Case Study - Fortune 500 Transistor Producer

Over the past years, several semiconductor producers have lost customers due to yield problems. While producers tend to be hesitant to disclose specific yield figures, industry estimates commonly place these yields between 85% and 95%. Yield losses are particularly pertinent for new product generations, where recipes and production parameters must be calibrated from scratch. Consequently, determining the root causes of yield losses is not only a primary concern but also a potential competitive edge in the semiconductor industry.

Existing quality methods in semiconductor manufacturing, both linear statistics and conventional machine learning, struggled to identify true root causes of yield loss. Linear approaches missed nonlinear interactions, while machine learning models focused on prediction, often overfitting data and highlighting correlated rather than causal factors. As a result, lacking a reliable way to pinpoint the process parameters truly driving quality issues.

The deployment focused on products with low and poorly understood yield. Process engineers combined equipment routing data, in-process measurements, and yield data to analyze the production flow. They uncovered previously hidden process relationships driving yield losses which enabled targeted corrective actions.

51% scrap reduction by uncovering critical process drivers missed by traditional quality tools.

Modern analytics stack with redesigned analytics pipelines and a data-driven approach to root cause analysis.

Sustained business impact with higher yield, improved supply stability, and measurable economic gains.