Taiwan Semiconductor Manufacturing Comp. Ltd.

Tzu-Cheng Lin

Chien Rhone Wang

Kewei Zuo

Jerry Wang

Chih-Wei Lai

Y Y Peng

Tsang-Jiuh Wu

Hung-Pin Chang

Zi-Jheng Liu

Ming-Tan Lee

Yu-Jen Cheng

Yy Peng

The current disclosure describes techniques for managing vertical alignment or overlay in semiconductor manufacturing using machine learning. Alignments of interconnection features in a fan-out WLP process are evaluated and managed through the disclosed techniques. Big data and neural networks system are used to correlate the overlay error source factors with overlay metrology categories. The overlay error source factors include tool related overlay source factors, wafer or die related overlay source factors and processing context related overlay error source factors.

Machine learning on overlay management

The current disclosure describes techniques for managing vertical alignment or overlay in semiconductor manufacturing using machine learning. Alignments of interconnection features in a fan-out WLP process are evaluated and managed through the disclosed techniques. Big data and machine learning are used to train a classification that correlates the overlay error source factors with overlay metrology categories. The overlay error source factors include tool signals. The trained classification includes a base classification and a Meta classification.

Machine learning on overlay virtual metrology

A system and method is disclosed for a quality control and/or inspection procedure for assembly line processes. The disclosed system and method enable automatic optical inspection of a device during different stages of manufacture as well as in its finished form. The disclosed system and method enable the automatic quality control process to be self-learning, dynamic, and to identify and classify defects in real time.

System and method for automatic quality control for assembly line processes

Methods and apparatus for performing end point determination. A method includes receiving a wafer into an etch tool chamber for performing an RIE etch; beginning the RIE etch to form vias in the wafer; receiving in-situ measurements of one or more physical parameters of the etch tool chamber that are correlated to the RIE etch process; providing a virtual metrology model for the RIE etch in the chamber; inputting the received in-situ measurements to the virtual metrology model for the RIE etch in the chamber; executing the virtual metrology model to estimate the current via depth; comparing the estimated current via depth to a target depth; and when the comparing indicates the current via depth is within a predetermined threshold of the target depth; outputting a stop signal. An apparatus for use with the method embodiment is disclosed.

Apparatus and methods for end point determination in reactive ion etching

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Hsinchu, Taiwan

Tzu-Cheng Lin