PROJECT TITLE :
A Vapor Phase Self-Assembly of Porphyrin Monolayer as a Copper Diffusion Barrier for Back-End-of-Line CMOS Technologies
We tend to implement a gradient-based mostly simulation optimization approach, the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to estimate clearing functions (CFs) that describe the expected output of a production resource as a operate of its expected workload from empirical knowledge. Instead of trying to optimize the fit of the CF to the information, we have a tendency to seek values of the CF parameters that optimize the expected performance for the system when the fitted CFs are used to develop release schedules. A simulation model of a scaled-down wafer fabrication facility is used to generate the info and evaluate the performance of the CFs obtained from the SPSA. We tend to show that SPSA considerably improves the production set up by either looking out for better CF parameters or by directly optimizing releases.
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