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Browse Prior Art Database

Method and System to Detect Root Cause of Special Fail Patterns

IP.com Disclosure Number: IPCOM000235776D
Publication Date: 2014-Mar-25
Document File: 3 page(s) / 232K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system to detect a process window shift within wafers that may cause fail patterns in a functional test or a performance test. The methods detect predefined fail patterns using data mining methods and exclude faked signals of match.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

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Method and System to Detect Root Cause of Special Fail Patterns

In semiconductor fabrication, the parameters from inline electrical tests and functional tests can show different fail patterns, which can be impacted by a plurality of process steps. Data mining methods are needed to identify tools, chambers, and process shifts that can create the fail patterns.

Different methods can be used to classify fail patterns, but no method can detect the root cause of the fail pattern.

The novel contribution is a method to detect a process window shift within wafers that may cause fail patterns in a functional test or a performance test. The method excludes faked signals of match.

The core idea is to provide methods to detect predefined fail patterns by using data mining methods. The system comprises the following methods:


 Two methods to define fail patterns:
- To define standard fail patterns

    - To define special fail patterns that can be saved into a database  To detect tool, chamber, and sub-chamber related fail patterns
 To detect process window related fail patterns

The method also provides a statistical summary report, which can be used to drill down analysis, such as to create visual maps and charts.

Figure 1: Standard Fail Pattern Definition Method

Figure 2: Special Fail Pattern Definition Method

1


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The process for implementing the system and method in a preferred embodiment follows, and is represented in Figure 3.

Fetch Parametric Pattern


 Block 1000: Load input data into application. The input data can include lot_ids or wafer_ids, which have fail patterns. User can also add lot_ids or wafer_ids, which do not have fail patterns as the reference (optional).

 Block 1004: Select process sectors. User can pick up inline electrical data, physical limited yield, or product data for fail pattern root cause fetching.
 Block 1006: Select data module. User can select related process models or photo layers. The system can pick up related parameters to do analysis automatically.


 Block 1008: Define fail patters. See Fig. 1 and Fig. 2. The area that has a failed pattern is uses as a bad zone. The other is used as a good zone.


 Block 1010: Select parameter statistics that can be used for faked signal check in block 1016. Start query data from database.


 Block 1012...