ML models adjust 200+ manufacturing parameters in real time to maximize output quality
Yield on their newest high-density drive was stuck at 74% — the business case required 82% to be profitable. Engineering had spent 4 months trying to optimize the process manually, adjusting one variable at a time. At the current trajectory, they'd miss the product launch window and cede the market to a Korean competitor who was 3 months ahead.
Manufacturing high-density storage media involves controlling 200+ process parameters simultaneously — temperature profiles, deposition rates, sputtering pressures, substrate rotation speeds, and clean room conditions. The interactions between variables are non-linear and counterintuitive. Human engineers can optimize 3-4 variables at a time. The combinatorial space of 200 variables is impossible to explore manually.
We deployed a Bayesian optimization system that models the relationship between all 200+ process parameters and yield outcomes. The system suggests parameter adjustments, runs controlled experiments on production equipment during scheduled changeovers, and converges on optimal settings. A digital twin simulates process changes before they're applied to production equipment. Our overnight engineering team runs optimization experiments during off-peak production hours.
We'd been adjusting temperature and pressure independently for four months. The AI discovered that the optimal settings were counterintuitive — lower temperature with higher pressure at a specific rotation speed. No engineer would have tried that combination.— Director of Process Engineering
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