DOE/Opt: a system for design of experiments, response surface modeling, and optimization using process and device simulation
Rapid modeling and optimization of manufacturing processes, devices, and circuits are required to support modern integrated circuit technology development and yield improvement. We have prototyped and applied an integrated system, called DOE/Opt, for performing Design of Experiments (DOE), Response...
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Veröffentlicht in: | IEEE transactions on semiconductor manufacturing 1994-05, Vol.7 (2), p.233-244 |
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Sprache: | eng |
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Zusammenfassung: | Rapid modeling and optimization of manufacturing processes, devices, and circuits are required to support modern integrated circuit technology development and yield improvement. We have prototyped and applied an integrated system, called DOE/Opt, for performing Design of Experiments (DOE), Response Surface Modeling (RSM), and Optimization (Opt). The system to be modeled and optimized can be either physical or simulation based. Within the DOE/Opt system, coupling to external simulation or experimental tools is achieved via an embedded extension language based on Tcl. The external problem then appears to DOE/Opt as a model with user defined inputs and outputs. DOE/Opt is used to generate splits for experiments, to dynamically build and evaluate regression models from experimental runs, and to perform nonlinear constrained optimizations using either regression models or embedded executions. The intermediate regression modeling can appreciably accelerate the optimization task when simulation or physical experiments are expensive. The primary application of DOE/Opt has been to process optimization using coupled process and device simulation. DOE/Opt has also been applied to process and device simulator tuning, and to aid in device characterization. Such a DOE/Opt system is expected to augment the use of TCAD tools and to utilize data collected by CIM systems in support of process synthesis. We have demonstrated the application of the system to process parameter determination, simulator tuning, process control modeling, and statistical process optimization. We are extending the system to more fully support emerging device design and process synthesis methodologies.< > |
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ISSN: | 0894-6507 1558-2345 |
DOI: | 10.1109/66.286858 |