Automation in Microwave-Induced Plasma Decapsulation Verification through Machine Learning
Automation in Microwave-Induced Plasma Decapsulation Verification through Machine Learning
Tuesday, October 6, 2026: 2:30 PM
Summary:
Microwave-induced plasma (MIP) decapsulation of microchips traditionally requires constant manual monitoring and verification to ensure an optimized process, a time-consuming task for researchers. This paper explores the feasibility of utilising an AI-driven approach to automate both the operation and verification of the MIP process. By leveraging machine learning trained on a dedicated image database, the AI successfully managed the etching cycles, automatically halting the process upon full die exposure. Furthermore, the system demonstrated the potential to identify the decapsulation states of "foreign" microchip not included in the original training set. These results suggest a significant opportunity to streamline industrial workflows through AI and expand automated failure analysis to more advanced microchip packaging.
Microwave-induced plasma (MIP) decapsulation of microchips traditionally requires constant manual monitoring and verification to ensure an optimized process, a time-consuming task for researchers. This paper explores the feasibility of utilising an AI-driven approach to automate both the operation and verification of the MIP process. By leveraging machine learning trained on a dedicated image database, the AI successfully managed the etching cycles, automatically halting the process upon full die exposure. Furthermore, the system demonstrated the potential to identify the decapsulation states of "foreign" microchip not included in the original training set. These results suggest a significant opportunity to streamline industrial workflows through AI and expand automated failure analysis to more advanced microchip packaging.