US 12,444,655 B2
Machine learning model for semiconductor manufacturing processes
Zhiqiang Huang, Singapore (SG); Li Ming Tan, Singapore (SG); Joanna Kejun Loh, Singapore (SG); Olivia Fatma Koentjoro, Waterford Residence (SG); and Roger Alan Lindley, Santa Clara, CA (US)
Assigned to Applied Materials, Inc., Santa Clara, CA (US)
Filed by Applied Materials, Inc., Santa Clara, CA (US)
Filed on Mar. 28, 2023, as Appl. No. 18/191,697.
Prior Publication US 2024/0332092 A1, Oct. 3, 2024
Int. Cl. H01L 21/66 (2006.01); G05B 13/02 (2006.01); G05B 13/04 (2006.01); G05B 19/401 (2006.01); G06F 30/3308 (2020.01); G06F 30/337 (2020.01); H01L 21/67 (2006.01)
CPC H01L 22/12 (2013.01) [G05B 13/0265 (2013.01); G05B 13/048 (2013.01); G05B 19/401 (2013.01); G06F 30/3308 (2020.01); G06F 30/337 (2020.01); H01L 21/67 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A processor-implemented method for processing a semiconductor wafer using a trained machine learning predictive model, the method comprising:
inputting, into a trained machine learning predictive model, at least one of: a semiconductor wafer design data or process parameters;
inputting, into the trained machine learning predictive model, a gas flow configuration for a pixelated showerhead;
receiving a generated predicted uniformity profile from the trained machine learning predictive model;
determining that the generated predicted uniformity profile matches a target uniformity profile;
directing a controller to process the semiconductor wafer;
receiving a measured uniformity of components on the processed semiconductor wafer;
determining whether the measured uniformity is within a tolerance limit; and
upon determination that the measured uniformity profile is within the tolerance limit, determining that processing of the semiconductor wafer has completed.