Since several years, there has been continuous focus on legacy tools for mask making and the critical requirements to address the end of life of equipment which has been workhorse for volume production. Toppan Photomask Company, Ltd. (TPC) has presented several papers on this subject and brought this to attention to many equipment manufacturers and other mask shops who have also shown keen interest to support the manufacturers who are willing to take up this task to provide solutions. HTL Co. Japan Ltd. And V Technology Japan has teamed up to manufacture equipment for the semiconductor mask making legacy tools and one such example is successfully manufacturing the mask repair system with support from TPC. Mask inspection, Registration, FIB repair and others also being addressed, and one attractive feature is to give an opportunity to enhance the performance of these new replacement tools by using AI software for defect classification for the system. We will discus our development process and capabilities served for legacy tool replacement.
The purpose of this trial is how an AI-based approach can contribute to improving the operability of photomask inspection equipment. It is important for the equipment operation how to efficiently identify photomask defects. In particular, it is essential to accurately perform the filtering of erroneous judgments of inspection equipment called false defects. Furthermore, for actual defects, it is necessary to classify the defect types as accurately as possible. This paper describes how to implement AI approach into the “Defect Review System”. Especially, in case that the equipment can capture both transmission and reflection images simultaneously, effective utilization of both images has been shown to result in more effective identification of defects.
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