Dielectric reliability assessment for advanced semiconductors

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 projection is made using according to:

F

2

=

1

-

{

1

+

A

2

A

1

⁢

(

(

1

-

F

1

)

-

1

/

α

-

1

)

}

-

α

≅

1

-

(

1

-

F

1

)

A

2

A

1



F

1

⪡

1

wherein A 1 is a size of the tested macro, A 2 is the size of the larger area of the advanced semiconductor, F 1 is a cumulative failure of the tested macro, and F 2 is a cumulative failure of the larger area of the advanced semiconductor.

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:

F

=

1

-

(

1

+

1

α

⁢

(

t

τ

)

β

)

-

α

.

6. 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 projection is made using according to:

F

2

=

1

-

{

1

+

A

2

A

1

⁢

(

(

1

-

F

1

)

-

1

/

α

-

1

)

}

-

α

≅

1

-

(

1

-

F

1

)

A

2

A

1



F

1

⪡

1

.

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:

F

=

1

-

(

1

+

1

α

⁢

(

t

τ

)

β

)

-

α

.

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;

F

2

=

1

-

{

1

+

A

2

A

1

⁢

(

(

1

-

F

1

)

-

1

/

α

-

1

)

}

-

α

≅

1

-

(

1

-

F

1

)

A

2

A

1



F

1

⪡

1

wherein A 1 is a size of the test macro, A 2 is the size of the larger area of the advanced semiconductor, F 1 is a cumulative failure of the tested macro, and F 2 is a cumulative failure of the larger area of the advanced semiconductor.

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 11 , 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:

F

=

1

-

(

1

+

1

α

⁢

(

t

τ

)

β

)

-

α

.