195271_en-US
Establishing Yield Improvement Decision Metrics Using Monte Carlo Simulation_Jeffrey Yang
Speaker: Jeffrey Yang, Micron Semiconductor Senior Engineering Manager
the speech topic: Establishing Yield Improvement Decision Metrics Using Monte Carlo Simulation
Summary:
In the LSI test industry, there are always challenges to improve yield by reducing false failures, especially in back-end operations. On the one hand, customers are increasingly demanding on-time delivery. On the other hand, there is tremendous pressure to save on the cost of each single die while meeting the quality standards of the customer. Finding the balance metric becomes a key point to guide the engineering team in making decisions. Monte Carlo simulations can effectively address this issue, but oftentimes engineers lack the proper tools to adopt this approach. JMP Pro can help, even if you're new to coding and algorithms. This article describes how to perform Monte Carlo simulations using JMP Pro to find the right recipe for production tools (socket disable rate, etc.) and operational KPIs (UPH, yield, recovery rate, etc.). Simulation results are reflected in radar charts to provide engineers with a dynamic view to judge balance points and adjust recipes as needed. By formulating a series of radar indicators combined with product portfolios and implementing formula adjustments, it helps improve the overall yield of terminal production.
Monte Carlo SimulationMonte Carlo simulationpredictive modeling
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