PREDICTION AND METROLOGY OF STOCHASTIC PHOTORESIST THICKNESS DEFECTS
A mask pattern for a semiconductor device can be used as an input to determine a photoresist thickness probability distribution using a machine learning module. For example, the machine learning module can determine a probability map of Z-height. This can be used to determine stochastic variation in...
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| creator | BAI, Kunlun ZHANG, Cao BUROV, Anatoly VUKKADALA, Pradeep GRAVES, Trey John S LI, Xiaohan PARSEY, Guy HIGGINS, Craig |
| description | A mask pattern for a semiconductor device can be used as an input to determine a photoresist thickness probability distribution using a machine learning module. For example, the machine learning module can determine a probability map of Z-height. This can be used to determine stochastic variation in photoresist thickness for a semiconductor device. The Z-height may be calculated at a coordinate in the X-direction and Y-direction. |
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| subjects | APPARATUS SPECIALLY ADAPTED THEREFOR CALCULATING CINEMATOGRAPHY COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTROGRAPHY HOLOGRAPHY MATERIALS THEREFOR ORIGINALS THEREFOR PHOTOGRAPHY PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES,e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTORDEVICES PHYSICS |
| title | PREDICTION AND METROLOGY OF STOCHASTIC PHOTORESIST THICKNESS DEFECTS |
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