Abstract: An endpoint detection system for enhanced spectral data collection is provided. An optical bundle is coupled to a light source configured to generate incident light. The optical bundle includes two or more sets of optical fibers that each include an emitting optical fiber and a receiving optical fiber. The receiving optical fibers are disposed within the optical bundle at a pairing angle relative to a respective emitting optical fiber. The optical bundle is also coupled to a collimator assembly that includes an achromatic lens. The achromatic lens receives a first light beam of incident light from a first emitting optical fiber and directs spectral components of the first light beam to a first and second portion of a surface of a substrate. The first portion of the substrate surface is substantially the same as the second portion. The achromatic lens collects reflected spectral components that are produced by the spectral components directed to the first and second portions of the substrate surface.

Abstract: Embodiments of the present disclosure disclose a semiconductor device and a method for manufacturing the same. The semiconductor device includes an active region and a passive region, the semiconductor device further includes a source, a gate and a drain located on one side of a substrate, the gate being located between the source and the drain, the gate includes a first end portion and an intermediate portion, the intermediate portion, the source and the drain all being located in the active region, and the first end portion being located in the passive region, the first end portion includes a first sub end portion and a second sub end portion. In a first direction, an extension width of the first sub end portion is greater than that of the intermediate portion, and an extension width of the second sub end portion is greater than that of the first sub end portion.

Abstract: Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model.

Abstract: Implementations disclosed describe a collimator assembly having a collimator housing that includes an interface configured to optically couple to a process chamber that has a target surface, and a port to receive an optical fiber to deliver, to an enclosure formed by the collimator housing, a first (second) plurality of spectral components of light belonging to a first (second) range of wavelengths, and an achromatic lens located, at least partially, within the enclosure formed by the collimator housing, the achromatic lens to direct the first (second) plurality of spectral components of light onto the target surface to illuminate a first (second) region on the target surface, wherein the second region is substantially the same as the first region.

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to train a machine-learning model based on the input data reflecting the feature. The manufacturing system is further configured to modify the machine-learning model in view of the virtual knob for the feature.

Abstract: A method includes determining that conditions of a processing chamber have changed since a trained machine learning model associated with the processing chamber was trained. The method further includes determining whether a change in the conditions of the processing chamber is a gradual change or a sudden change. Responsive to determining that the change in the conditions of the processing chamber is a gradual change, the method further includes performing a first training process to generate a new machine learning model. Responsive to determining that the change in the conditions of the processing chamber is a sudden change, the method further includes performing a second training process to generate the new machine learning model. The first training process is different from the second training process.

Abstract: A cool cluster comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device of the tool cluster. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Abstract: Implementations disclosed describe a collimator assembly having a collimator housing that includes an interface configured to optically couple to a process chamber that has a target surface, and a port to receive an optical fiber to deliver, to an enclosure formed by the collimator housing, a first (second) plurality of spectral components of light belonging to a first (second) range of wavelengths, and an achromatic lens located, at least partially, within the enclosure formed by the collimator housing, the achromatic lens to direct the first (second) plurality of spectral components of light onto the target surface to illuminate a first (second) region on the target surface, wherein the second region is substantially the same as the first region.

Abstract: A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Abstract: A method of taking an elevator by an unmanned device is provided including: through a remote communication connection established with a cloud platform, sending a start floor elevator call request to the cloud platform; receiving real-time running information of at least one elevator returned by the cloud platform; selecting a target elevator according to the real-time running information of the at least one elevator, and entering the target elevator; and establishing a communication connection with an elevator controlling communication device of the target elevator, and sending a target floor elevator call request to an elevator controlling device of the target elevator through the communication connection such that the elevator controlling device controls the target elevator to run to a target floor based on the target floor elevator call request, where the elevator controlling communication device of the target elevator is connected with the elevator controlling device of the target elevator.

Abstract: A machine learning model trained to provide metrology measurements for a substrate is provided. Training data generated for a prior substrate processed according to a prior process is provided to train the model. The training data includes a training input including a subset of historical spectral data extracted from a normalized set of historical spectral data collected for the prior substrate during the prior process. The subset of historical spectral data includes an indication of historical spectral features associated with a particular type of metrology measurement. The training data also includes a training output including a historical metrology measurement obtained for the prior substrate, the historical metrology measurement associated with the particular type of metrology measurement. Spectral data is collected for a current substrate processed according to a current process.

