Cross-Process Defect Attribution using Potential Loss Analysis
This addresses a critical challenge in semiconductor manufacturing for process engineers, though it appears incremental as an enhancement to a previously proposed partial trajectory regression approach.
The paper tackles cross-process root-cause analysis of wafer defects in semiconductor manufacturing by proposing a Potential Loss Analysis (PLA) framework that attributes high defect densities to upstream processes by comparing best possible outcomes from partial trajectories, showing it can solve both defect density prediction and attribution problems using real wafer data.
Cross-process root-cause analysis of wafer defects is among the most critical yet challenging tasks in semiconductor manufacturing due to the heterogeneity and combinatorial nature of processes along the processing route. This paper presents a new framework for wafer defect root cause analysis, called Potential Loss Analysis (PLA), as a significant enhancement of the previously proposed partial trajectory regression approach. The PLA framework attributes observed high wafer defect densities to upstream processes by comparing the best possible outcomes generated by partial processing trajectories. We show that the task of identifying the best possible outcome can be reduced to solving a Bellman equation. Remarkably, the proposed framework can simultaneously solve the prediction problem for defect density as well as the attribution problem for defect scores. We demonstrate the effectiveness of the proposed framework using real wafer history data.