Integrated Board Measurement System

Abstract

The apparatus includes a substrate holder, a first actuator for rotating the substrate holder, a second actuator for linearly moving the substrate holder, a first sensor for generating one or more first measurements or images of the substrate, a second sensor for generating one or more second measurements of a target location on the substrate, and a processing device. The processing device estimates a position of the substrate on the substrate holder and causes the first actuator to rotate the substrate holder about a first axis. The rotation causes an offset between the field of view of the second sensor and the target location on the substrate due to the substrate not being centered on the substrate holder. The processing device causes the second actuator to linearly move the substrate holder along the second axis to correct for the offset. The processing device determines a profile across a surface of the substrate based on the one or more second measurements of the target location.

Description

[Technical field]

[0001] Some embodiments of the present disclosure generally relate to systems and methods for performing reflectometry, imaging, and / or other measurements, as well as integrated substrate measurement systems that can include a reflectometer and / or an imager. [Background technology]

[0002] The production of modern materials often involves various deposition techniques, such as chemical vapor deposition (CVD) or physical vapor deposition (PVD) techniques, in which one or more selected types of atoms are deposited on a substrate (wafer) held in a low or high vacuum environment provided by a vacuum deposition chamber. The materials thus produced can include single crystals, semiconductor films, fine coatings, and numerous other substances used in practical applications such as electronic device manufacturing. Many of these applications rely on the purity and uniformity of the materials grown in the substrate processing system, as well as the uniformity of the processes. The goal of uniformity leads to the monitoring and measurement of the uniformity of the substrate and the manufacturing process. Improving the precision, reliability, and efficiency of such techniques presents several technical challenges. Summary of the Invention

[0003] In an exemplary embodiment, the integrated reflectometer (IR) includes a substrate holder, a first actuator, a second actuator, a first sensor, a second sensor, and a processing device. The substrate holder can secure the substrate. The first actuator can rotate the substrate holder about a first axis, and the second actuator can linearly move the substrate holder along a second axis. The first sensor can generate one or more first measurements or images of a first plurality of target locations on the substrate. The second sensor can generate one or more second measurements of a second plurality of target locations on the substrate. The processing device can determine an estimate of a position of the substrate on the substrate holder based on the one or more first measurements or images. The processing device can further cause the first actuator to rotate the substrate holder about the first axis for measurement of one target location of the second plurality of target locations. The rotation can cause an offset between the field of view of the second sensor and the target location on the substrate due to the substrate not being centered on the substrate holder. The processing device further causes the second actuator to linearly move the substrate holder along a second axis to correct for the offset. The processing device can further determine a profile (e.g., a uniformity profile) across the surface of the substrate based on the one or more second measurements of the second plurality of target positions.

[0004] In an exemplary embodiment, the system can include a transfer chamber, a process chamber, a load lock connected to the transfer chamber, a factory interface (FI) connected to the load lock, and an optical measurement device connected to the FI. The FI can include a robot arm. The robot arm can move a substrate from the process chamber to the optical measurement device. The optical measurement device can include a substrate holder for securing the substrate, a first actuator for rotating the substrate holder about a first axis, a second actuator for linearly moving the substrate holder along a second axis, a first sensor for generating one or more first measurements or images of the substrate, a second sensor for generating one or more second measurements of a set of target locations on the substrate, and a processing device for estimating an estimate of an offset of a center of the substrate relative to a center of the substrate holder based on the one or more first measurements or images.

[0005] In an exemplary embodiment, the method includes receiving a substrate on a substrate holder of an optical measurement device. The method further includes determining an estimate of a position of the substrate on the substrate holder. The method further includes measuring a set of target locations on the substrate using a sensor. To measure the target location or set of target locations, the method further includes rotating the substrate holder about a first axis, which may cause an offset between the field of view of the sensor and the target locations on the substrate due to the substrate not being centered on the substrate holder. To measure the target locations, the method further includes correcting the offset by linearly moving the substrate holder along a second axis, and determining, by a processing device, a profile across a surface of the substrate based on the measurements of the set of target locations.

