G05B2219/40066

SHORTENED LOAD PORT FOR FACTORY INTERFACE

The disclosure describes devices, systems, and methods for integrating load locks into a factory interface footprint space. A factory interface for an electronic device manufacturing system can include a load port for receiving a substrate carrier. The load port can include a frame adapted for connecting the load port to a factory interface, the frame comprising a transport opening through which one or more substrates are capable of being transported between the substrate carrier and the factory interface. The load port can also include an actuator coupled to the frame, and a load port door coupled to the actuator and configured to seal the transport opening. The frame height can be greater than the height of the load port door, and less than 2.5 times the height of the load port door.

In-situ metrology and process control

Methods and apparatus for the in-situ measurement of metrology parameters are disclosed herein. Some embodiments of the disclosure further provide for the real-time adjustment of process parameters based on the measure metrology parameters. Some embodiments of the disclosure provide for a multi-stage processing chamber top plate with one or more sensors between process stations.

Substrate production control system and substrate production control method
11131984 · 2021-09-28 · ·

A substrate production control system includes production start timing obtaining section configured to obtain information of production start timing on substrate production lines, setup time estimation section configured to estimate setup times required for a setup work of setting up the substrate production line, setup start timing determination section configured to determine setup start timing of starting the setup work based on the production start timing and the setup time, delivery time estimation section configured to estimate delivery time required for a delivery work of receiving a member necessary for production of the substrate from member warehouse and conveying the member to at least one of an execution location (external setup area) of the setup work and the substrate production line, and delivery start timing determination section configured to determine delivery start timing of starting the delivery work based on the setup start timing and the delivery time.

Substrate processing system, substrate processing method, and control program

A substrate processing system includes a processing unit including processing modules and a first transfer device, a loading/unloading unit including a load port holding a substrate accommodating container and a second transfer device, and a control unit. The control unit controls the substrates to be sequentially transferred. When an error has occurred in a certain processing module, the control unit executes: collecting a substrate that has been unloaded from the substrate accommodating container but has not been processed in the substrate accommodating container; continuing processing of a preceding substrate in a processing module sequentially following the processing module in which the error has occurred; retreating an error substrate processed in the process module in which the error has occurred from the processing module to a retreat position; and continuing processing of a subsequent substrate processed in a processing module sequentially preceding the processing module in which the error has occurred.

SENSOR-BASED POSITION AND ORIENTATION FEEDBACK OF ROBOT END EFFECTOR WITH RESPECT TO DESTINATION CHAMBER

A system includes a light emitter attached to a destination chamber, the light emitter to emit a collimated light beam across an entrance to the destination chamber. The system includes an end effector attached to a distal end of an arm of a robot. The system includes a two-dimensional (2D) area sensor disposed on the end effector at a location that coincides with the collimated light beam while the end effector reaches within the destination chamber. The 2D area sensor is to detect a location of the collimated light beam incident on a surface of the 2D area sensor and transmit, to a controller coupled to the robot, sensing data including the location.

In-Situ Metrology And Process Control
20200335369 · 2020-10-22 ·

Methods and apparatus for the in-situ measurement of metrology parameters are disclosed herein. Some embodiments of the disclosure further provide for the real-time adjustment of process parameters based on the measure metrology parameters. Some embodiments of the disclosure provide for a multi-stage processing chamber top plate with one or more sensors between process stations.

Surface machining device

The invention relates to a surface machining device comprising a substrate support for receiving a substrate to be machined, a machining unit which can be moved relative to the substrate support along a first and a second movement axis, a position detecting unit for ascertaining the orientation of the substrate, and a control unit for controlling the movement of the machining unit dependent on the orientation of the substrate on the substrate support. The aim of the invention is to provide a surface machining device which can be produced in a compact manner and which allows a precise machining of the surface of the substrate to be machined in an inexpensive manner regardless of the position of the substrate on the substrate support. This is achieved in that the machining unit can be pivoted relative to the substrate support, in particular about a height axis which extends perpendicularly to the plane formed by the first and second movement axis.

SUBSTRATE PROCESSING SYSTEM, SUBSTRATE PROCESSING METHOD, AND CONTROL PROGRAM

A substrate processing system includes a processing unit including processing modules and a first transfer device, a loading/unloading unit including a load port holding a substrate accommodating container and a second transfer device, and a control unit. The control unit controls the substrates to be sequentially transferred. When an error has occurred in a certain processing module, the control unit executes: collecting a substrate that has been unloaded from the substrate accommodating container but has not been processed in the substrate accommodating container; continuing processing of a preceding substrate in a processing module sequentially following the processing module in which the error has occurred; retreating an error substrate processed in the process module in which the error has occurred from the processing module to a retreat position; and continuing processing of a subsequent substrate processed in a processing module sequentially preceding the processing module in which the error has occurred.

Training Spectrum Generation for Machine Learning System for Spectrographic Monitoring

A method of generating training spectra for training of a neural network includes measuring a first plurality of training spectra from one or more sample substrates, measuring a characterizing value for each training spectra of the plurality of training spectra to generate a plurality of characterizing values with each training spectrum having an associated characterizing value, measuring a plurality of dummy spectra during processing of one or more dummy substrates, and generating a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra. Each training spectrum of the second plurality of training spectra having an associated characterizing value.

Training Spectrum Generation for Machine Learning System for Spectrographic Monitoring

A method of generating training spectra for training of a neural network includes generating a plurality of theoretically generated initial spectra from an optical model, sending the plurality of theoretically generated initial spectra to a feedforward neural network to generate a plurality of modified theoretically generated spectra, sending an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network, determining that the discriminatory convolutional neural network does not discriminate between the modified theoretically generated spectra and empirically collected spectra, and thereafter, generating a plurality of training spectra from the feedforward neural network.