Semiconductor Today | How AI and ML can save $38 billion for semiconductor manufacturers

Today, increasing throughput is the number-one priority for semiconductor fabs, as they work to overcome the challenges of the global chip shortage.
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Looking beyond throughput, there are significant opportunities for long-term cost savings from optimiz­ing, simplifying or removing processing steps. We ca II this approach EPCO - Equipment and Process Co-Opti­mization. It is a combination of good engineering and applying data-driven machine learning (ML) to the manufacturing process and equipment.

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A 2021 paper by McKinsey argued that semiconduc­tor manufacturing optimization, using artificial intelli­gence (Al) and machine learning (ML), could save $38 billion, through improved yields and increased throughput.

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