Patent ID: 11921155
Assignee: TANGO AI CORP.
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A dice testing method configured to be executed by a dice testing system, wherein the dice testing system stores N test items and is configured to perform N tests on one of a plurality of dices according to the N test items, the dice testing system executes the dice testing method to test P dices, and N and P are positive integers, the dice testing method comprising:
implementing a first test step which includes: performing the N tests on P1 dices to obtain P1*N pieces of test data, wherein P1 is a positive integer, and P1≥100;
implementing an item selection step, which includes:
a data partition step, wherein the P1*N pieces of test data are divided into P11 calculation data sets and P12 verification data sets; wherein each of a plurality of calculation data sets is N pieces of test data corresponding to the N test items for a single one of the dices, and each of a plurality of verification data sets is the N pieces of test data corresponding to the N test items for a single one of the dices; wherein P11 is 70% of P, P12 is 30% of P, and P11 and P12 are each a positive integer greater than 0;
a data cleaning step, wherein each piece of test data in each of the calculation data sets is read one by one, so as to determine whether or not each of the calculation data sets contains any invalid test data; wherein, in response to one of the calculation data sets not containing any invalid test data, each piece of test data corresponding to the one of the calculation data sets is recorded as modeling data;
a modeling step, wherein N partial least square equations are established by using a partial least square method (PLS) and a significance test; wherein a dependent variable of each of the partial least square equations is predicted data obtained after the dice testing system makes one of the dices pass one of the test items; wherein an independent variable of each of the partial least square equations includes the test data corresponding to at least one of the test items that is not the dependent variable, and a P-value of a coefficient of each of the partial least square equations is less than 0.01; and
a verification step, wherein the test data included in each of the verification data sets is used to calculate a determination coefficient, a normalized root mean square error (NRMSE), a skew, and a kurtosis for each of the partial least square equations one by one; wherein, in response to the determination coefficient of one of the partial least square equations being within a range from 50 to 100, the NRMSE of the one of the partial least square equations being less than or equal to 0.2, the skew of the one of the partial least square equations being greater than or equal to −0.8, and the kurtosis of the one of the partial least square equations being greater than or equal to 2, the one of the partial least square equations is recorded as one of a plurality of predictable equations, and one of the test items corresponding to the dependent variable of the one of the partial least square equations is recorded as one of a plurality of predictable items; wherein, in response to each of the predictable equations predicting the predicted data of one of the dices in one of the predictable items, each of the predictable equations is configured to jointly define a prediction range with the modeling data corresponding to the one of the predictable items; wherein, in response to the test data corresponding to one of the dices and one of the predictable items in one of the verification data sets being within the prediction range, and an upper limit of the prediction range is 3 times of a standard deviation of the modeling data corresponding to the predictable item plus the predicted data, a lower limit of the prediction range is the predicted data minus the 3 times of the standard deviation; wherein a sum of the predictable items is defined as T; and

implementing a second test step which includes: performing, according to N−T test items that are not recorded as the predictable items, N−T tests on each of P−P1 dices to obtain (P−P1)*(N−T) pieces of test data, and using T partial least square equations and the (P−P1)*(N−T) pieces of test data to calculate (P−P1)*T pieces of predicted data; wherein T is a positive integer.