[0006] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like references indicate like elements. It should be noted that different references to "an" or "one" embodiment in this disclosure are not necessarily to the same embodiment, but such references mean at least one. [Brief description of the drawings]

[0007] [Figure 1] 1 is a top schematic view of an exemplary processing system according to one embodiment. [Figure 2A] FIG. 1 is a cross-sectional schematic diagram of an IR system with rotary and linear actuators according to one embodiment. [Figure 2B] FIG. 2B is a top-down schematic view of a substrate holder of the IR system of FIG. 2A according to one embodiment. [Diagram 3] FIG. 2 is a schematic diagram illustrating rotational and linear coordinates of a substrate holder of an IR system according to one embodiment. [Figure 4] FIG. 2 is a schematic diagram of a substrate placed on a substrate holder according to one embodiment. [Figure 5A-5D] 1 is a series of graphs illustrating an example transformation of a circular orbit according to one embodiment. [Figure 6] 1 is a flow diagram of a method for performing coordinate transformation to correct for substrate and substrate holder offset according to one embodiment. [Figure 7] FIG. 13 is a block diagram of a method for performing coordinate transformation to correct for substrate and substrate holder offset according to one embodiment. [Figure 8] FIG. 8 illustrates a model training and model application workflow 817 for process drift determination and film thickness determination according to one embodiment. [Figure 9] FIG. 1 is a block diagram of an example computing device capable of process drift and film thickness determination, operating in accordance with one or more aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] The embodiments described herein relate to systems and methods for measuring film thickness and other parameters, such as critical dimension (CD), CD bias (delta between process steps), etc., across all or a portion of a surface of a substrate after the substrate has been processed in a processing chamber, and other physical parameters related to substrate processing results. Process results, including film thickness, CD, CD bias, etc., can be monitored across one or more substrates for etch and deposition processes. Film thickness information, CD information, CD bias information, etc. can be used to monitor process drifts that may result in non-uniformity across the surface of the substrate. In embodiments, an integrated reflectance measuring device or other integrated measurement and / or imaging device is a component of a device manufacturing system. The integrated measuring device can be used to measure the surface of the substrate while the substrate is still in the device manufacturing system. This allows issues such as film non-uniformity to be detected before more substrates are processed by the process chamber that caused the film non-uniformity. For example, the integrated measuring device can be used to monitor process drifts and measure the uniformity of the substrate after processing. The integrated measurement device in embodiments has a small footprint due to its unique design, which allows the integrated reflectance measurement device to be integrated into or attached to a transfer chamber or factory interface without consuming significant factory floor space. The integrated measurement device in embodiments generates an accurate wafer map of the wafer even when the wafer is not centered on the substrate support of the reflectance measurement device.

[0009] The systems and methods described herein can provide information and / or alerts to a user quickly, efficiently, and in real time regarding film thickness issues, non-uniformity, process drift, CD, CD bias, and other physical parameters related to substrate processing results, thereby enabling the user to correct film thickness, CD, CD bias, and other issues, non-uniformity, process drift, and the like, as they occur. Additionally, aspects of the present disclosure solve the above problems and others by utilizing a reflectometry and / or other measurement system that includes a substrate support (e.g., a substrate holder) that both rotates and moves linearly so that a sensor (e.g., an optical sensor) can capture measurements of points all across the surface of the substrate. The measurement system can additionally include a camera or other sensor that can be utilized to identify the location of an edge of a substrate secured on the substrate support. The substrate support can be moved in a linear motion until the camera and / or sensor detects the edge. The substrate support can be moved rotationally to determine the center of the substrate, which may not be centered on the substrate support. Once the center of the substrate is determined relative to the center of the substrate support, a coordinate transformation can be applied while rotating the substrate to cause an appropriate linear translation of the substrate support to compensate for the center of the substrate being offset from the center of the substrate holder. Thus, measurements can be made for many points on the substrate (e.g., across the surface of the substrate) and each measurement can be associated with a precise location on the substrate.

