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The Human Hand: Curating Good Data And Creating An Effective Deep-Learning R2R Strategy For High-Volume Manufacturing


Currently, the semiconductor manufacturing industry uses artificial intelligence and machine learning to take data and autonomously learn from that data. With the additional data, AI and ML can be used to quickly discover patterns and determine correlations in various applications, most notably those applications involving metrology and inspection, whether in the front-end of the manufacturing ... » read more

Strategies For Faster Yield Ramps On 5nm Chips


Leading chipmakers TSMC and Samsung are producing 5nm devices in high volume production and TSMC is forging ahead with plans for first 3nm silicon by year end. But to meet such aggressive targets, engineers must identify defects and ramp yield faster than before. Getting a handle on EUV stochastic defects — non-repeating patterning defects such as microbridges, broken lines, or missing con... » read more

Demystifying ADC


ADC stands for automatic defect classification. It’s a software that classifies defects based on image and metadata such as location, ROI, and other information associated with a defect. ADC is not a mysterious black box that’s impossible to understand. Instead, ADC classifies defects the same way a human operator does, by first being trained by an expert. Then, just like human classificati... » read more

Fabs Meet Machine Learning


Aki Fujimura, chief executive of D2S, sat down with Semiconductor Engineering to discuss Moore’s Law and photomask technology. Fujimura also explained how artificial intelligence and machine learning are impacting the IC industry. What follows are excerpts of that conversation. SE: For some time, you’ve said we need more compute power. So we need faster chips at advanced nodes, but cost... » read more