The Carinthia dataset contains Scanning Electron Microscope (SEM) images of defects found on one production layer of unstructured semiconductor wafers. The dataset consists of 4,591 images unevenly distributed in six defect classes. The dataset's description is available in the 'carinthia_dataset.html' file, and the images themselves can be found in the 'data.zip' file.

The Carinthia dataset contains Scanning Electron Microscope (SEM) images of defects found on one production layer of unstructured semiconductor wafers. The dataset consists of 4,591 images unevenly distributed in six defect classes. The dataset's description is available in the 'carinthia_dataset.html' file, and the images themselves can be found in the 'data.zip' file.

The Carinthia dataset contains Scanning Electron Microscope (SEM) images of defects found on one production layer of unstructured semiconductor wafers. The dataset consists of 4,591 images unevenly distributed in six defect classes. The dataset's description is available in the 'carinthia_dataset.html' file, and the images themselves can be found in the 'data.zip' file.

This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. Out of these time-dependent sensor data, so called key numbers (KNs) are extracted using a certain time frame in the individual sensor recordings judged by experts to be important for the process. To keep the entire production as stable as possible, the KNs are used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given KNs are provided. The aim is to find correlations between the wafer test data and the KNs in order to identify the root cause. The given data is divided into three data sets: "process1.csv", "process2.csv" and "response.csv". "process1.csv" and "process2.csv" represent the extracted KNs from two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. The exact column structure is given as follows:
for "process1.csv" and "process2.csv": lot: the lot number
wafer: the wafer number
KN1: the recordings of the first sensor
KN2: the recordings of the second sensor
.
.
.
KN50: the recordings of the last sensor "KN1"-"KN36" belongs to "process1" and "KN37"-"KN50" belongs to "process2". for "response.csv": lot: the lot number
wafer: the wafer number
response: the numerical test values
class: the "good"/"bad" classification depending on the response value (threshold: 0,75)

This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. Out of these time-dependent sensor data, so called key numbers (KNs) are extracted using a certain time frame in the individual sensor recordings judged by experts to be important for the process. To keep the entire production as stable as possible, the KNs are used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given KNs are provided. The aim is to find correlations between the wafer test data and the KNs in order to identify the root cause. The given data is divided into three data sets: "process1.csv", "process2.csv" and "response.csv". "process1.csv" and "process2.csv" represent the extracted KNs from two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. The exact column structure is given as follows:
for "process1.csv" and "process2.csv": lot: the lot number
wafer: the wafer number
KN1: the recordings of the first sensor
KN2: the recordings of the second sensor
.
.
.
KN50: the recordings of the last sensor "KN1"-"KN36" belongs to "process1" and "KN37"-"KN50" belongs to "process2". for "response.csv": lot: the lot number
wafer: the wafer number
response: the numerical test values
class: the "good"/"bad" classification depending on the response value (threshold: 0,75)

This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause. The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).

This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause. The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).

This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause. The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment. The exact column structure is given as follows: for "equipment1.csv" and "equipment2.csv": lot: the lot number wafer: the wafer number timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors) sensor_1: the recordings of the first sensor sensor_2: the recordings of the second sensor ... sensor_56: the recordings of the last sensor "sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)

This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause. The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment. The exact column structure is given as follows: for "equipment1.csv" and "equipment2.csv": lot: the lot number wafer: the wafer number timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors) sensor_1: the recordings of the first sensor sensor_2: the recordings of the second sensor ... sensor_56: the recordings of the last sensor "sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)

This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause. The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment. The exact column structure is given as follows: for "equipment1.csv" and "equipment2.csv": lot: the lot number wafer: the wafer number timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors) sensor_1: the recordings of the first sensor sensor_2: the recordings of the second sensor ... sensor_56: the recordings of the last sensor "sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)