[0010] A rotational motor or actuator can rotate the substrate holder about a rotational axis, and a linear actuator can move the substrate holder linearly along a linear axis while measurements are generated. In an embodiment, a single linear actuator is used with a rotor and the substrate holder is moved linearly only along a single axis (called the r-axis). A camera or first sensor can generate images of a first plurality of target locations on the substrate, and a second sensor can measure one or more of the second plurality of target locations on the substrate to find, for example, film thickness, CD, material properties, etc., at the one or more target locations. In one embodiment, the edge of the substrate can be identified by moving the substrate holder along a linear axis until the edge is identified (e.g., by the camera or sensor). Once the edge of the substrate is identified, the second sensor and / or the first sensor or camera can be used to determine the periphery of the substrate by rotating the substrate about the rotational axis and generating one or more images or measurements of the edge (e.g., periphery) of the substrate while rotating the substrate about the rotational axis. In one embodiment, the second sensor is an infrared sensor. In other embodiments, the second sensor can be another type of sensor, such as an optical sensor or a reflectometric sensor.

[0011] Based on the images and / or sensor measurements of the first sensor or camera, it can be determined that the substrate is not centered on the substrate holder (e.g., there is an offset between the center of the substrate and the center of the substrate holder) and / or an estimate of the offset position of the substrate on the substrate holder can be determined. A coordinate transformation can be determined between the center of the substrate and the center of the substrate holder (about which the substrate holder rotates) and applied to correct for the offset. In some embodiments, the coordinate transformation can be applied during the rotation of the substrate holder to correct for the offset.

[0012] In some embodiments, the second sensor may be the same as or a different sensor as described above, and may be a reflectometry sensor. The second sensor measures one or more target locations on the substrate to generate reflectometry measurements of the one or more target locations. In some embodiments, the substrate holder may be a vacuum substrate holder with a mass to provide the substrate holder with inertia to enable high speed rotation. Alternatively, the substrate holder may be an electrostatic substrate holder, or a substrate holder that applies a gripping force to the substrate.

[0013] In some embodiments, profiles such as a uniformity profile, particle count profile, optical constant profile, etc. across the surface of the substrate may be determined based on measurements of one or more target positions.

[0014] In some embodiments, at least the substrate holder, the rotary actuator, the linear actuator, the camera, and the sensor can be part of an integrated measurement and / or imaging system, such as an integrated reflectometer (IR) system, coupled to or integrated into a factory interface. The measurement and / or imaging system can have a small three-dimensional footprint, which is possible because the measurement and / or imaging system has the ability to measure the entire surface of the substrate using at most rotational and linear (R-θ) motions. The measurement and / or imaging system can have a much smaller footprint than other optional metrology systems. In addition, the measurement and / or imaging system can have a higher throughput than other optical metrology systems (e.g., than other reflectance measurement systems). In some embodiments, the measurement and / or imaging system can instead move the substrate holder in a plane by linearly moving the substrate holder in two directions (e.g., XY).

[0015] 1 is a top-view schematic diagram of an exemplary processing system 100 according to one embodiment. In some embodiments, the processing system 100 can be an electronics processing system configured to perform one or more processes on a substrate 102. In some embodiments, the processing system 100 can be an electronic device manufacturing system. The substrate 102 can be any suitably rigid, flat article of fixed dimensions suitable for fabricating electronic devices or circuit components thereon, such as, for example, a silicon-containing disk or wafer, a patterned wafer, a glass plate, or the like.

[0016] The processing system 100 includes a process tool 104 (e.g., a mainframe) and a factory interface 106 coupled to the process tool 104. The process tool 104 includes a housing 108 having a transfer chamber 110 therein. The transfer chamber 110 includes one or more processing chambers (also referred to as process chambers) 114, 116, 118 disposed about and coupled to it. The processing chambers 114, 116, 118 can be coupled to the transfer chamber 110 via respective ports, such as slit valves, or the like.

