Abstract: Methods and apparatus for generating a standard pattern for data signals from a set of multiple data signals are provided. The standard pattern consists of a signal length, a centerline, an upper limit, and a lower limit. One of methods comprises, receiving first and second data signals, determining a standard pattern length for each of the first and second data signals, sampling each of the first and second data signals by as much as the determined standard pattern length, aligning the sampled first and second data signals, and generating a standard pattern for the first and second data signals by overlapping the aligned first and second data signals, wherein the generated standard pattern is a standard pattern having reflected thereinto upper and lower limit ranges that are determined using levels of the aligned first and second data signals.

Abstract: Methods and apparatus for generating a standard pattern for data signals from a set of multiple data signals are provided. The standard pattern consists of a signal length, a centerline, an upper limit, and a lower limit. One of methods comprises, receiving first and second data signals, determining a standard pattern length for each of the first and second data signals, sampling each of the first and second data signals by as much as the determined standard pattern length, aligning the sampled first and second data signals, and generating a standard pattern for the first and second data signals by overlapping the aligned first and second data signals, wherein the generated standard pattern is a standard pattern having reflected thereinto upper and lower limit ranges that are determined using levels of the aligned first and second data signals.

Abstract: Provided are a method of clustering defects generated in bad samples shown on a defect map of bad samples including bad products, and an apparatus thereof. The defect cell clustering method includes generating a sample defect map showing a defect cell distribution by cell positions of bad samples comprised of products each including one or more defect cells among products each partitioned into a plurality of cells, selecting at least some cell positions having one or more defect cells as clustering targets from the sample defect map, selecting one or more suspected bad equipments for each of cell positions included in the clustering targets using pass equipment information for the product, and grouping the clustering targets into one or more clusters according to position coherence between a first cell position and a second cell position included in one cluster, the cell position and the second cell position each having at least one suspected bad equipment.

Abstract: A method for verifying bad pattern in time series sensing data by calculating a bad pattern error rate, which can be applied to the time series sensing data measured and produced from a predetermined sensor provided in predetermined equipment, and an apparatus thereof are provided. The method includes receiving information on the bad pattern applied to time series sensing data measured by a suspicious sensor, accessing the time series sensing data of each product, generated by the suspicious sensor during a verification period, calculating similarity measures between the bad pattern based on the bad pattern information and the time series sensing data for each product, and calculating an error rate of the bad pattern based on the similarity measures.

Abstract: A method for verifying bad pattern in time series sensing data by calculating a bad pattern error rate, which can be applied to the time series sensing data measured and produced from a predetermined sensor provided in predetermined equipment, and an apparatus thereof are provided. The method includes receiving information on the bad pattern applied to time series sensing data measured by a suspicious sensor, accessing the time series sensing data of each product, generated by the suspicious sensor during a verification period, calculating similarity measures between the bad pattern based on the bad pattern information and the time series sensing data for each product, and calculating an error rate of the bad pattern based on the similarity measures.

Abstract: A method for verifying bad pattern in time series sensing data by calculating a bad pattern error rate, which can be applied to the time series sensing data measured and produced from a predetermined sensor provided in predetermined equipment, and an apparatus thereof are provided. The method includes receiving information on the bad pattern applied to time series sensing data measured by a suspicious sensor, accessing the time series sensing data of each product, generated by the suspicious sensor during a verification period, calculating similarity measures between the bad pattern based on the bad pattern information and the time series sensing data for each product, and calculating an error rate of the bad pattern based on the similarity measures.

Abstract: A system and method for analyzing a product fabrication process are disclosed. A product yield analysis system according to an exemplary embodiment of the present disclosure includes a data extraction unit that extracts sensor data from a plurality of sensors arranged in equipment for fabricating a product, a reference signal generation unit that generates a reference signal for each of the plurality of sensors from the sensor data, and a sensor detection unit that detects one or more sensors having a correlation with a yield of the product using the sensor data and the reference signal.

Abstract: Provided are a method of clustering defects generated in bad samples shown on a defect map of bad samples including bad products, and an apparatus thereof. The defect cell clustering method includes generating a sample defect map showing a defect cell distribution by cell positions of bad samples comprised of products each including one or more defect cells among products each partitioned into a plurality of cells, selecting at least some cell positions having one or more defect cells as clustering targets from the sample defect map, selecting one or more suspected bad equipments for each of cell positions included in the clustering targets using pass equipment information for the product, and grouping the clustering targets into one or more clusters according to position coherence between a first cell position and a second cell position included in one cluster, the cell position and the second cell position each having at least one suspected bad equipment.