Semiconductor Today | How AI and ML can save $38bn for semiconductor manufacturers
Today, increasing throughput is the number-one priority for semiconductor fabs, as they work toovercome the challenges of the global chip shortage.
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.
A 2021 paper by McKinsey argued that semiconductor manufacturing optimization, using artificial intelligence (Al) and machine learning (ML), could save
$38bn, through improved yields and increased throughput.