[0017] The processing chambers 114, 116, 118 may be adapted to perform any number of processes on the substrate 102. The same or different substrate processes may be performed in each processing chamber 114, 116, 118. Examples of substrate processes include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, and the like. In one example, a PVD process is performed in one or both of the process chambers 114, an etching process is performed in one or both of the process chambers 116, and an annealing process is performed in one or both of the process chambers 118. Other processes may be performed on the substrate therein. The processing chambers 114, 116, 118 may each include a substrate support assembly. The substrate support assembly may be configured to hold the substrate in place while the substrate process is being performed.

[0018] The transfer chamber 110 further includes a transfer chamber robot 112. The transfer chamber robot 112 can include one or multiple arms, each arm including one or more end effectors at the end of the arm. The end effectors can be configured to handle a particular object, such as a wafer. In some embodiments, the transfer chamber robot 112 is a Selective Compliance Assembly Robot Arm (SCARA) robot, such as a two-link SCARA robot, a three-link SCARA robot, a four-link SCARA robot, or the like.

[0019] The load lock 120 may also be coupled to the housing 108 and the transfer chamber 110. The load lock 120 may be configured to interface and couple to the transfer chamber 110 on one side and the factory interface 106 on another side. The load lock 120 may have an environmentally controlled atmosphere that is changed from a vacuum environment (where substrates are transferred to and from the transfer chamber 110) to or near an atmospheric pressure inert gas environment (where substrates are transferred to and from the factory interface 106). In some embodiments, the load lock 120 is a stacked load lock having a pair of upper internal chambers and a pair of lower internal chambers disposed at different vertical levels (e.g., one above the other). In some embodiments, the pair of upper internal chambers is configured to receive processed substrates from the transfer chamber 110 for removal from the process tool 104, while the pair of lower internal chambers is configured to receive substrates from the factory interface 106 for processing within the process tool 104. In some embodiments, the load lock 120 is configured to perform a substrate process (eg, etching or pre-cleaning) on ​​one or more substrates 102 received therein.

[0020] The factory interface 106 may be any suitable enclosure, such as, for example, a front-end equipment module (EFEM). The factory interface 106 may be configured to receive substrates 102 from substrate carriers 122 (e.g., Front Opening Unified Pods (FOUPs)) docked to various load ports 124 of the factory interface 106. A factory interface robot 126 (shown in dotted lines) may be configured to transfer substrates 102 between the substrate carriers 122 (also referred to as containers) and the load locks 120. In other and / or similar embodiments, the factory interface 106 is configured to receive replacement parts from a replacement parts storage container 123. The factory interface robot 126 may include one or more robot arms and may be or include a SCARA robot. In some embodiments, the factory interface robot 126 has more links and / or more degrees of freedom than the transfer chamber robot 112. The factory interface robot 126 may include an end effector at the end of each robot arm. The end effector may be configured to pick up and handle a particular object, such as a wafer. Alternatively or additionally, the end effector may be configured to handle an object, such as a process kit ring.

[0021] Any conventional robot type can be used for the factory interface robot 126. Transfers can occur in any order or direction. The factory interface 106 can be maintained in some embodiments, for example, in a non-reactive gas environment (e.g., using nitrogen as the non-reactive gas) at a slight positive pressure.

[0022] The processing system 100 can include an integrated measurement and / or imaging system 101, which can be, for example, a reflectometry (IR) system. Reflectometry is a measurement technique that uses measured changes in light reflected from an object to determine the geometric and / or material properties of the object. A reflectance spectrometer measures the intensity of reflected light over a range of wavelengths. For dielectric films, these intensity changes can be used to determine the thickness of the film. Additionally, reflectometry measurements can be used to detect CD, CD bias, and other physical parameters related to substrate processing results.

