Predictive analytics for failure detection
A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor sing a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.
1. A computer-implemented method for failure prediction of a batch of manufactured objects, the method comprising:
receiving the batch of manufactured objects for the failure prediction by coupling the batch of manufactured objects to the computer;
generate manufactured object sample classifications for the batch of manufactured objects comprising integrated circuits by training, by the computing device using a simulation, a K Nearest Neighbor (K-NN) classifier by classifying a set of samples with uniformly distributed parameter values using a NN-based classification approach and NN vector space;
determining a centroid of failing ones of the samples in the set, based on the manufactured object sample classifications, the centroid being a geometric mean of the failing ones of the samples in the set;
generating a new set of samples with a distribution around the centroid of the failing ones of the samples in the set, the new set of samples relating to the batch of manufactured objects;
populating the NN vector space using the new set of samples;
classifying, using the trained K-NN classifier, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric and assigning weights to each of the samples in the new set based on shifted sampling locations relative to the centroid using an inherited weight scheme when a number of neighbors is equal to one and using a voting scheme when the number of neighbors is odd and greater than one; and
selectively accepting or physically discarding the batch of manufactured objects based on a failure probability estimate generated for the batch of manufactured objects, the failure probability estimate based on classifications for the new set of samples.
2. The computer-implemented method of claim 1 , wherein the centroid is the geometric mean of the failing ones of the samples in the set.
3. The computer-implemented method of claim 1 , wherein the set of samples comprises a set of vectors.
4. The computer-implemented method of claim 1 , wherein the sample classifications comprise pass classifications and fail classifications.
5. The computer-implemented method of claim 4 , wherein the nearest neighbor search is a k-nearest neighbor search, and said classifying step comprises inheriting a pass classification or a fail classification of a retrieved one of the samples, when a value of k is equal to one.
6. The computer-implemented method of claim 1 , further comprising:
building a k-nearest neighbor classifier; and
adjusting the k-nearest neighbor classifier based on the new set of samples.
7. The computer-implemented method of claim 1 , wherein said generating step uses mixed importance sampling to reduce classification variation by shifting a sampling location to the centroid of the failing ones of the sample in the set.
8. The computer-implemented method of claim 7 , wherein the new set of samples are classified by assigning weights to each of the samples in the new set based on the shifted sampling location.
9. The computer-implemented method of claim 7 , wherein the mixed importance sampling selectively drives an accuracy of and a speed in generating classifications for the new set of samples.
10. The computer-implemented method of claim 1 , further comprising arranging the new set of samples into a tree structure in preparation for the nearest neighbor search.
11. The computer-implemented method of claim 10 , further comprising selectively accepting or rejecting a given one of the manufactured objects in the batch to which a given one of the samples in the new set represents, based on a classification generated for the given one of the samples.
12. The computer-implemented method of claim 1 , wherein said classifying step comprises weighing respective contributions of the new set of samples by the distance metric.
13. The computer-implemented method of claim 1 , wherein each of the samples in the new set of samples is weighted by a value of 1/d, where d is a distance from a given one of the samples to the centroid.