Abstract: A flexible element is configured to form a seal between a seal plate and another component in a substrate processing chamber. The flexible element includes a first portion engaged with a first interface surface of the seal plate, and a second portion facing a second interface surface of the component in the processing chamber. An intermediate portion spans from the first portion to the second portion. The intermediate portion is configured to flex to accommodate a misalignment between the first interface surface and the second interface surface by allowing relative movement between the first and second portions.

Abstract: A method includes receiving, by a processing device, first trace data associated with a first processing chamber, wherein the first processing chamber satisfies one or more performance metrics. The method further includes generating target trace data based on the first trace data associated with the first processing chamber. The method further includes receiving second trace data associated with a second processing chamber, wherein the second processing chamber does not satisfy the one or more performance metrics. The method further includes generating, based on the target trace data and the second trace data, a first recommended corrective action associated with the second processing chamber, wherein the first recommended corrective action includes updating one or more equipment constants of the second processing chamber. The method further includes performing the first recommended corrective action.

Abstract: Embodiments disclosed herein include a method for use with a semiconductor processing tool. In an embodiment, the method comprises configuring the semiconductor processing tool, running a benchmark test on the semiconductor processing tool, providing hardware operating window (HOW) analytics, generating a design of experiment (DoE) using the HOW analytics, implementing process optimization, and releasing an iteration of the process recipe. In an embodiment, the method further comprises margin testing the iteration of the process recipe.

Abstract: A method includes providing, as input to a first trained machine learning model, trace data associated with one or more substrate processing procedures. The input further includes equipment constants associated with the one or more substrate processing procedures. The input further includes trace data of a first processing chamber. The input further includes equipment constants of the first processing chamber. The method further includes obtaining, as output from the first trained machine learning model, a recommended update to a first equipment constant of the first processing chamber. The method further includes updated the first equipment constant of the first processing chamber responsive to obtaining the output from the first trained machine learning model.

Abstract: The present disclosure describes a semiconductor wafer processing system that uses a splitter circuit to delivery RF power from a power supply to multiple process chambers. The wafer processing system includes a first process chamber, a second process chamber, a power supply, a match circuit, and a splitter circuit. The power supply produces an electric current. The match circuit receives the electric current from the power supply and presents an impedance to the power supply. The splitting circuit includes a first leg and a second leg. The first leg includes a first capacitor that directs a first portion of the electric current from the match circuit to the first process chamber. The second leg includes a second capacitor that directs a second portion of the electric current from the match circuit to the second process chamber.

Abstract: A method includes receiving, by a processing device, data indicative of performance of a plurality of process chambers. The method further includes providing the data indicative of performance of the plurality of process chambers to a model. The method further includes receiving as output from the model a first recommended equipment constant update associated with a first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with a second process chamber of the plurality of process chambers. The method further includes updating a first equipment constant of the first process chamber and a second equipment constant of the second process chamber in view of the first recommended equipment constant update and the second recommended equipment constant update.

Abstract: Embodiments disclosed herein include a diagnostic substrate, comprising a baseplate, and a first plurality of image sensors on the baseplate, where the first plurality of image sensors are oriented horizontal to the baseplate. In an embodiment, the diagnostic substrate further comprises a second plurality of image sensors on the baseplate, where the second plurality of image sensors are oriented at a non-orthogonal angle to the baseplate. In an embodiment, the diagnostic substrate further comprises a printed circuit board (PCB) on the baseplate, and a controller on the baseplate, where the controller is communicatively coupled to the first plurality of image sensors and the second plurality of image sensors by the PCB. In an embodiment, the diagnostic substrate further comprises a diffuser lid over the baseplate, the PCB, and the controller.

Abstract: A cluster tool for fabricating substrates includes a factory interface; a first processing mainframe coupled to the factory interface, including: a processing chamber monolithic structure including four processing chambers in the same housing; four load locks coupled to the processing chamber monolithic structure, each load lock including a heater assembly configured to increase the temperature of a substrate disposed in the load lock; and a swapper assembly disposed between the four load locks and the processing chamber monolithic structure, wherein the swapper assembly includes four swappers, each swapper configured to swap substrates between one processing chamber and one load lock along a linear trajectory.

Abstract: A cluster tool includes: a factory interface including a first robot with an end effector; a first processing mainframe coupled to the factory interface, including: a first processing chamber; a first load lock including a first opening facing the factory interface configured to receive the end effector of the first robot, the first load lock further including a first slit valve configured to selectively open and close the first opening; at least one first LCF sensor disposed between the factory interface and the first slit valve; and a swapper assembly disposed between the first load lock and the first processing chamber, wherein the swapper assembly includes a first swapper configured to swap substrates between the first processing chamber and the first load lock along a first trajectory.