[0023] The measurement and / or imaging system 101 can be connected to the factory interface 106. Alternatively, the measurement and / or imaging system 101 can be connected to a transfer chamber (e.g., in place of one of the illustrated processing chambers). Alternatively, the measurement and / or imaging system 101 can be located inside the factory interface 106 or the transfer chamber 110. The measurement and / or imaging system 101 can also be a stand-alone system that is not connected to the processing system 100. The measurement and / or imaging system 101 can be mechanically isolated from the factory interface 106 and the external environment to protect the measurement and / or imaging system 101 from external vibrations. In some embodiments, the measurement and / or imaging system 101 and the components included therein can perform analytical measurements (e.g., thickness measurements) that can provide profiles across the surface of the substrate, such as a thickness uniformity profile, a particle count profile, a CD profile, a CD uniformity profile, an optical constant profile, a material property profile, and the like. The measurement and / or imaging system 101 can provide feedback to a user regarding the uniformity profile. The measurement and / or imaging system 101 can be an assembly capable of measuring film thickness, CD, CD bias, optical properties, particle counts, material properties, surface roughness, etc., across a substrate after it has been processed in the chamber. Such metrology can be used to monitor process drift, out-of-specification film thickness, out-of-specification CD, CD bias, etc., for etch, deposition, and / or other processes. The results of the measurements can be used to quickly correct or adjust process parameters of one or more process recipes being performed in one or more process chambers to account for the determined process drift. Additionally, the results of the measurements can be used to determine when maintenance should be performed on a process chamber, when further inspection should be performed on the substrate, when the substrate should be flagged as out-of-specification, etc.Although the measurement and / or imaging system 101 is shown as being connected to the factory interface 106, in other embodiments, as described above, it may be a stand-alone reflectometry system or may be located elsewhere within or attached to the processing system 100.

[0024] The factory interface robot 126 can place the substrate on a substrate transfer lift (e.g., lift pins) of the measurement and / or imaging system 101. In an embodiment, the substrate transfer lift can then lower the substrate onto a substrate support, such as a chuck (e.g., a vacuum chuck or an electrostatic chuck) of the measurement and / or imaging system 101. In other embodiments, the substrate can instead be lowered onto another type of substrate holder, such as a mechanical chuck, a magnetic chuck, or the like. The measurement and / or imaging system 101 can include various covers and ventilation systems to maintain the clean substrate and environment.

[0025] Within the measurement and / or imaging system 101, the substrate holder can be translated by a linear actuator such that the edge of the substrate is centered under an optical camera. The substrate and substrate holder can then be rotated with a rotational motor (actuator) and an optical camera or a first sensor (e.g., IR sensor, visible light sensor, etc.) can capture an image or measurement of the edge of the substrate. Motion of the substrate holder about the axis of rotation may be referred to as θ motion, motion of the substrate holder along a linear axis may be referred to as r motion, and a combination of motion of the substrate holder about the axis of rotation and motion of the substrate holder along a linear axis may be referred to herein as r-θ motion. The image or measurement can be analyzed to determine the center point of the substrate, as described in more detail below. Moving the substrate holder in both linear (e.g., radial) and rotational motions can minimize the overall size of the measurement and / or imaging system 101 while still allowing for the capture of measurements along the entire surface of the substrate. For example, the measurement and / or imaging system 101, in one embodiment, can have a width of about 16"-17", a length of about 23"-24", and a height of about 25"-26". In some embodiments, the measurement and / or imaging system 101 can be implemented with two linear actuators and no rotational actuators (wherein the motion of such a system is referred to as XY motion), however, this may result in a measurement and / or imaging system having a larger footprint than the measurement and / or imaging system 101. In some embodiments, the measurement and / or imaging system 101 can have the capability to measure film thickness, CD, CD bias, etc. of about 100 or more substrates per hour, thereby significantly increasing throughput compared to conventional optical metrology systems that measure the entire surface of a substrate. For each substrate, hundreds to thousands (e.g., 3000) of points on each substrate can be measured. The measurements can be used to determine a uniformity profile of the substrate.Thus, in an embodiment, the measurement and / or imaging system 101 may be capable of determining the uniformity profile of a substrate within a duration of between 20 and 50 seconds.

