公开(公告)号：US11074376B2

公开(公告)日：2021-07-27

申请号：US15497489

申请日：2017-04-26

Applicant: UNITED MICROELECTRONICS CORP.

Inventor： Ya-Ching Cheng ,  Chun-Liang Hou ,  Chien-Hung Chen ,  Wen-Jung Liao ,  Min-Chin Hsieh ,  Da-Ching Liao ,  Li-Chin Wang

IPC: G06F30/20

Abstract: A method for analyzing a process output and a method for creating an equipment parameter model are provided. The method for analyzing the process output includes the following steps: A plurality of process steps are obtained. A processor obtains a step model set including a plurality of first step regression models, each of which represents a relationship between N of the process steps and a process output. The processor calculates a correlation of each of the first step regression models. The processor picks up at least two of the first step regression models to be a plurality of second step regression models whose correlations are ranked at top among the correlations of the first step regression models. The processor updates the step model set by a plurality of third step regression models, each of which represents a relationship between M of the process steps and the process output.

公开(公告)号：US20180314773A1

公开(公告)日：2018-11-01

申请号：US15497489

申请日：2017-04-26

Applicant: UNITED MICROELECTRONICS CORP.

Inventor： Ya-Ching Cheng ,  Chun-Liang Hou ,  Chien-Hung Chen ,  Wen-Jung Liao ,  Min-Chin Hsieh ,  Da-Ching Liao ,  Li-Chin Wang

IPC: G06F17/50

Abstract: A method for analyzing a process output and a method for creating an equipment parameter model are provided. The method for analyzing the process output includes the following steps: A plurality of process steps are obtained. A processor obtains a step model set including a plurality of first step regression models, each of which represents a relationship between N of the process steps and a process output. The processor calculates a correlation of each of the first step regression models. The processor picks up at least two of the first step regression models to be a plurality of second step regression models whose correlations are ranked at top among the correlations of the first step regression models. The processor updates the step model set by a plurality of third step regression models, each of which represents a relationship between M of the process steps and the process output.