PRIZ Academy
Explore how structured engineering thinking helps semiconductor teams uncover the real mechanism behind copper electroplating non-uniformity and move toward more efficient, more intrinsic process improvement.
In this PRIZ Academy webinar, Alex and Anatoly Agulyansky walk through a real semiconductor case study focused on copper electroplating uniformity improvement. Using the PRIZ Platform, they show how engineering teams can move beyond surface-level fixes and investigate the deeper mechanisms driving radial thickness non-uniformity across the wafer.
The session follows a practical problem-solving path through a chronic semiconductor manufacturing issue: copper deposition is thicker at the wafer edge and thinner at the center. Rather than treating that outcome as something to compensate for, the webinar shows how to analyze it systematically using structured reasoning inside PRIZ.
The walkthrough highlights how teams can use tools such as Problem Statement Analysis, 5 Whys, Cause-and-Effect analysis, and Functional Modeling to organize the investigation, understand system interactions, and reveal where the real amplification mechanism lives.
Copper electroplating non-uniformity is often managed through overplating, CMP compensation, or edge-current diversion techniques. These methods may protect yield, but they also add waste, cost, and process burden. The webinar explores a more valuable question: what makes the non-uniformity happen in the first place, and how can the process be improved at the root?
A central insight of the session is that the issue is not only a matter of seed-layer resistance. The stronger amplification comes from operating in a kinetically controlled regime, where small voltage variations create large current differences and, as a result, large thickness deviations. From there, the discussion points toward a more fundamental solution direction: shifting the process closer to diffusion-controlled behavior so deposition becomes more uniform intrinsically.
This webinar is also a demonstration of how PRIZ turns engineering investigation into a structured, documented, and reusable process. Instead of relying on scattered notes, isolated expertise, or disconnected troubleshooting steps, teams can capture how the problem was understood, what hypotheses were considered, and why certain solution directions make more sense than others.
The result is not just a better answer to one technical problem. It is a clearer reasoning process the organization can revisit, refine, and reuse.
The live discussion added an important practical layer to the session. It touched on how far cause-and-effect analysis should go before teams expect concrete design direction, and why embedded AI inside a structured project workspace offers a different value than a standalone chat. The conversation reinforced a consistent PRIZ theme: AI can accelerate analysis and reduce friction, but strong engineering judgment still depends on structure, context, and human thinking.
This webinar is especially relevant for semiconductor manufacturers, process engineers, yield teams, quality leaders, and technical managers working on process stability, recurring defect mechanisms, root-cause analysis, and cost reduction in advanced manufacturing environments.
Watch the session to see how structured, mechanism-based problem-solving can help engineering teams move from symptom management to deeper technical understanding — and from recurring process inefficiency to more meaningful improvement.