[0026] A sensor (e.g., a reflectometer), e.g., a second sensor, of the measurement and / or imaging system 101 may be used to perform measurements of one or more target locations on the substrate while the substrate is moved by the linear actuator and the rotary motor. The one or more target locations may be determined based on an algorithm or set of instructions that specifies the locations of the target locations and the number of target locations to be measured. In some examples, the substrate may not be centered on the substrate holder. In such examples, the processing logic may determine a coordinate transformation to convert between a coordinate system centered on the substrate holder and a coordinate system centered on the substrate. For each location on the substrate to be measured, the appropriate transformation may be applied. Thus, as the substrate holder (and thus the substrate attached to the substrate holder) rotates, the substrate holder (and thus the substrate) is also moved linearly according to the transformation, so that the correct point on the substrate is measured.

[0027] During setup of the measurement and / or imaging system 101, the linear actuator and the rotary motor can be calibrated to determine the location of the reflectometer relative to the axis of motion of the linear actuator and / or rotary motor. In addition, the substrate holder can have an integrated reference target (or multiple integrated reference targets) that can be measured by a second sensor (e.g., a reflectometer) and / or captured by a first sensor (e.g., a camera) to obtain reference measurements / images. The integrated reference target can be positioned on the substrate holder and / or a stage supporting the substrate holder in a position that is not covered by the substrate, and can rotate and / or otherwise change position when the substrate holder is rotated and / or moved. The integrated reference target can have a known thickness, which does not change over time.

[0028] In one embodiment, the measurement and / or imaging system includes multiple reference targets, which may be fabricated of different materials. For example, one reference target may include bare silicon and one reference target may include silicon with a silicon dioxide layer having a known thickness. The sensor may periodically measure the thickness of the integrated reference target and compare the measured thickness to the known thickness of the integrated reference target. If the measured thickness does not correspond to the known thickness of the target, the processing logic may determine that the sensor is producing an inaccurate measurement. In some embodiments, a linear offset may be determined based on the determined difference between the measured thickness and the known thickness of the integrated reference target. Alternatively, a non-linear offset may be determined. A linear or non-linear offset may be applied to the measurement to obtain an accurate adjusted measurement of the film thickness. In one embodiment, the processing logic determines whether the difference between the measured thickness of the reference target and the known thickness of the reference target exceeds a difference threshold. If the difference is below the difference threshold, an adjustment may be applied as described above. If the difference is equal to or greater than the difference threshold, the measurement and / or imaging system 101 may be scheduled for maintenance. The measurement and / or imaging system 101 may perform a calibration of the second sensor (e.g., a reflectometer) between or during the measurements, or both, to determine an appropriate offset to apply to the measurements.

[0029] In one embodiment, the integrated reference target can cause a spectral reflection towards a second sensor (e.g., a reflectometer) from scanning across the reference target during stage setup. Processing logic can determine where the second sensor is relative to the target based on the location (e.g., (r,θ) coordinates) where the spectral reflection is captured. A similar process can be determined for the first sensor (e.g., a camera). This can provide two coordinate systems that are both referenced by the target. Hence, the relationship between the first sensor coordinate system and the second sensor coordinate system can be known via the reference target.

[0030] When the substrate is lowered onto the substrate holder and secured to the substrate holder, the center of the substrate may be offset from the center of the substrate holder. A processing device of the measurement and / or imaging system 101 may determine one or more coordinate transformations between the center of the substrate and the center of the substrate holder (which corresponds to the axis of rotation about which the substrate holder rotates) and apply the one or more coordinate transformations to correct for the offset, as described in more detail below.

[0031] The processing system 100 may further include a system controller 128. The system controller 128 may be and / or may include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, and / or the like. The system controller 128 may include one or more processing devices, which may be general-purpose processing devices such as a microprocessor, a central processing unit, and / or the like. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIM) microprocessor, or a processor implementing other instruction sets or a processor implementing a combination of instruction sets. The processing device may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, and / or the like. The system controller 128 may include a data storage device (e.g., one or more disk drives and / or solid state drives), a main memory, a static memory, a network interface, and / or other components. The system controller 128 may execute instructions to perform any one or more of the methods and / or embodiments described herein. The instructions may be stored (during execution of the instructions) on a computer-readable storage medium, which may include a main memory, a static memory, a secondary storage, and / or a processing device. In an embodiment, executing the instructions on the system controller 128 causes the system controller to perform the method of Figure 5. The system controller 128 may also be configured to allow for input and display of data, operational commands, and the like by a human operator.

