DIELECTRIC RELIABILITY ASSESSMENT FOR ADVANCED SEMICONDUCTORS

• US 20140351785A1
• Filed: 06/19/2014
• Published: 11/27/2014
• Est. Priority Date: 05/22/2013
• Status: Active Grant

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: LI, BAOZHEN, STATHIS, JAMES H., WU, ERNEST Y.
• Granted Patent: US 9,026,981 B2
• Time in Patent Office: Days
• US Class Current: 716/136
• CPC Class Codes: G01R 31/2831 Testing of materials or semi-finished products, e.g. semiconductor wafers or substrates G01R31/318511 takes precedence; testing during manufacture H01L22/00
• 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 22/14 for electrical parameters, e.g. resistance, deep-levels, CV, diffusions by electrical means
• CPC Class Codes: H01L 22/34 Circuits for electrically characterising or monitoring manufacturing processes, e. g. whole test die, wafers filled with test structures, on-board-devices incorporated on each die, process control monitors or pad structures thereof, devices in scribe line switching, multiplexing, gating devices G01R19/25; process control with lithography, e.g. dose control, G03F7/20; structures for alignment control by optical means G03F7/70633

Abstract

Embodiments relate to methods, computer systems and computer program products for performing a dielectric reliability assessment for an advanced semiconductor. Embodiments include receiving data associated with a test of a macro of the advanced semiconductor to a point of dielectric breakdown. Embodiments also include scaling the data for the macro down to a reference area and extracting a parameter for a Weibull distribution from the scaled down data for the reference area. Embodiments further include deriving a cluster factor (α) from the scaled down data for the reference area and projecting a failure rate for a larger area of the advanced semiconductor based on the extracted parameter, the cluster factor and the recorded data associated with the dielectric breakdown of the macro.

Claims

1. A computer implemented method for performing a dielectric reliability assessment for an advanced semiconductor, the method comprising:

receiving data associated with a test of a macro of the advanced semiconductor to a point of dielectric breakdown;

scaling, by a processor, the data for the macro down to a reference area;

extracting a parameter for a Weibull distribution from the scaled down data for the reference area;

deriving a cluster factor (α

) from the scaled down data for the reference area; and

projecting a failure rate for a larger area of the advanced semiconductor based on the extracted parameter, the cluster factor and the recorded data associated with the dielectric breakdown of the macro.

2. The computer implemented method of claim 1, wherein the testing includes using voltage and current for acceleration of the dielectric breakdown.

3. The computer implemented method of claim 1, wherein a size of the reference area is selected to be small enough that failure time data for the reference area is accurately represented by a Weibull distribution.

4. The computer implemented method of claim 1, wherein the parameter include a Weibull shape factor (β

) and scale factor (τ

).

5. The computer implemented method of claim 4, wherein the Weibull shape factor (β

), the scale factor (τ

) and the clustering factor (α

) are extracted according to;

6. A computer program product for performing a dielectric reliability assessment for an advanced semiconductor, the computer program product comprising:

a tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising;

receiving data associated with a test of a macro of the advanced semiconductor to a point of dielectric breakdown;

scaling the data for the macro down to a reference area;

extracting a parameter for a Weibull distribution from the scaled down data for the reference area;

deriving a cluster factor (α

) from the scaled down data for the reference area; and

projecting a failure rate for a larger area of the advanced semiconductor based on the extracted parameter, the cluster factor and the recorded data associated with the dielectric breakdown of the macro.

7. The computer program product of claim 6, wherein the testing includes using voltage and current for acceleration of the dielectric breakdown.

8. The computer program product of claim 6, wherein a size of the reference area is selected to be small enough that failure time data for the reference area is accurately represented by a Weibull distribution.

9. The computer program product of claim 6, wherein the parameter include a Weibull shape factor (β

) and scale factor (τ

).

10. The computer program product of claim 9, wherein the Weibull shape factor (β

), the scale factor (τ

) and the clustering factor (α

) are extracted according to;

11. A computer system for performing a dielectric reliability assessment for an advanced semiconductor, the system comprising:

a processor configured to communicate with a memory and one or more auxiliary storage devices, the processor configured to perform a method comprising;

receiving data associated with a test of a macro of the advanced semiconductor to a point of dielectric breakdown;

scaling the data for the macro down to a reference area;

extracting a parameter for a Weibull distribution from the scaled down data for the reference area;

deriving a cluster factor (α

) from the scaled down data for the reference area; and

projecting a failure rate for a larger area of the advanced semiconductor based on the extracted parameter, the cluster factor and the recorded data associated with the dielectric breakdown of the macro.

12. The computer system of claim 11, wherein the testing includes using voltage and current for acceleration of the dielectric breakdown.

13. The computer system of claim 11, wherein a size of the reference area is selected to be small enough that failure time data for the reference area is accurately represented by a Weibull distribution.

14. The computer system of claim 13, wherein the parameter include a Weibull shape factor (β

) and scale factor (τ

).

15. The computer system of claim 14, wherein the Weibull shape factor (β

), the scale factor (τ

) and the clustering factor (α

) are extracted according to;