Abnormality measuring method and abnormality measuring apparatus

• US 10,281,519 B2
• Filed: 10/05/2016
• Issued: 05/07/2019
• Est. Priority Date: 03/23/2016
• Status: Active Grant

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Hsu, Yao-Chung, Chang, Sen-Chia, Hsieh, Tsung-Jung
• Primary Examiner(s): Bui, Bryan
• Application Number:
  US15/286,394
• Publication Number:
  US 20170276720A1
• Time in Patent Office: 944 Days
• CPC Class Codes: G01R 31/2601 Apparatus or methods therefor G01R31/2607, G01R31/2642 take precedence
• CPC Class Codes: G01R 31/2642 Testing semiconductor operation lifetime or reliability, e.g. by accelerated life tests
• CPC Class Codes: G08B 21/18 Status alarms G08B21/02 takes precedence
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing

Abstract

An abnormality measuring method and an abnormality measuring apparatus of equipment are provided. The abnormality measuring method includes the following steps: acquiring a feature sequence corresponding to a life cycle according to recipe information and sensing information, wherein the feature sequence includes a plurality of feature subset sequences, and the life cycle is relative to a plurality of process runs; performing repeatedly a life segment analyzing process to acquire a plurality of life segments of the life cycle and each of the plurality of the feature subset sequences corresponding to one of the plurality of life segments; building a corresponding trending distribution of each of the plurality of life segments according to a corresponding feature subset sequence of the life segment; and determining whether to send an alarm message according to a plurality of trending distributions.

Claims

1. An apparatus for measuring abnormality, comprising:

a feature extraction module, acquiring a feature sequence corresponding to a life cycle according to a recipe information and a sensing information, wherein the feature sequence includes a plurality of feature subset sequences, and the life cycle is related to a plurality of process runs, the apparatus has the recipe information at each process run, the recipe information includes a plurality of recipe values, the recipe values are recipe set points, and the sensing information corresponding to the recipe information is fetched by sensors;

a life segment analyzing module coupled to the feature extraction module, performing repeatedly a life segment analyzing process to acquire a plurality of life segments of the life cycle and each of the plurality of feature subset sequences corresponding to one of the plurality of life segments, wherein the life segment analyzing module further performs the life segment analyzing process according to a analyzing start point to acquire a segment transformation point corresponding to one of the plurality of life segments, and redefines repeatedly the analyzing start point according to the segment transformation point until no more segment transformation point is acquired;

wherein an initial analyzing start point is defined according to a start point of the life cycle;

a trending analyzing module coupled to the feature extraction module and the life segment analyzing module, building a corresponding trending distribution of each of the plurality of life segments according to a corresponding feature subset sequence of the life segment; and

a warning module coupled to the trending analyzing module, generating a warning threshold correspondingly according to the plurality of trending distributions, and comparing the plurality of trending distributions with the warning threshold to determine whether to announce an alarm message.

2. The apparatus of claim 1, wherein the life segment analyzing process includes:

selecting randomly one feature subset sequence from the plurality of feature subset sequences;

analyzing each of the plurality of feature subset sequences for a corresponding distribution status starting from the analyzing start point to acquire a candidate transformation point and a feature distinguishing capability index of the candidate transformation point;

repeating the above steps until reaching an iterative critical value;

defining the candidate transformation point having a highest feature distinguishing capability index to be the segment transformation point; and

defining a feature subset sequence corresponding to the segment transformation point having the highest feature distinguishing capability index to be the feature subset sequence of the life segment.

3. The apparatus of claim 2, the life segment analyzing module further gets a first window according to the start point of the life cycle in a selected feature subset sequence, and gets a shifting reference point and a second window corresponding to the shifting reference point in the selected feature subset sequence, then analyzes the start point according to the analyzing start point and a window shifting amount, and compares the first window with the second window to identify whether a distribution difference of the second window corresponding to the first window is greater than a critical value, and repeats the above steps while the distribution difference of the second window with respect to the first window is less than a critical value until the shifting reference point reaches an end point of the life cycle, and defines the shifting point to be the candidate transformation point while the distribution difference of the second window with respect to the first window is greater than the critical value.

4. The apparatus of claim 2, wherein the life segment analyzing module further determines window lengths of both the first window and the second window according to the analyzing start point.