[0032] 2A is a schematic cross-sectional side view of a measurement and / or imaging system 200 having a rotary actuator 202 and a linear actuator 204 according to one embodiment. The measurement and / or imaging system 200 may correspond to the measurement and / or imaging system 101 of FIG. 1 in an embodiment. The rotary actuator 202 may be a motor, a rotary actuator (e.g., a motorized rotary actuator), etc. The linear actuator 204 may be a motorized linear actuator that may convert the rotational motion of a motor into linear or straight motion along an axis. The measurement and / or imaging system 200 may include a substrate holder 206, a camera 208, a sensor 210, and a processing device 212.

[0033] The substrate holder 206 can be a chuck, such as a vacuum chuck, an electrostatic chuck, a magnetic chuck, a mechanical chuck (e.g., a four-jaw chuck, a three-jaw chuck, an edge / ring clamp chuck, etc.), or other type of chuck. The substrate holder 206 can also be or include a substrate-shaped pocket, and / or a plate or other surface with a set of pins or other features (e.g., three pins) that surround the substrate and prevent the substrate from shifting relative to the substrate holder 206 during movement of the substrate holder 206. The substrate holder 206 can secure a substrate 214 (e.g., a wafer). In one embodiment, the substrate holder 206 includes an edge clamp that clamps the substrate from an edge. In an embodiment, the substrate holder 206 is a vacuum chuck. In other embodiments, the substrate holder 206 can be a different type of chuck, such as an electrostatic chuck, a mechanical chuck, a magnetic chuck, etc.

[0034] The rotational actuator 202 can rotate the substrate holder 206 about a first axis 203. The rotational actuator 202 can be controlled by a servo controller and / or a servo motor, which can allow precise control of the rotational position, velocity, and / or acceleration of the rotational actuator, and thus the substrate holder 206. The substrate holder 206 can have a mass between 1.0 kilograms (kg) and 2.0 kg, which can provide a rotational speed of 6000 deg / sec 2 and 14000deg / sec 2 The linear actuator 204 can linearly move the substrate holder 206 along the second axis 205. The linear actuator 204 can be controlled by a servo controller and / or servo motor 222, which can allow precise control of the linear position, velocity, and acceleration of the linear actuator 204, and thus the substrate holder 206.

[0035] The camera 208 can be positioned above the substrate holder 206 and can generate one or more images of the substrate 214 held by the substrate holder 206. The camera 208 can be an optical camera, an infrared camera, or any other suitable type of camera. Alternatively, the camera 208 can be replaced with another type of sensor. The sensor 210 can also be positioned above the substrate holder 206 and can measure at least one target location on the substrate at a time. Depending on the sensor type of the sensor 210, the measurement can be a reflectometry measurement, an optical property measurement, a particle count measurement, a CD measurement, a roughness measurement, a surface geometry measurement, and / or other types of measurements. The camera 208 (or other sensor) and the sensor 210 can be fixed in a stationary position on the measurement and / or imaging system 200, while the substrate holder 206 can be moved in an r-θ motion by the rotary actuator 202 and the linear actuator 204.

[0036] In some embodiments, the ability of the chuck to move according to r-θ motion allows the overall dimensions of the measurement and / or imaging system to be compact compared to a measurement and / or imaging system that moves the chuck with a rectangular (XY) motion system (e.g., a first dimension having a dimension between 20 inches and 28 inches, a second dimension having a dimension between 22 inches and 28 inches, and a third dimension having a dimension between 14 inches and 20 inches, the first dimension, second dimension, and third dimension being perpendicular to each other). In addition, by moving the chuck using an r-θ motion system, substrates may be processed (e.g., uniformity profiles may be determined) at a rate of up to 100 or more substrates per hour. In other words, the processing device 212 may be capable of generating thickness measurements across the substrate 214 and determining a uniformity profile of the substrate 214 within a duration of between 20 seconds and 50 seconds in embodiments.

