A method includes determining, by a processing device, a first eco-efficiency characterization associated with a first design of manufacturing equipment based on one or more of water eco-efficiency characterization, emissions eco-efficiency characterization, or electrical energy eco-efficiency characterization. The water eco-efficiency characterization, the emissions eco-efficiency characterization, the electrical energy eco-efficiency characterization, and the first eco-efficiency characterization are associated with an amount of environmental impact generated by the manufacturing equipment per unit product produced by the manufacturing equipment. The method further includes comparing the first eco-efficiency characterization to a second eco-efficiency characterization that is associated with a second design of the manufacturing equipment. The method further includes implementing the second design of the manufacturing equipment responsive to determining, based on the comparing, that the second eco-efficiency characterization is associated with a lower amount of environmental impact per unit product than the first eco-efficiency characterization.

A control system includes a drive-side control device and a host-side control device. The drive-side control device has a drive-side control structure including a drive-side feedback system and a drive-side control model part and configured so as to be capable of model tracking control in accordance with a control model which the drive-side control model part has. The host-side control device has a host-side control structure including a host-side control model part a host-side correcting signal based on a deviation between an output of the host-side control model part and the operation command signal is fed back to an input side of the host-side control model part, and a corrected command signal generated based on the host-side correcting signal that is fed back and the operation command signal is input to the host-side control model part. The corrected command signal is further input to the drive-side control structure.

Embodiments disclosed herein generally relate to methods, systems, and non-transitory computer readable medium for scheduling a substrate processing sequence in an integrated substrate processing system. A client device assigns a processing sequence to each substrate in a batch of substrates to be processed. The client device assigns a processing chamber to each process in the process sequence for each processing chamber in the integrate substrate processing system. The client device generates a processing model for the batch of substrates. The processing model defines a start time for each substrate in each processing chamber. The client device generates a timetable for the batch of semiconductor substrates based off the processing model. The client device processes the batch of substrates in accordance with the timetable.

Algorithmic reasoning about a cutting tool assembly's space of feasible configurations can be effectively harnessed to construct a sequence of motions that guarantees a collision-free path for the tool assembly to remove each support structure in the sequence. A greedy algorithm models the motion of the cutting tool assembly through the free-spaces around the intermediate shapes of the part as the free-spaces iteratively reduce in size to the near-net shape to determine feasible points of contact for the cutting tool assembly. Each support beam is evaluated for a contact feature along the boundary of the near-net shape that constitutes a feasible point of contact. If a support beam has at least one feasible configuration at each point, the support beam is deemed accessible and a collection of tool assembly configurations that are guaranteed to be non-colliding but which can access all points of contact of each accessible support beam can be generated.

There is provided a method for optimizing a manufacturing process of a new part. The method includes executing, by a system configured to drive the manufacturing process, a set of manufacturing functions. Executing these functions include receiving data associated with one or more field parts similar to the new part, and generating, based on the data, a forecast representative of a longevity of the one or more parts. The method further includes generating a digital thread forming a surrogate model for the new part, based on the forecast. Further, the method includes creating the set of manufacturing functions based on the surrogate model and manufacturing the new part according to the set of manufacturing functions.

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., to account for chamber-to-chamber variability) using machine learning techniques.

The present invention is suitable for easily properly setting control parameters in short time. The simulation device of the present invention comprises: a frequency response function computing part (53) computing a frequency response function according to a first command value and a measured value of a mechanical system; an impulse response computing part (41) computing an impulse response by performing inverse Fourier transform on the frequency response function obtained according to the frequency response function and the control parameters; and a time response outputting part (44) executing time response simulation of the mechanical system (7) according to a second command value and the impulse response.

The present invention easily displays a frequency response and a time response to a user. The simulation device of the present invention comprises: a frequency response function computing part (53) computing a frequency response function according to a measured value of a response of a mechanical system (7), a time response outputting part (44) executing time response simulation, a frequency response outputting part (45) outputting a frequency response characteristic and a display control part (26) displaying the time response simulation and frequency response characteristic simultaneously or selectively.

A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

Techniques are described for viewing a plurality of related presentation areas linked to a set of machine-related data. In one example, a machine-related data set associated with a manufacturing process session for manufacturing a particular workpiece is presented, the data set representing a common data set presented in a plurality of presentation areas, each associated with a separate view on the machine-related data set. In one of the presentation areas, a selection of a particular group of data points is identified and that presentation area is updated. Reference values associated with the common data set are identified based on the selected group of data points. For each of the other presentation areas, (1) a particular set of data included in the particular other presentation area corresponding to the identified reference values is identified and (2) the corresponding presentation area is updated based on that identified data set.