Root Cause Distribution Determination Based On Layout Aware Scan Diagnosis Results

• US 20120297264A1
• Filed: 05/16/2012
• Published: 11/22/2012
• Est. Priority Date: 05/16/2011
• Status: Active Grant

• Current Assignee: Siemens Industry Software Limited (Siemens AG)
• Original Assignee: Mentor Graphics Corporation (Siemens AG)
• Inventors: Benware, Robert Brady
• Granted Patent: US 8,930,782 B2
• Time in Patent Office: Days
• US Class Current: 714/738
• CPC Class Codes: G01R 31/2836 Fault-finding or characterising G01R31/2822 - G01R31/2831 take precedence
• CPC Class Codes: G01R 31/3183 Generation of test inputs, e.g. test vectors, patterns or sequences
• CPC Class Codes: G01R 31/318307 computer-aided, e.g. automatic test program generator [ATPG], program translations, test program debugging
• CPC Class Codes: G01R 31/318342 by preliminary fault modelling, e.g. analysis, simulation
• CPC Class Codes: G01R 31/318511 Wafer Test
• CPC Class Codes: G01R 31/318544 Scanning methods, algorithms and patterns G01R31/3183 takes precedence
• CPC Class Codes: G06F 30/39 Circuit design at the physical level physical level design for reconfigurable circuits G06F30/347
• CPC Class Codes: G06F 30/398 Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] optical proximity correction [OPC] design processes G03F1/36
• CPC Class Codes: H01L 2027/11864 Yield or reliability
• CPC Class Codes: H01L 2027/11874 Layout specification, i.e. inner core region
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14
• CPC Class Codes: H01L 22/14 for electrical parameters, e.g. resistance, deep-levels, CV, diffusions by electrical means
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps
• CPC Class Codes: H01L 22/30 Structural arrangements specially adapted for testing or measuring during manufacture or treatment, or specially adapted for reliability measurements
• CPC Class Codes: H01L 2924/37001 Yield

Abstract

Aspects of the invention relate to yield analysis techniques for generating root cause distribution information. Suspect information for a plurality of failing dies is first generated using a layout-aware diagnosis method. Based on the suspect information, potential root causes for the plurality of failing dies, and suspect feature weights and total feature weights for each of the potential root causes may then be determined. Next, the probability information of observing a particular suspect that is related to a particular root cause may be extracted. Finally, an expectation-maximization analysis may be conducted for generating the root cause distribution information based on the probability information and the suspect information. Heuristic information may be used to prevent the analysis from over-fitting.

Claims

1. A method of yield analysis, comprising:

receiving failure files for a plurality of failing dies, and corresponding test patterns and circuit design information, the circuit design information comprising logic and layout information;

performing a layout-aware diagnosis based on the failure files, the test patterns and the circuit design information to derive suspect information for the plurality of failing dies, the suspect information comprising a plurality of suspects;

determining potential root causes for the plurality of failing dies based on the suspect information;

determining suspect feature weights and total feature weights for each of the potential root causes;

generating probability information of observing a particular suspect that is related to a particular root cause based on the suspect feature weights, the total feature weights, the suspect information and the circuit design information; and

generating root cause distribution information based on the probability information and the suspect information.

2. The method recited in claim 1, wherein the generating probability information comprises performing an expectation-maximization analysis.

3. The method recited in claim 2, wherein the performing expectation-maximization analysis comprises using heuristic information to eliminate one or more potential root causes.

4. The method recited in claim 2, wherein the performing expectation-maximization analysis comprises solving an over-fitting problem based on heuristic information.

5. The method recited in claim 1, wherein the suspects are arranged into symptoms.

6. The method recited in claim 1, wherein the suspect feature weights for open and bridge suspects are determined based, at least in part, on critical area determination.

7. A processor-readable medium storing processor-executable instructions for causing one or more processors to perform a method of yield analysis, the method comprising:

receiving failure files for a plurality of failing dies, and corresponding test patterns and circuit design information, the circuit design information comprising logic and layout information;

performing a layout-aware diagnosis based on the failure files, the test patterns and the circuit design information to derive suspect information for the plurality of failing dies, the suspect information comprising a plurality of suspects;

determining potential root causes for the plurality of failing dies based on the suspect information;

determining suspect feature weights and total feature weights for each of the potential root causes;

generating probability information of observing a particular suspect that is related to a particular root cause based on the suspect feature weights, the total feature weights, the suspect information and the circuit design information; and

generating root cause distribution information based on the probability information and the suspect information.

8. The processor-readable medium recited in claim 7, wherein the generating probability information comprises performing an expectation-maximization analysis.

9. The processor-readable medium recited in claim 8, wherein the performing expectation-maximization analysis comprises using heuristic information to eliminate one or more potential root causes.

10. The processor-readable medium recited in claim 8, wherein the performing expectation-maximization analysis comprises solving an over-fitting problem based on heuristic information.

11. A system comprising one or more processors, the one or more processors programmed to perform a method of yield analysis, the method comprising:

receiving failure files for a plurality of failing dies, and corresponding test patterns and circuit design information, the circuit design information comprising logic and layout information;

performing a layout-aware diagnosis based on the failure files, the test patterns and the circuit design information to derive suspect information for the plurality of failing dies, the suspect information comprising a plurality of suspects;

determining potential root causes for the plurality of failing dies based on the suspect information;

determining suspect feature weights and total feature weights for each of the potential root causes;

generating probability information of observing a particular suspect that is related to a particular root cause based on the suspect feature weights, the total feature weights, the suspect information and the circuit design information; and

generating root cause distribution information based on the probability information and the suspect information.

12. The system recited in claim 11, wherein the generating probability information comprises performing an expectation-maximization analysis.

13. The system recited in claim 12, wherein the performing expectation-maximization analysis comprises using heuristic information to eliminate one or more potential root causes.

14. The system recited in claim 12, wherein the performing expectation-maximization analysis comprises solving an over-fitting problem based on heuristic information.