Adaptive Design of Experiments for Optimal Uniformity in Semiconductor Manufacturing

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Research:Engineers in the microelectronics industry are frequently faced with the problem of selecting tool design and/or operating conditions (recipes) that produce high uniformity in the etching of thin films. Experiments that determine optimal uniformity conditions are expensive and constraint on input variables have to be satisfied at the optimum. Standard response-surface-methodology (RSM) can handle simple but not complicated constraints. This work is aimed at developing a new algorithmic approach to inequality constrained RSM with application to plasma etching. This will include the development of a high-fidelity fundamental plasma reactor model.The work will consist of: -Formulation of the problem as an adaptive sequential design of E-optimal experiments based on linear, or if necessary, nonlinear models; -Development of numerical solution techniques for the above optimization; -Development of a high-fidelity model for a dual-coil inductively coupled plasma (ICP) etching tool; -Investigation of connections between sequential RSM and adaptive control; and -Studying the properties of the algorithms developed through computer simulations and laboratory and industrial experiments.Impact:Interaction with an industrial collaborator will ensure focus on realistic problems encountered in the semiconductor industry. The project has potential infrastructure benefits in that students trained in developing this methodology would be of great use to industry. Given that over 50% of undergraduates at the University of Houston are minorities and that the project will have heavy undergraduate participation, the involvement of students from underrepresented groups can be expected.