Search for: All records

Failure analysis and defect detection are crucial processes in industries, governments, and societies to mitigate the risks associated with defective microelectronics. The accurate identification of faulty parts is vital for preventing potential damages. However, traditional manual and automated defect detection approaches face challenges due to the scarcity of ground truth data from defective parts. This limitation hampers the effectiveness of subject matter experts and machine learning models in recognizing and classifying new instances of defects. To address this issue, we propose a synthetic data augmentation workflow that generates virtual defective parts, effectively overcoming the data scarcity problem and enabling the creation of large datasets at a low cost. Our approach enhances defect detection capabilities, empowering industries and governments to improve the quality and reliability of electronic devices. 

Failure analysis of microelectronics is essential to identify the root cause of a device’s failure and prevent future failures. This process often requires removing material from the device sample to reach the region of interest, which can be done through various destructive methods, such as mechanical polishing, chemical etching, focused ion beam milling, and laser machining. Among these, laser machining offers a unique combination of speed, precision, and controllability to achieve a high-throughput, highly targeted material removal. In using lasers for processing of microelectronic samples, a much-desired capability is automated endpointing which is crucial for minimizing manual checks and improving the overall process throughput. In this paper, we propose to integrate laser-induced breakdown spectroscopy (LIBS), as a fast and high-precision material detection and process control means, into an ultrashort pulsed laser machining system, to enable vertical endpointing for sample preparation and failure analysis of microelectronics. The capabilities of the proposed system have been demonstrated through several sample processing examples.