Semiconductor Energy Laboratory Co., Ltd.

Ryo Nakazato

Tatsuya Okano

Atsuya Tokinosu

A novel classification device is provided. The classification device includes a memory unit, a processing unit, and a classifier. A plurality of pieces of image data and a discriminative model are stored in the memory unit. Each of the plurality of pieces of image data is image data determined to contain a defect. The discriminative model includes an input layer, an intermediate layer, and an output layer. First to n-th (n is an integer greater than or equal to 2) image data of the plurality of pieces of image data are supplied to the processing unit. The processing unit has a function of outputting feature values of the first to the n-th image data (a first to an n-th feature value) on the basis of the discriminative model. A feature value output from the processing unit is a numerical value of a neuron included in the intermediate layer. The first to the n-th feature value output from the processing unit are supplied to the classifier. The classifier has a function of performing clustering of the first to the n-th image data on the basis of the first to the n-th feature value.

Classification device, image classification method, and pattern inspection device

A learning data generation device that can generate learning data suitable for learning of an identification model. The learning data generation device has a function of cutting out part of first image data as second image data, a function of generating a two-dimensional graphic corresponding to the area of the second image data and representing a pseudo defect, a function of generating third image data by combining the second image data and the two-dimensional graphic, and a function of assigning a label corresponding to the two-dimensional graphic to the third image data. By using the third image data for learning of the identification model, a highly accurate identification model can be generated.

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Kanagawa, Japan

Ryo Nakazato