Abstract: A semiconductor device manufacturing system and method for improving processing yield including a semiconductor device manufacturing apparatus including a wafer stage having an upper surface configured to allow the wafer to be placed, a plurality of heaters disposed inside the wafer stage and below a plurality of regions of the upper surface, and a controller configured to adjust outputs of a plurality of heater power supplies supplied to the plurality of heaters; and a wafer temperature calculation system configured to determine whether first output values of the plurality of the heater power supplies calculated in advance to implement a target temperature of the wafer during the processing are within an allowable range, and calculate second output values obtained by correcting all the first output values to values within the allowable range when the first output values are out of the allowable range.

Abstract: An object of the present disclosure is to provide a technique for detecting an anomaly of the surface state of the film of an electrostatic chuck. In a diagnostic device for diagnosing the state of a semiconductor manufacturing device having a sample stage on which a sample electrostatically adsorbed to a film is mounted, temperature data before and after a change of energy applied to the sample is obtained, and an anomaly of the film is detected on the basis of the obtained temperature data.

Abstract: A dimension measurement apparatus that automatically corrects a deviation of a contour without operator determination. The dimension measurement apparatus includes: a model learning unit that obtains a learning cross-sectional image and learning labels attached to different regions of the learning cross-sectional image and generates a deep learning model for image region division using the learning cross-sectional image and the learning labels; a model estimation unit that applies the model to a newly input target image and labels each independent region; a contour correction unit that detects a contour of each region using the target image and the labels, sets a representative point on the contour of the region, moves each representative point according to a movement rule, and repeats movement of the representative point until contour correction is that measures a dimension of a device complete; and a dimension measurement unit cross-sectional structure using the corrected contour.

Abstract: A preprocessor into which time-series sensor data is input includes a correspondence table associating sensor data with a physical unit. A physical extraction unit that obtains sensor data, and specifies a physical unit based on the correspondence table. A latent variable generation unit specifies a nonlinear function from the physical unit, and generates a latent variable for applying the sensor data to the specified nonlinear function. A latent variable addition unit obtains time-series sensor data and sets the data as a variable, applies the obtained time-series sensor data to the latent variable to generate time-series data and sets the data as a variable, and adds the generated time-series data to the obtained time-series sensor data to generate a data set of a plurality of variables. Then a relational expression estimation-processing unit that analyzes a relationship between the variables of the data set, and generates a linear relational expression.

Abstract: A preprocessor includes a sensor data storage part that is connected to an abnormality sign diagnosing device and stores multi-dimensional sensor data, a data analysis processing part that calculates a variable value by using the multi-dimensional sensor data stored in the sensor data storage part, an analysis data storage part that stores the variable value calculated by the data analysis processing part, and an analysis data addition processing part that combines the multi-dimensional sensor data stored at the sensor data storage part and the variable value stored in the analysis data storage part and outputs a combined result to the abnormality sign diagnosing device.

Abstract: A preprocessor into which time-series sensor data is input includes a correspondence table associating sensor data with a physical unit. A physical extraction unit that obtains sensor data, and specifies a physical unit based on the correspondence table. A latent variable generation unit specifies a nonlinear function from the physical unit, and generates a latent variable for applying the sensor data to the specified nonlinear function. A latent variable addition unit obtains time-series sensor data and sets the data as a variable, applies the obtained time-series sensor data to the latent variable to generate time-series data and sets the data as a variable, and adds the generated time-series data to the obtained time-series sensor data to generate a data set of a plurality of variables. Then a relational expression estimation-processing unit that analyzes a relationship between the variables of the data set, and generates a linear relational expression.

Abstract: A pre-processor pre-processes sensor data, and includes: an interval setting unit that acquires chronological sensor data, calculates a first index and a second index indicating different influences for noise included in the sensor data, and sets an interval in which influence of the noise is suppressed in accordance with the first index and the second index; a division processing unit that sets a dividing point at which a trend of the sensor data changes in units of intervals in which the influence of the noise is suppressed, the intervals being set by the interval setting unit; and a dividing point extraction processing unit that generates a dividing point vector from a time of the dividing point set by the division processing unit and outputs the dividing point vector.

Abstract: A preprocessor includes a sensor data storage part that is connected to an abnormality sign diagnosing device and stores multi-dimensional sensor data, a data analysis processing part that calculates a variable value by using the multi-dimensional sensor data stored in the sensor data storage part, an analysis data storage part that stores the variable value calculated by the data analysis processing part, and an analysis data addition processing part that combines the multi-dimensional sensor data stored at the sensor data storage part and the variable value stored in the analysis data storage part and outputs a combined result to the abnormality sign diagnosing device.