Abstract: A swapper assembly of a cluster tool includes a housing, at least two swappers, and a motor assembly. The at least two swappers are at least partially disposed within and rotatable relative to the housing. Each swapper includes a body, a first arm, and a second arm. The first arm and second arm are rotatable relative to the body. The motor assembly includes at least one motor, and the motor assembly is configured to operate the at least two swappers simultaneously to change the position of the first arm and second arm.

Abstract: A method includes receiving, from one or more sensors, sensor data associated with manufacturing equipment and updating one or more values of a digital replica associated with the manufacturing equipment based on the sensor data. The digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates. One or more outputs indicative of predictive data is obtained from the digital replica and, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment is caused.

Abstract: Embodiments disclosed herein include a diagnostic substrate, comprising a baseplate, and a first plurality of image sensors on the baseplate, where the first plurality of image sensors are oriented horizontal to the baseplate. In an embodiment, the diagnostic substrate further comprises a second plurality of image sensors on the baseplate, where the second plurality of image sensors are oriented at a non-orthogonal angle to the baseplate. In an embodiment, the diagnostic substrate further comprises a printed circuit board (PCB) on the baseplate, and a controller on the baseplate, where the controller is communicatively coupled to the first plurality of image sensors and the second plurality of image sensors by the PCB. In an embodiment, the diagnostic substrate further comprises a diffuser lid over the baseplate, the PCB, and the controller.

Abstract: Embodiments disclosed herein include a diagnostic substrate, comprising a baseplate, and a first plurality of image sensors on the baseplate, where the first plurality of image sensors are oriented horizontal to the baseplate. In an embodiment, the diagnostic substrate further comprises a second plurality of image sensors on the baseplate, where the second plurality of image sensors are oriented at a non-orthogonal angle to the baseplate. In an embodiment, the diagnostic substrate further comprises a printed circuit board (PCB) on the baseplate, and a controller on the baseplate, where the controller is communicatively coupled to the first plurality of image sensors and the second plurality of image sensors by the PCB. In an embodiment, the diagnostic substrate further comprises a diffuser lid over the baseplate, the PCB, and the controller.

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 includes providing, as input to a first trained machine learning model, trace data associated with one or more substrate processing procedures. The input further includes equipment constants associated with the one or more substrate processing procedures. The input further includes trace data of a first processing chamber. The input further includes equipment constants of the first processing chamber. The method further includes obtaining, as output from the first trained machine learning model, a recommended update to a first equipment constant of the first processing chamber. The method further includes updated the first equipment constant of the first processing chamber responsive to obtaining the output from the first trained machine learning model.

Abstract: A method includes receiving, by a processing device, first trace data associated with a first processing chamber, wherein the first processing chamber satisfies one or more performance metrics. The method further includes generating target trace data based on the first trace data associated with the first processing chamber. The method further includes receiving second trace data associated with a second processing chamber, wherein the second processing chamber does not satisfy the one or more performance metrics. The method further includes generating, based on the target trace data and the second trace data, a first recommended corrective action associated with the second processing chamber, wherein the first recommended corrective action includes updating one or more equipment constants of the second processing chamber. The method further includes performing the first recommended corrective action.

Abstract: A method includes receiving, by a processing device, data indicative of performance of a plurality of process chambers. The method further includes providing the data indicative of performance of the plurality of process chambers to a model. The method further includes receiving as output from the model a first recommended equipment constant update associated with a first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with a second process chamber of the plurality of process chambers. The method further includes updating a first equipment constant of the first process chamber and a second equipment constant of the second process chamber in view of the first recommended equipment constant update and the second recommended equipment constant update.

Abstract: A method includes receiving, from one or more sensors, sensor data associated with manufacturing equipment and updating one or more values of a digital replica associated with the manufacturing equipment based on the sensor data. The digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates. One or more outputs indicative of predictive data is obtained from the digital replica and, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment is caused.

Abstract: First sensor data indicative of a first mass flow rate of a first gas flowing to a vaporization chamber is received. The vaporization chamber includes a compound and is to transition the compound into a gaseous state. Second sensor data indicative of a second mass flow rate of a second gas including the compound and the first gas flowing out of the vaporization chamber is received. Third sensor data indicative of a third mass flow rate of the compound into the vaporization chamber is received. The first sensor data, the second sensor data, and the third sensor data is processed using a trained machine learning model to determine an estimated concentration of the compound within the second gas. At least one of a) modifying a flow rate of the first gas or b) providing the predicted concentration for display by a graphical user interface (GUI) is performed.

Abstract: A chemical mechanical polishing apparatus, including a platen supporting a polishing pad; a carrier head to hold a surface of a substrate against the polishing pad; a motor to generate relative motion between the platen and the carrier head so as to polish an overlying layer on the substrate; an array of acoustic sensors arranged within the carrier head to receive acoustic signals from the surface of the substrate; and a controller configured to detect a position of an acoustic event on the surface of the substrate based on received acoustic signals from the array of acoustic sensors.