Method for classifying microelectronic dies using die level cherry picking system based on dissimilarity matrix

• US 7,930,266 B2
• Filed: 03/09/2006
• Issued: 04/19/2011
• Est. Priority Date: 03/09/2006
• Status: Expired due to Fees

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Tuv, Eugene, Borisov, Alexander, Shahapurkar, Somnath
• Primary Examiner(s): Sparks; Donald
• Assistant Examiner(s): OLUDE AFOLABI, OLATOYOSI
• Application Number:
  US11/372,610
• Publication Number:
  US 20070239652A1
• Time in Patent Office: 1,867 Days
• Field of Search: 706/62, 703/2, 257/620, 361/749
• US Class Current: 706/62
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A computer implemented method for ordering a plurality of entities by computing a dissimilarity matrix based on a plurality of probabilities. The pluralities of probabilities are determined based on a plurality of classes. A weighted distance matrix is computed based the dissimilarity matrix. A plurality of rank ordered sequence candidates based at least in part on the sum of weighted distances between neighboring entities in the rank ordered sequence is calculated. Other embodiments are described in the claims.

Claims

1. A computer implemented method for ordering a plurality of microelectronic dies, the computer implemented method comprising:

computing a dissimilarity matrix based on a plurality of probabilities of classifications, wherein the classifications are determined by a characteristic of each microelectronic die of the plurality of microelectronic dies, wherein each microelectronic die of the plurality includes an integrated circuit, wherein the characteristic includes clocking speed of each microelectronic die of the plurality of microelectronic dies, and wherein the characteristic of each microelectronic die is classified by a die level cherry picking system;

computing a weighted distance matrix based on the following;

the dissimilarity matrix, weighted distances between at least one pair of the microelectronic dies of the plurality of microelectronic dies, and geometric distances between neighboring microelectronic dies of the plurality of microelectronic dies;

generating a plurality of rank ordered sequence of candidates representing the plurality of microelectronic dies, the generating is based on the weighted distance matrix; and

selecting, from the plurality of rank ordered sequence of candidates, a rank ordered sequence representing the order of the plurality of microelectronic dies, the selecting based on a sum of weighted distances between neighboring microelectronic dies in the rank ordered sequence.

2. The computer implemented method of claim 1, wherein the sum of weighted distances is a smallest sum from among the rank ordered sequence of candidates.

3. The computer implemented method of claim 1, wherein the characteristic of each microelectronic die comprises:

chemical usage, wafer sorting, power consumption, and electrical performance binning.

4. The computer implemented method of claim 1, wherein the dissimilarity matrix includes dissimilarity elements S_km,such that

5. The computer implemented method of claim 1, wherein the weighted distance matrix includes elements d_km,such that

d_km=W·

g_km+S_km

wherein W represents a weight applied to the geometric distance to assign a degree of importance to the geometric distance, and wherein g_km represents the geometric distance between the microelectronic dies k and m.

6. The computer implemented method of claim 1, wherein generating the plurality of rank ordered sequence of candidates further comprising applying a one dimensional scaling to the weighted distance matrix.

7. The computer implemented method of claim 1, further comprising:

determining based on historical data the plurality of probabilities of classifications for each of the microelectronic die of the plurality of microelectronic dies; and

retrieving the plurality of microelectronic dies from a container containing the plurality of microelectronic dies, the retrieving based on the rank ordered sequence.

8. The computer implemented method of claim 7, wherein the container is a wafer containing the plurality of microelectronic dies.

9. The computer implemented method of claim 1, wherein the weighted distance matrix and the dissimilarity matrix have equal number of elements.

