Equipment Sensor Data from Semiconductor Frontend Production

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)本数据集贴合半导体产业场景生成，包含高精度高科技生产设备的传感记录数据。简言之，半导体生产包含数百道工艺步骤，对所谓晶圆（wafer）——即以半导体材料制成的薄片——开展物理与化学加工操作。通常，多片晶圆会被整合为批次（lot），单批次尺寸为25片，且批次内晶圆始终沿产线执行完全一致的加工流程。在产线中，每台工艺设备搭载多组传感器，用于采集气体流量、温度、电压等物理参数，由此得到每道工艺步骤执行期间记录的传感数据（sensor data）。为维持整体生产的稳定性，可依托传感数据在出现偏差时及时介入调整。生产完成后，会对晶圆上的每颗器件开展严格检测，由此生成晶圆检测数据（wafer test data）。部分场景下，晶圆检测数据中会出现异常模式，可能预示器件失效，此时则需在整条产线中追溯故障根源。基于此需求，本数据集提供了对应的传感数据，目标为挖掘晶圆检测数据与传感数据间的关联关系，从而实现故障根源的精准定位。 本次提供的数据分为三个数据集："equipment1.csv"、"equipment2.csv"与"response.csv"。其中"equipment1.csv"与"equipment2.csv"对应两台工艺设备的传感数据，"response.csv"则包含配套的晶圆检测数据。为实现唯一标识，每个数据集的前两列分别为批次号与晶圆号。需说明的是，单台设备的晶圆数量可存在差异，不同设备间的晶圆数量亦可能各不相同。 各数据集的精确列结构如下： 针对"equipment1.csv"与"equipment2.csv"： - 批次（lot）：批次号 - 晶圆（wafer）：晶圆编号 - 时间戳（timestamp）：对应传感记录的时间戳（单晶圆对应176条时间戳，传感器约每秒采集一次数据） - sensor_1：第一路传感器的采集数据 - sensor_2：第二路传感器的采集数据 - …… - sensor_56：第56路传感器的采集数据 其中"sensor_1"至"sensor_24"属于设备1（equipment1），"sensor_25"至"sensor_56"属于设备2（equipment2）。 针对"response.csv"： - 批次（lot）：批次号 - 晶圆（wafer）：晶圆编号 - 响应值（response）：数值化检测结果 - 分类（class）：基于响应值判定的“合格/不合格”分类（判定阈值为0.75）