Information processing device and method

An information processing device comprising

• a processor configured to:
• determine a cluster of chips not satisfying a first criterion on the basis of a first data set being a set of inspection results for multiple chips formed on a wafer or cut out from the wafer, the inspection results being obtained by executing a first inspection on the multiple chips;
• calculate a second data set being a set of feature amounts of chips related to a distance to the cluster;
• acquire numerical information corresponding to a possibility that a chip has early failure by inputting the first data set and the second data set to a trained machine learning model configured to output the numerical information;
• and
• estimate a chip with early failure on the basis of the numerical information.

A method comprising:

• acquiring a first data set and a second data set, the first data set being a set of inspection results for multiple chips formed on a wafer or cut out from the wafer, the inspection results being obtained by executing a first inspection on the multiple chips, the second data set indicating, for each of the multiple chips, presence or absence of early failure obtained by executing a second inspection on the multiple chips;
• determining, based on the first data set, a cluster of chips not satisfying a first criterion out of the multiple chips;
• calculating a third data set being a set of feature amounts of chips related to a distance to the cluster;
• and
• executing training of a machine learning model by using, as input data, the first data set and the third data set and using the second data set as correct answer data.

A method comprising:

• determining a cluster of chips not satisfying a first criterion on the basis of a first data set being a set of inspection results for multiple chips formed on a wafer or cut out from the wafer, the inspection results being obtained by executing a first inspection on the multiple chips;
• calculating a second data set being a set of feature amounts of chips related to a distance to the cluster;
• acquiring numerical information corresponding to a possibility that a chip has early failure by inputting the first data set and the second data set to a trained machine learning model configured to output the numerical information;
• and
• estimating a chip with early failure on the basis of the numerical information.