Performance predictors for semiconductor-manufacturing processes

A system comprising:

• a hardware-based memory comprising instructions;
• and
• one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:
• obtaining a plurality of machine-learning (ML) models, each of the plurality of ML models being related to predicting a performance metric for an operation of a semiconductor manufacturing tool, each ML model to utilize a plurality of features defining inputs for the ML model and include at least one semiconductor manufacturing process operation selected from operations including etch/atomic layer etch, electrofill, chemical vapor deposition (CVD), atomic layer deposition (ALD), clean and strip, physical vapor deposition, and chemical/mechanical polishing, the plurality of ML models being based on training data to make data-driven decisions expressed as outputs;
• receiving, by one or more processors, a process definition for manufacturing a product with the semiconductor manufacturing tool;
• utilizing, by the one or more processors, one or more ML models from the plurality of ML models to estimate a performance of the process definition used in the semiconductor manufacturing tool;
• and
• presenting, on a display, results showing the estimate of the performance of the manufacturing of the product.

A machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:

• obtaining a plurality of machine-learning (ML) models, each of the plurality of ML models being related to predicting a performance metric for an operation of a semiconductor manufacturing tool, each ML model to utilize a plurality of features defining inputs for the ML model and include at least one semiconductor manufacturing process operation selected from operations including etch/atomic layer etch, electrofill, chemical vapor deposition (CVD), atomic layer deposition (ALD), clean and strip, physical vapor deposition, and chemical/mechanical polishing, the plurality of ML models being based on training data to make data-driven decisions expressed as outputs;
• receiving, by one or more processors, a process definition for manufacturing a product with the semiconductor manufacturing tool;
• utilizing, by the one or more processors, one or more ML models from the plurality of ML models to estimate a performance of the process definition used in the semiconductor manufacturing tool;
• and
• presenting, on a display, results showing the estimate of the performance of the manufacturing of the product.