Abstract: An endpoint detection system for enhanced spectral data collection is provided. An optical bundle is coupled to a light source configured to generate incident light. The optical bundle includes two or more sets of optical fibers that each include an emitting optical fiber and a receiving optical fiber. The receiving optical fibers are disposed within the optical bundle at a pairing angle relative to a respective emitting optical fiber. The optical bundle is also coupled to a collimator assembly that includes an achromatic lens. The achromatic lens receives a first light beam of incident light from a first emitting optical fiber and directs spectral components of the first light beam to a first and second portion of a surface of a substrate. The first portion of the substrate surface is substantially the same as the second portion. The achromatic lens collects reflected spectral components that are produced by the spectral components directed to the first and second portions of the substrate surface.

Abstract: Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model.

Abstract: A method is disclosed for operating an endpoint detection system of a processing chamber having a ceiling formed therein, a substrate support located internal to the processing chamber, and a substrate resting on the substrate support. A transparent panel is located in the ceiling of the processing chamber, the panel oriented at a first acute angle relative to the substrate and the substrate support. The transparent panel receives an incident light beam from the endpoint detection system at a second acute angle relative to the panel. The transparent panel transmits the incident light beam to the substrate within the processing chamber at an angle perpendicular to the substrate and the substrate support.

Abstract: A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Abstract: Disclosed herein is an endpoint detection having an optical bundle configured to emit light through a ceiling of a processing chamber. The optical bundle has a plurality of fibers configured to transmit the light from a light source towards a substrate and is configured to receive light reflected from the substrate. The plurality of fibers include a first emitting fiber and a first receiving fiber. The first receiving fiber is radially disposed at a pairing angle from the first emitting fiber, and is configured to receive light emitted from the first emitting fiber. The plurality of fibers further include a second emitting fiber and a second receiving fiber. The second receiving fiber is radially disposed at the pairing angle from the second emitting fiber. The second receiving fiber is configured to receive light originating from the second emitting fiber. The pairing angle is between about 175 degrees and 185 degrees.

Abstract: Implementations disclosed describe a collimator assembly having a collimator housing that includes an interface configured to optically couple to a process chamber that has a target surface, and a port to receive an optical fiber to deliver, to an enclosure formed by the collimator housing, a first (second) plurality of spectral components of light belonging to a first (second) range of wavelengths, and an achromatic lens located, at least partially, within the enclosure formed by the collimator housing, the achromatic lens to direct the first (second) plurality of spectral components of light onto the target surface to illuminate a first (second) region on the target surface, wherein the second region is substantially the same as the first region.

Abstract: A method is disclosed for operating an endpoint detection system of a processing chamber having a ceiling formed therein, a substrate support located internal to the processing chamber, and a substrate resting on the substrate support. A transparent panel is located in the ceiling of the processing chamber, the panel oriented at a first acute angle relative to the substrate and the substrate support. The transparent panel receives an incident light beam from the endpoint detection system at a second acute angle relative to the panel. The transparent panel transmits the incident light beam to the substrate within the processing chamber at an angle perpendicular to the substrate and the substrate support.

Abstract: A tilted window for use in an endpoint detection system of a processing chamber, and a processing chamber having the same are described herein. In one example, the tilted window includes a mounting frame, and a panel mounted in the mounting frame. The mounting frame has a body having a top surface, a bottom surface, and an inner edge connecting the top surface to the bottom surface of the body of the mounting frame. The mounting frame further has a panel disposed in the mounting frame. The panel has a body having a top surface and a bottom surface. The top surface of the body of the panel is oriented at acute angle relative to the top surface of the body of the mounting frame.

Abstract: A terahertz (THz) anti-reflection device, for example, a broadband tunable THz anti-reflection device, includes a silicon substrate having a plurality of recesses, each of the plurality of recesses having a plurality of cavities of decreasing dimension. The cavities may be nested polygonal cavities, for example, having a square or rectangular cross section. The recesses having the cavities may be positioned at regular periods, for example, periods ranging from 10 ?m to 20 ?m. The devices may be fabricated by conventional lithographic methods. Also disclosed are methods for modifying terahertz radiation and methods for fabricating anti-reflection devices.