fascinating multi-functionalities to nanotextures by modulating the nanopixel’s height. But nanoscale height control is a daunting task involving chemical and/or physical processes. As a facile, cost-effective, and potentially scalable remedy, the nanoscale capillary force lithography (CFL) receives notable attention. The key enabler is optical pre-modification of photopolymer’s characteristics via ultraviolet (UV) exposure. Still, the underlying physics of the nanoscale CFL is not well understood, and unexplained phenomena such as the “forbidden gap” in the nano capillary rise (unreachable height) abound. Due to the lack of large data, small length scales, and the absence of first principles, direct adoptions of machine learning or analytical approaches have been difficult. This paper proposes a hybrid intelligence approach in which both artificial and human intelligence coherently work together to unravel the hidden rules with small data. Our results show promising performance in identifying transparent, physics-retained rules of air diffusivity, dynamic viscosity, and surface tension, which collectively appear to explain the forbidden gap in the nanoscale CFL. This paper promotes synergistic collaborations of humans and AI for advancing nanotechnology and beyond.

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

“…One intriguing phenomenon investigated is the Klingenberg effect [11] and its incorporation in gas flow through porous media [12][13][14]. One of the most recent advances involved discovering the hidden rules that explain the sudden jump phenomenon, with gas diffusivity, dynamic viscosity, and surface tension identified as the previously obscure physical principles governing this phenomenon [15]. However, it could not find the relationship between the height of the PDMS mold and its effect on the sudden jump.…”

“…However, they are "black-box" models where the inner working of the model is not easily understandable or interpretable. Due to the inability of ML to unveil "transparent rules", this paper applies hybrid intelligence, used in [15], to explain the sudden jump phenomenon and its variation with PDMS height. Hybrid intelligence combines human and artificial intelligence to reveal the unknown physical rules (Figure 1d).…”

“…Unknown terms that are beyond current human understanding are included in the formulation. On the other hand, artificial intelligence provides the search capabilities to identify the most promising expressions for these unknown rules [15].…”

“…Although this paper has its beginnings in [15], considerable advances have been made since. There are significant differences between this paper and [15].…”

“…Although this paper has its beginnings in [15], considerable advances have been made since. There are significant differences between this paper and [15]. Firstly, this paper focuses on nano-grating geometry, while [15] deals with circular nanopillars.…”