Establishing Digital Recognition and Identification of Microscopic Objects for Implementation of Artificial Intelligence (AI) Guided Microassembly

TL;DR

This paper introduces an AI-based approach to micro-assembly, leveraging force modeling at micro and nano scales to enable high-throughput, low-cost assembly of tiny components for advanced technological applications.

Contribution

It presents a novel phenomenological AI method to model and control micro-particle movement under complex forces, improving micro-assembly processes.

Findings

AI effectively correlates particle movement with applied forces.

The method achieves high yield micro-assembly at low cost.

Applicable to MEMS, biotech, and tissue engineering fields.

Abstract

s miniaturization of electrical and mechanical components used in modern technology progresses, there is an increasing need for high-throughput and low-cost micro-scale assembly techniques. Many current micro-assembly methods are serial in nature, resulting in unfeasibly low throughput. Additionally, the need for increasingly smaller tools to pick and place individual microparts makes these methods cost prohibitive. Alternatively, parallel self-assembly or directed-assembly techniques can be employed by utilizing forces dominant at the micro and nano scales such as electro-kinetic, thermal, and capillary forces. However, these forces are governed by complex equations and often act on microparts simultaneously and competitively, making modeling and simulation difficult. The research in this paper presents a novel phenomenological approach to directed micro-assembly through the use of artificial intelligence to correlate micro-particle movement via dielectrophoretic and electro-osmotic forces in response to varying frequency of an applied non-uniform electric field. This research serves as a proof of concept of the application of artificial intelligence to create high yield low-cost micro-assembly techniques, which will prove useful in a variety of fields including micro-electrical-mechanical systems (MEMS), biotechnology, and tissue engineering.