10. The computer implemented method of claim 1, wherein the plurality of microelectronic dies is contained in a wafer.

11. A computer implemented system for ordering a plurality of microelectronic dies, the computer implemented system comprising:

a memory to store historical data;

means for determining, based on the historical data, a plurality of probabilities of classifications, wherein the classifications are determined by a characteristic of each microelectronic die of the plurality of microelectronic dies, and wherein each microelectronic die of the plurality includes an integrated circuit, wherein the characteristic includes clocking speed of each microelectronic die of the plurality of microelectronic dies, and wherein the characteristic of each microelectronic die is classified by a die level cherry picking system;

means for representing a dissimilarity between a first microelectronic die and a second microelectronic die based on the determined probability, wherein the first and the second microelectronic dies from the plurality of microelectronic dies;

means for representing a weighted distance between the first microelectronic die and the second microelectronic die based on a weight, the means for representing the weighted distance based on a geometric distance and the dissimilarity between the first microelectronic die and the second microelectronic die;

means for generating, via the means for representing the weighted distance, a plurality of rank ordered sequence of candidates representing the plurality of microelectronic dies; and

means for selecting, from the plurality of rank ordered sequence of candidates, a rank ordered sequence representing the order of the plurality of microelectronic dies, the means for selecting based on a sum of weighted distances between neighboring microelectronic dies in the rank ordered sequence.

12. The computer implemented system of claim 11, wherein the sum of weighted distances is a smallest sum from among the rank ordered sequence of candidates.

13. The computer implemented system of claim 11, wherein the means for determining the plurality of probabilities of classifications is based on an artificial intelligence based module which includes a random subspace classifier.

14. The computer implemented system of claim 11, wherein dissimilarity between the first microelectronic die and the second microelectronic die is determined according to the formula:

15. The computer implemented system of claim 14, wherein the weighted distance between the first microelectronic die and the second microelectronic die is determined according to the formula:

d_km=W·

g_km+S_km

wherein d_km is the weighed distance between microelectronic dies k and m, wherein W represents a weight applied to the geometric distance to assign a degree of importance to the geometric distance, and wherein g_km represents the geometric distance between the microelectronic die k and microelectronic die m.

16. The computer implemented system of claim 11, wherein the means for representing a weighted distance and the means for representing a dissimilarity have equal number of elements.

17. The computer implemented system of claim 11, wherein the plurality of microelectronic dies is contained in a wafer.

18. A machine-accessible storage medium that provides instructions that, when executed by a processor, causes the processor to:

compute a dissimilarity matrix based on a plurality of probabilities of classifications and a plurality of microelectronic dies, wherein the classifications are determined based on a characteristic of each of the plurality of microelectronic dies, wherein each microelectronic die of the plurality includes an integrated circuit, wherein the characteristic includes clocking speed of each microelectronic die of the plurality of microelectronic dies, and wherein the characteristic of each microelectronic die is classified by a die level cherry picking system;

compute a weighted distance matrix based on the following;

a weighted distance between one pair of the microelectronic dies of the plurality of microelectronic dies, a geometric distance between a first microelectronic die and a second microelectronic die from the plurality of microelectronic dies, and the dissimilarity matrix;

generate a plurality of rank ordered sequence of candidates representing the plurality of microelectronic dies, the generating is based on the weighted distance matrix; and

select, from the plurality of rank ordered sequence of candidates, a rank ordered sequence representing the order of the plurality of microelectronic dies, the selecting based on a sum of weighted distances between neighboring microelectronic dies in the rank ordered sequence.

19. The machine-accessible storage medium of claim 18, wherein the sum of weighted distances is a smallest sum from among the rank ordered sequence of candidates.

20. The machine-accessible storage medium of claim 18, wherein the dissimilarity matrix includes dissimilarity elements s_km, such that

21. The machine-accessible storage medium of claim 20, wherein the weighted distance matrix includes elements d_km such that

d_km=W·

g_km+S_km

wherein W represents a weight applied to the geometric distance to assign a degree of importance to the geometric distance, and wherein g_km represents the geometric distance between the first microelectronic die k and the second microelectronic die m.

22. The machine-accessible storage medium of claim 18, wherein the plurality of rank ordered sequence of candidates is generated by instructions that when executed on the processor cause the processor to apply a one dimensional scaling to the weighted distance.

23. The machine-accessible storage medium of claim 18, wherein the weighted distance matrix and the dissimilarity matrix have equal number of elements.

24. The machine-accessible storage medium of claim 18, wherein the plurality of microelectronic dies is contained in a wafer.