Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, a machine-learning engine obtains an empirical process model for a given process for a given processing chamber. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: 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., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: Embodiments of the disclosure provide methods and system for inspecting and treating a substrate. In one embodiment, a method is provided including transmitting a first plurality of beams from a diffractive beam splitter to a first surface of a substrate to generate a reflection of a second plurality of beams, wherein the first plurality of beams are spaced apart from each other upon arriving at the first surface of the substrate; receiving the second plurality of beams on a recording surface of an optical device, wherein the second plurality of beams are spaced apart from each other upon arriving at the recording surface; measuring positional information of the second plurality of beams on the recording surface; comparing the positional information of the second plurality of beams to positional information stored in a memory; and storing a result of the comparison in the memory.

Abstract: A system for processing a substrate is provided. The system includes a process chamber including one or more sidewalls enclosing a processing region; and a substrate support. The system further includes a passageway connected to the process chamber; and a first particle detector disposed at a first location along the passageway. The first particle detector includes an energy source configured to emit a first beam; one or more optical devices configured to direct the first beam along one or more paths, where the one or more paths extend through at least a portion of the passageway. The first particle detector further includes a first energy detector disposed at a location other than on the one or more paths. The system further includes a controller configured to communicate with the first particle detector, wherein the controller is configured to identify a fault based on signals received from the first particle detector.

Abstract: A plurality of energy filter values are obtained using a model that simulates potential distribution within a 3D feature when an electron beam of an SEM impinges on a selected area that includes the 3D feature. A correspondence is extracted between the plurality of energy filter values and respective depths of the 3D feature along a longitudinal direction by analyzing the simulated potential distribution. A plurality of SEM images of the 3D feature corresponding to the plurality of energy filter values are obtained. The plurality of SEM images are associated with their respective depths based on the extracted correspondence between the plurality of energy filter values and the respective depths. A composite 3D profile of the 3D feature is generated from the plurality of SEM images obtained from various depths of the 3D feature.

Abstract: A system for processing a substrate is provided. The system includes a process chamber including one or more sidewalls enclosing a processing region; and a substrate support. The system further includes a passageway connected to the process chamber; and a first particle detector disposed at a first location along the passageway. The first particle detector includes an energy source configured to emit a first beam; one or more optical devices configured to direct the first beam along one or more paths, where the one or more paths extend through at least a portion of the passageway. The first particle detector further includes a first energy detector disposed at a location other than on the one or more paths. The system further includes a controller configured to communicate with the first particle detector, wherein the controller is configured to identify a fault based on signals received from the first particle detector.

Abstract: Process kit components for use with a substrate support of a process chamber are provided herein. In some embodiments, a process kit ring may include a ring shaped body having an outer edge, an inner edge, a top surface and a bottom, wherein the outer edge has a diameter of about 12.473 inches to about 12.479 inches and the inner edge has a diameter of about 11.726 inches to about 11.728 inches, and wherein the ring shaped body has a height of about 0.116 to about 0.118 inches; and a plurality of protrusions disposed on the top surface of the ring shaped body, each of the plurality of protrusions disposed symmetrically about the ring shaped body.

Abstract: 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.

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, a machine-learning engine obtains an empirical process model for a given process for a given processing chamber. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: 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.

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, the method obtains virtual metrology (VM) data from sensors of the chamber and on-board metrology (OBM) data from devices on the wafers; obtains in-line metrology data from precision scanning electron microscope (SEM); and also obtains an empirical process model for a given process. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, the method obtains virtual metrology (VM) data from sensors of the chamber and on-board metrology (OBM) data from devices on the wafers; obtains in-line metrology data from precision scanning electron microscope (SEM); and also obtains an empirical process model for a given process. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: 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.

Abstract: 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., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: Methods and apparatus for plasma processing are provided herein. In some embodiments, a plasma processing apparatus includes a process chamber having an interior processing volume; a first RF coil disposed proximate the process chamber to couple RF energy into the processing volume; and a second RF coil disposed proximate the process chamber to couple RF energy into the processing volume, the second RF coil disposed coaxially with respect to the first RF coil, wherein the first and second RF coils are configured such that RF current flowing through the first RF coil is out of phase with RF current flowing through the RF second coil.

Abstract: Embodiments of inductively coupled plasma (ICP) reactors are provided herein. In some embodiments, a dielectric window for an inductively coupled plasma reactor includes: a body including a first side, a second side opposite the first side, an edge, and a center, wherein the dielectric window has a dielectric coefficient that varies spatially. In some embodiments, an apparatus for processing a substrate includes: a process chamber having a processing volume disposed beneath a lid of the process chamber; and one or more inductive coils disposed above the lid to inductively couple RF energy into and to form a plasma in the processing volume above a substrate support disposed within the processing volume; wherein the lid is a dielectric window comprising a first side and an opposing second side that faces the processing volume, and wherein the lid has a dielectric coefficient that spatially varies to provide a varied power coupling of RF energy from the one or more inductive coils to the processing volume.

Abstract: A gas injection system includes (a) a side gas plenum, (b) a plurality of N gas inlets coupled to said side gas plenum, (c) plural side gas outlets extending radially inwardly from said plenum, (d) an N-way gas flow ratio controller having N outputs coupled to said N gas inlets respectively, and (e) an M-way gas flow ratio controller having M outputs, respective ones of said M outputs coupled to said tunable gas nozzle and a gas input of said N-way gas flow ratio controller.

Abstract: Embodiments of the disclosure provide a metrology system. In one example, a metrology system includes a laser source adapted to transmit a light beam, a lens adapted to receive at least a portion of the light beam from the laser source, a first beam splitter positioned to receive at least the portion of the light beam passing through the lens, a first beam displacing device adapted to cause a portion of the light beam received from the beam splitter to be split into two or more sub-light beams a first recording device having a detection surface, and a first polarizer that is positioned between the first displacing device and the first recording device, wherein the first polarizer is configured to cause the two or more sub-light beams provided from the first displacing device to form an interference pattern on the detection surface of the first recording device.

Abstract: A method for flexible inspection of a sample includes forming an input beam using a beam source, blocking a portion of the input beam using an input mask, and forming a shaped beam from a portion of the input beam. The shaped beam is received at a first portion of an objective lens and focused onto a sample. A reflected beam is collected at a second portion of the objective lens. Scattered light is collected at the first and second portions of the objective lens and at a third portion of the objective lens. The scattered light is received at a dark-field detector module and a portion of the scattered light is directed to a dark-field detector. The dark-field detector module includes an output mask having one or more output apertures that allow at least part of the scattered light that passes through the third portion of the object lens to pass as the portion of the scattered light that is directed to the dark-field detector.

Abstract: A gas injection system includes (a) a side gas plenum, (b) a plurality of N gas inlets coupled to said side gas plenum, (c) plural side gas outlets extending radially inwardly from said plenum, (d) an N-way gas flow ratio controller having N outputs coupled to said N gas inlets respectively, and (e) an M-way gas flow ratio controller having M outputs, respective ones of said M outputs coupled to said tunable gas nozzle and a gas input of said N-way gas flow ratio controller.