Focusing on factors with the greatest impact on the manufacturing process helps boost manufacturers’ efficiency while continuing to meet high quality standards.
For years, the semiconductor was motivated by Moore’s Law, which posits that the number of transistors in a densely integrated circuit will double every two years. The furious pursuit of semiconductor improvements often presents manufacturers with the dilemma of how to achieve better performance while maintaining product quality. One semiconductor industry company wanted to reduce defects in its manufacturing process—a highly instrumented and complex environment with much room for error—thereby improving its year-on-year production efficiency.
Insight and Action
QuantumBlack aggregated data from 22 sensors and selected a random forest model to assess the probability of failure based on a number of operating factors available during the live manufacturing process, such as temperature, pressure, voltage, current, and resistance. We identified and quantified the key characteristics of the manufacturing process responsible for thickness-related quality issues, including optimal voltage and temperature controls, lamp power, and throughput.
We validated a model that can provide several hours of advanced warning for semiconductor wafers with quality issues, which has helped the company significantly improve its manufacturing operations.