Machine Learning-Guided Etch Proximity Correction
Rule- and model-based methods of etch proximity correction (EPC) are widely used, but they are insufficiently accurate for technologies below 20 nm. Simple rules are no longer adequate for the complicated patterns in layouts; and models based on a few empirically determined parameters cannot reflect...
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| Veröffentlicht in: | IEEE transactions on semiconductor manufacturing 2017-02, Vol.30 (1), p.1-7 |
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| creator | Shim, Seongbo Shin, Youngsoo |
| description | Rule- and model-based methods of etch proximity correction (EPC) are widely used, but they are insufficiently accurate for technologies below 20 nm. Simple rules are no longer adequate for the complicated patterns in layouts; and models based on a few empirically determined parameters cannot reflect etching phenomena physically. We introduce machine learning to EPC: each segment of interest, together with its surroundings, is characterized by geometric and optical parameters, which are then submitted to an artificial neural network that predicts the etch bias. We have implemented this new approach to EPC using a commercial OPC tool, and applied it to a DRAM gate layer in 20-nm technology, achieving predictions that are 34% more accurate than model-based EPC. |
| doi_str_mv | 10.1109/TSM.2016.2626304 |
| format | Article |
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| subjects | artificial neural network Etch proximity correction (EPC) Etching Kernel Layout machine learning Optical imaging Resists Semiconductor device measurement Semiconductor device modeling |
| title | Machine Learning-Guided Etch Proximity Correction |
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