Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974

Extreme ultraviolet mask surface cleaning effects on lithography process performance

Extreme UV (EUV) masks are expected to undergo cleaning processes in order to maintain the lifetimes necessary for high volume manufacturing. For this study, the impact of repetitive cleaning of EUV masks on imaging performance is evaluated. Two high quality industry standard EUV masks are used, with one of the masks undergoing repeated cleaning and the other one kept as a reference. Lithographic performance, in terms of process window analysis and line edge roughness, was monitored after every two cleans and was compared to the reference mask performance. Surface analysis by atomic force microscopy did not show changes in the midspatial frequency roughness measured after each clean. After a total of eight cleans, minimal degradation is observed in the lithographic performance of the mask. From these observations, the authors conclude that the cleaning cycles completed thus far did not damage the mask multilayer or the absorber structures. The cleaning cycles will be continued until significant loss in imaging fidelity is found. © 2010 American Vacuum Society