A state management system includes an acquisition unit configured to acquire cumulative data, an execution unit configured to execute, in each cycle, a processing of calculating a specific index indicating a state of the apparatus using a plurality of types of data among the acquired data, and a display control unit configured to display time-series data indicating a time-series change in the calculated specific index. The processing executed by the execution unit includes at least one of calculating a first index indicating a degree of normality of the apparatus, calculating a second index indicating a rate at which the apparatus was in the operable state, and calculating a third index indicating productivity of the apparatus. The display control unit displays the time-series data having the first index, the second index, and/or the third index as the specific index.

A user interface for designing, configuring and/or editing a control flow representing a control strategy associated with a semiconductor manufacturing process, the user interface including: a library of control elements having at least a control element representing a task of simulation and each control element being selectable by a user; a control flow editor configured to organize the control elements into a control flow representing the control strategy; and a communication interface for communicating the control flow to a calculation engine configured to evaluate the control flow.

A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

Methods and systems for input/output (IO) handling during an update process for a manufacturing system controller are provided. First notifications are transmitted by an IO driver of a system controller between components of a process chamber and a substrate process IO handler of the system controller. The substrate process IO handler executes substrate process control instructions corresponding to a process recipe. The first notifications correspond to operations associated with substrate process control instructions. A determination is made that substrate process control instructions are to be updated. A detection is made that the substrate process is terminated. Second notifications are transmitted between the components of the process chamber and a system IO handler. The system update IO handler executes system update control instructions including commands configured to cause the components of the process chamber to maintain an environment of the process chamber at a target condition while the substrate process control instructions are updated.

Provided is a manufacturing support system capable of manufacturing an optoelectronic device with a low cost and a high yield rate and also achieving improved characteristics. The system is constituted by an inspection apparatus and a server. The inspection apparatus outputs to the server results obtained by performing defect inspections in a plurality of steps different from each other that may relate to the occurrence of defect determining a defective item in a primary process of device manufacturing. In the server, defect inspection result acquisition units acquire inspection results in respective steps, and a database separately stores the inspection results of the respective steps. By comparing defect information included in the inspection results obtained in the plurality of steps and the reference information indicating an inspection result of the normal state, a data processing control unit of the server determines whether an identical defect is indicated.

A system for determining the cause of an abnormality in a semiconductor manufacturing process includes an abnormality mode determination module, a selection module, and a root cause analysis module. The abnormality mode determination module is used to determine the similarity between wafer bin maps containing the abnormal data. When the similarity among the wafer maps is higher than a reference value, the selection module executes the steps of: determining a bad lot based on the wafer maps where the similarity is higher than the reference value; determining a time span within which the bad lot is generated; selecting other bad lots occurring in the time span and satisfying a failure model; selecting a good lot based on a fixed lot interval. The root cause analysis module is used to execute the steps of calculating the correlation among data to obtain confidence indexes.

A semiconductor manufacturing apparatus including: a first device; one or more sensors; a first calculation circuit that calculates one or more feature quantities of the first device from the detected physical quantities; and a failure prediction circuit that compares the one or more feature quantities with a plurality of pieces of model data of a temporal change in one or more feature quantities until the first device fails, decides a piece of model data with the minimum difference from the calculated one or more feature quantities among the plurality of pieces of model data, calculates predicted failure time from a difference between a failure point in time and a point in time at which a difference from the calculated one or more feature quantities is the minimum in the piece of model data.

Provided is a plasma processing apparatus including a processing unit in which a sample is plasma processed and which includes a monitor (optical emission spectroscopy) that monitors light emission of plasma, wherein the processing unit includes a prediction model storage unit that stores a prediction model predicting a plasma processing result, and a control device in which the plasma processing result is predicted by using a prediction model selected based on light emission data and device data as an indicator of state change of the processing unit.

An analysis device includes: a calculation part configured to calculate a degree of deviation of a processing space, in which a plasma process is performed, from a reference condition by inputting, among time-series data groups measured in the processing space, a time-series data group measured in a determination section, which is a predetermined period of time before a control section, into a time-series analysis model; and a specifying part configured to specify a characteristic value for determining control data at a time of the plasma process of a substrate in the control section based on the calculated degree of deviation.

An information processing apparatus for controlling display on a user interface includes an acquisition unit, and a display control unit. The acquisition unit acquires information including an operated state indicating a state regarding an operation performed on each of a plurality of apparatuses, and an operating state indicating a state regarding an operating status of each of the plurality of apparatuses. The display control unit controls display of the information regarding an apparatus identified from among the plurality of apparatuses on the user interface, based on the information including the operated state and the operating state acquired by the acquisition unit.