Data-Driven Optimization for Controllable Multi-Scale Aperture Fabrication of Nanopipettes

Key Points

• The aim is to enhance the fabrication process of controllable nanopipettes through a novel optimization framework that integrates AI and physical modeling.
• Proposed a multi-parameter collaborative optimization framework combining artificial neural networks with a physical model.
• Utilized data-driven approaches to improve fabrication accuracy and control.
• Evaluated the effectiveness of the framework in nanopipette production.
• The optimization framework significantly increased the controllability of nanopipette dimensions.
• Improvements in fabrication accuracy were observed with the integrated approach, leading to enhanced performance characteristics of the nanopipettes.