US 11,862,520 B2
Systems and methods for predicting film thickness of individual layers using virtual metrology
Bharath Ram Sundar, Chennai (IN); Raman K. Nurani, Chennai (IN); Utkarsha Avinash Dhanwate, Amravati (IN); Ramakrishnan S. Hariharan, Trichy (IN); Suresh Bharatharajan Kudallur, Chennai (IN); and Vishwath Ram Amarnath, Chennai (IN)
Assigned to Applied Materials, Inc., Santa Clara, CA (US)
Filed by Applied Materials, Inc., Santa Clara, CA (US)
Filed on Feb. 3, 2021, as Appl. No. 17/166,288.
Prior Publication US 2022/0246481 A1, Aug. 4, 2022
Int. Cl. H01L 21/66 (2006.01); G01N 23/2251 (2018.01); G01N 23/04 (2018.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01)
CPC H01L 22/12 (2013.01) [G01N 23/04 (2013.01); G01N 23/2251 (2013.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); H01L 22/26 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining, by a processor, sensor data associated with a deposition process performed in a process chamber to deposit a film stack on a surface of a substrate, wherein the film stack comprises a plurality of layers of a first material and a plurality of layers of a second material;
obtaining metrology data associated with the film stack;
training a first machine-learning model based on the sensor data and the metrology data, wherein the first machine-learning model is trained to generate predictive metrology data associated with layers of the first material; and
training a second machine-learning model based on the sensor data and the metrology data, wherein the second machine-learning model is trained to generate predictive metrology data associated with layers of the second material.