Dielectric reliability assessment for advanced semiconductors

• US 9,026,981 B2
• Filed: 06/19/2014
• Issued: 05/05/2015
• Est. Priority Date: 05/22/2013
• Status: Expired due to Fees

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Li, Baozhen, Stathis, James H., Wu, Ernest Y.
• Primary Examiner(s): Ngo, Brian
• Application Number:
  US14/308,835
• Publication Number:
  US 20140351785A1
• Time in Patent Office: 320 Days
• Field of Search: 716/106, 716/111, 716/136, 716/112, 716/113, 324/551, 324/548, 702/65, 702/184
• 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,

wherein the parameter include a Weibull shape factor (β

) and scale factor (τ

) and wherein the Weibull shape factor (β

), the scale factor (τ

) and the clustering factor (α

) are extracted according to;

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

a non-transitory 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,

wherein the parameter include a Weibull shape factor (β

) and scale factor (τ

) and wherein the Weibull shape factor (β

), the scale factor (τ

) and the clustering factor (α

) are extracted according to;

5. The computer program product of claim 4, wherein the testing includes using voltage and current for acceleration of the dielectric breakdown.

6. The computer program product of claim 4, 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.

7. 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,

wherein the parameter include a Weibull shape factor (β

) and scale factor (τ

) and wherein the Weibull shape factor (β

), the scale factor (τ

) and the clustering factor (α

) are extracted according to;

8. The computer system of claim 7, wherein the testing includes using voltage and current for acceleration of the dielectric breakdown.

9. The computer system of claim 7, 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.