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In this paper, the optimization of cutting parameters for constrained machining operations is reported. Modified genetic algorithm (MGA) is an evolutionary computation technique that has been proposed in this paper to solve the machining problem. Additional constraints have been incorporated to the multipass turning model. The optimization of drilling and facing parameters have also been carried out. To demonstrate the procedure and performance of the approach, an illustrative example is discussed. The results of the proposed algorithm are compared with other traditional and non-traditional techniques such as Newton’s method, hill climbing and ants colony technique. [ABSTRACT FROM AUTHOR]

This paper considers an interesting topic of recipe qualification during each fabrication step of semiconductor manufacturing. In particular, this type of stochastic optimization scheme within a discrete-event simulation process is discussed and a new optimization method, with the capability to anchor robust recipes for batch processing, is proposed, implemented and evaluated. A particular real-coded genetic algorithm (GA) suitable for resolving continuous optimization problems in noisy environments is developed. Four test functions appearing in the literature are used as a test-bed to assess the proposed genetic algorithm via a variety of experimental studies, showing that the new method can produce much more accurate estimates of the true optimum points than the other two optimization procedures, the Nelder-Mead (NM) simplex search procedure and a well-known variant (RS+S9) of Nelder-Mead suitable for stochastic optimization. As such, the new method could serve as a useful tool for process recipe optimization in noisy semiconductor manufacturing environments. Finally, the chemical mechanical planarization (CMP) process, a turnkey process during semiconductor fabrication, is simulated from batch to batch based on the real-data equipment model and the presented algorithm is employed to seek the optimal recipe profile while processing each batch of wafers sequentially through the CMP tool. [ABSTRACT FROM AUTHOR]

Conventional tolerance analysis is tedious and time consuming, which makes engineers resist doing it. Complex assembly problems are generally beyond the capabilities of most design and manufacturing engineers. In this paper, genetic algorithm, a kind of non-traditional optimization technique is used as the basic foundation for optimal tolerance allocation to help design and manufacturing engineers to overcome the shortcomings in the conventional tolerance stack analysis and allocation system. [ABSTRACT FROM AUTHOR]