According to one aspect of the technique, there is provided a method of manufacturing a semiconductor device, including checking a leak from a process furnace before a substrate is processed. The checking includes: (a) measuring, by a partial pressure sensor provided at an exhaust pipe, an oxygen partial pressure value of a residual oxygen after the process furnace is vacuum-exhausted; (b) comparing the oxygen partial pressure value measured by the partial pressure sensor with a threshold value; and (c) when the oxygen partial pressure value is higher than the threshold value in (b), performing at least one among: purging the process furnace and evacuating the process furnace.

A semiconductor analysis system includes a machining device that machines semiconductor wafer to prepare a thin film sample for observation, a transmission electron microscope device that acquires a transmission electron microscope image of the thin film sample, and a host control device that controls the machining device and the transmission electron microscope device. The host control device evaluates the thin film sample based on the transmission electron microscope image, updates machining conditions based on an evaluation result of the thin film sample, and outputs the updated machining conditions to the machining device.

The invention provides a correction and compensation method in a semiconductor manufacturing process. The method includes the following steps: providing a machine, the machine is at least used for exposure manufacturing of a first product and a second product, performing period maintenance (PM) on the machine, recording an original offset map before and after the period maintenance of the machine is performed, the original offset map has an original exposure size, and adjusting the original exposure size of the original offset map to correspond to a first exposure size of the first product, and performing a first offset compensation correction on the first product. And adjusting the original exposure size of the original offset map to correspond to a second exposure size of the second product, and performing a second offset compensation correction on the second product.

The present disclosure provides a measuring system. The measuring system includes an insulative tube, a capacitor and a static charge meter. The insulative tube is configured to allow a fluid to flow therethrough. The capacitor is disposed on a surface of a section of the insulative tube. The capacitor includes a first metallic layer, a second metallic layer opposite to the first metallic layer, and a dielectric layer sandwiched between the first metallic layer and the second metallic layer. The static charge meter is electrically coupled to the capacitor and configured to measure static charge accumulated inside the section of the insulative tube.

The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

An object transfer device includes: a motor; a motor drive circuit; an encoder that detects a motor output shaft rotation angle; a drive system that transmits motion of the output shaft to an object; an origin sensor that detects arrival of a predetermined site of the drive system interlocked with the object at a predetermined position; a drive circuit-including servo amplifier that records a current position of the object based on detection by the encoder and the origin sensor, and controls the object to a target position; an absolute position holder that acquires an absolute position of the predetermined site based on detection by the encoder at least while the drive circuit is off; and a power saving controller that is bidirectionally communicable with the servo amplifier and can resume servo control with the absolute position as a current position of the predetermined site when the drive circuit is on.

There is provided a configuration at least including a screen control part configured to display on a display part a maintenance component management screen displaying a component, a mechanism, or both, as a maintenance component, a collection part configured to collect component data related to the maintenance component, a determination part configured to compare a cumulative value of the component data with a predetermined threshold value to determine the cumulative value exceeding the threshold value, a calculation part configured to calculate a replacement time, and an operation part configured to calculate replacement times based on an average value of the component data and a cumulative value of the component data for each predetermined cycle, display the maintenance component sequentially from the maintenance component reaching the earliest replacement time, and display the maintenance component on the display part in a state where the component data is updated for the predetermined cycle.

A substrate processing apparatus includes a substrate holding section, a chemical liquid supply section, a substrate information acquiring section, a chemical liquid processing condition information acquiring section, and a controller. The substrate information acquiring section acquires substrate information including hardened layer thickness information indicating a thickness of a hardened layer in a resist layer of a processing target substrate or ion implantation condition information indicating a condition for ion implantation by which the hardened layer has been formed in the resist layer. The chemical liquid processing condition information acquiring section acquires based on the substrate information chemical liquid processing condition information indicating a chemical liquid processing condition for the processing target substrate from a trained model. The controller controls the substrate holding section and the chemical liquid supply section -to perform processing with a chemical liquid on the processing target substrate based on the chemical liquid processing condition information.

A process control method of a substrate processing apparatus includes monitoring a process status of each process chamber of a plurality of process chambers, determining a shift-to-idle process chamber to be shifted from a process state to an idle state among the plurality of process chambers, storing a first to-be-processed substrate in a storage, associated with the shift-to-idle process chamber, among a plurality of storages provided in a transfer chamber, and transferring the first to-be-processed substrate stored in the storage to the shift-to-idle process chamber in accordance with a shift to the idle state of the shift-to-idle process chamber.

A method for determining processing chamber conditions using sensor data and a machine learning model is provided. The method includes receiving, by a processing device, sensor data that include chamber data indicating a state of an environment of a processing chamber processing a substrate according to a set of process parameters of a current process. The sensor data further include spectral data indicating optical emission spectra (OES) measurements of a plasma disposed within the processing chamber. The method further includes using the sensor data as input to a machine learning model and obtaining one or more outputs that indicate one or more chamber condition metrics. The method further includes determining a recovery status of a processing chamber based on the one or more chamber condition metrics. The method further includes causing a modification to a performance of the processing chamber based on the recovery status of the processing chamber.