Subresolution Assist Feature Generation With Supervised Data Learning. Xu, X., Lin, Y., Li, M., Matsunawa, T., Nojima, S., Kodama, C., Kotani, T., & Pan, D. Z. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(6):1225–1236, June, 2018.
doi  abstract   bibtex   
Subresolution assist feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model-based and rule-based approaches are widely adopted in the semiconductor industry. The model-based SRAF generation can achieve a high accuracy but it is known to be time-consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. The rule-based SRAF generation is highly technology dependent and it is becoming extremely difficult to render high-quality results in advanced technology nodes. This paper proposes supervised data learning techniques for fast yet consistent SRAF generation with high-quality results. We first propose the constrained concentric circle with area sampling scheme for feature extraction. Illumination source symmetry-based feature compaction technique is further invented to reduce the training data set size and achieve consistent SRAF predictions. Using accurate model-based SRAFs as training data, classification models based on logistic regression (LGR) and support vector machine are calibrated for SRAF predictions. Moreover, the probability maximum prediction is proposed to generate manufacturing-friendly SRAFs with a greedy simplification scheme. We compare support vector machine and LGR models by embedding into an entire mask optimization flow, where the support vector machine model obtains better lithographic performance. Experimental results demonstrate that, compared with the commercial Calibre tool, supervised data learning techniques for SRAF generation obtain significant speed up (\textgreater3X for a 100 um2layout clip) and comparable lithographic performance in terms of edge placement error and process variation band.
@article{xu_subresolution_2018,
	title = {Subresolution {Assist} {Feature} {Generation} {With} {Supervised} {Data} {Learning}},
	volume = {37},
	issn = {0278-0070},
	doi = {10.1109/TCAD.2017.2748029},
	abstract = {Subresolution assist feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model-based and rule-based approaches are widely adopted in the semiconductor industry. The model-based SRAF generation can achieve a high accuracy but it is known to be time-consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. The rule-based SRAF generation is highly technology dependent and it is becoming extremely difficult to render high-quality results in advanced technology nodes. This paper proposes supervised data learning techniques for fast yet consistent SRAF generation with high-quality results. We first propose the constrained concentric circle with area sampling scheme for feature extraction. Illumination source symmetry-based feature compaction technique is further invented to reduce the training data set size and achieve consistent SRAF predictions. Using accurate model-based SRAFs as training data, classification models based on logistic regression (LGR) and support vector machine are calibrated for SRAF predictions. Moreover, the probability maximum prediction is proposed to generate manufacturing-friendly SRAFs with a greedy simplification scheme. We compare support vector machine and LGR models by embedding into an entire mask optimization flow, where the support vector machine model obtains better lithographic performance. Experimental results demonstrate that, compared with the commercial Calibre tool, supervised data learning techniques for SRAF generation obtain significant speed up ({\textgreater}3X for a 100 um2layout clip) and comparable lithographic performance in terms of edge placement error and process variation band.},
	number = {6},
	journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
	author = {Xu, X. and Lin, Y. and Li, M. and Matsunawa, T. and Nojima, S. and Kodama, C. and Kotani, T. and Pan, D. Z.},
	month = jun,
	year = {2018},
	keywords = {Data models, Feature extraction, LGR models, Layout, Lithography, Logistic regression (LGR), Optimization, SRAF generation, Training data, Two dimensional displays, area sampling scheme, classification models, consistent SRAF predictions, consistent SRAFs, feature extraction, greedy algorithms, greedy simplification scheme, highly technology dependent, illumination source symmetry-based feature compaction technique, learning (artificial intelligence), lithography, logistic regression, manufacturing-friendly SRAFs, model-based SRAF, modern semiconductor manufacturing process, pattern classification, photolithography, probability maximum prediction, regression analysis, rule-based SRAF, rule-based approaches, semiconductor industry, subresolution assist feature (SRAF), subresolution assist feature generation, supervised data learning techniques, supervised learning, support vector classification (SVC), support vector machine model, support vector machines, training data set size},
	pages = {1225--1236},
}
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