[0037] In some embodiments, the processing device 212 can determine that the substrate 214 is not centered on the substrate holder 206 and / or an estimate of the position of the substrate on the chuck (e.g., an estimate of the center of the substrate on the chuck) based on one or more images or other measurements of the substrate 214 generated by the camera 208 or other sensor. The substrate 214 may not be centered on the substrate holder 206 when initially placed on the substrate holder 206. The robot blade 220 can place the substrate 214 on the transfer station 218 (e.g., on a set of lift pins or other lift mechanism). The substrate holder 206 can be moved in a first direction along the second axis 205 such that the substrate holder 206 is positioned at the transfer station 218. The transfer station 218 can be located on a lift mechanism 216 (or may be a set of lift pins), which can move the transfer station 218 up and down in a vertical direction (perpendicular to the second axis 205 and parallel to the first axis 203). The substrate 214 may be received by the substrate holder 206 while the substrate holder 206 is positioned at the transfer station 218. The substrate 214 may not be centered on the substrate holder 206. The substrate holder 206 may be moved in a second direction along the second axis 205 until the edge of the substrate 214 is detected by the sensor 210 to be at the target position.

[0038] The substrate holder 206 may be rotated 360 degrees, and images or other measurements (optionally referred to as edge images or peripheral images) may be generated during the rotation of the substrate holder 206. One or more of the measurements and / or images may be obtained with the substrate holder at different θ values, and the detected location of the edge may change. A change in the detected edge may indicate that the substrate (which may be a circular substrate) is off-center. Additionally, the determined change in the detected edge may be used to calculate the amount of offset. The camera, sensor, and stage are synchronized, so that a peripheral image scan may be accomplished in 3-6 seconds, in embodiments, without stopping the stage.

[0039] The edge image (e.g., image frame) can be analyzed with an image processing system. The image processing system detects the edge of the substrate and the location of the flat or notch of the substrate. In one embodiment, the measurement and / or imaging system 200 is connected to an illumination system (e.g., including one or more light sources such as light emitting diodes (LEDs)) that can be turned on and off when the camera measurements and / or sensor measurements (e.g., spectrometer measurements) are collected. In one embodiment, the image processing system corrects for illumination non-uniformity and finds the boundary of the wafer edge. The edge position of each image frame can be input into a circle fitting least squares optimization algorithm. The notch (or flat) angle can be determined by combining the angle of the image with the most likely notch or flat candidate and the position of the notch or flat in the image. The fitted circle can be the center of the substrate and the notch angle can be an angle offset.

[0040] In one embodiment, in addition to or instead of generating an image of the edge of the substrate, the camera generates images of surface features (e.g., alignment features) of the substrate. The features may have known positions on the substrate and, in embodiments, may be used to determine a center of the substrate. In some embodiments, an initial estimate of the center of the substrate is determined based on the image of the edge of the substrate, and a refined or improved estimate of the center of the substrate is determined based on the image of the surface features of the substrate.

[0041] In one embodiment, the parameters (r,θ) determine the offset of the substrate relative to the stage. Using these parameters, the motion system can create forward and inverse transformations that convert the (r,θ) coordinates of the stage to the (r,θ) coordinates of the substrate. The motion system can then calculate the trajectory in space of the substrate while sending commands to move the motors attached to the substrate holder 206. In one embodiment, the motion system runs real-time control software that is connected (e.g., via an EtherCAT network) to the motion drives of the linear and rotary actuators, so that any trajectory in space can be calculated. The controller 212 can calculate the corrected trajectory and send the commanded positions to the motion drives in real time (e.g., at a 1 kHz rate).

[0042] In some embodiments, additional calibrations are performed to determine one or more of the following: 1. The camera stage / chuck origin, defined by the center of rotation of the stage / chuck, 2. Misalignment of the camera with the center of rotation of the stage / chuck, 3. Camera installation error, 4. Boresight correction between the sensor and the camera, and 5. One or more additional calibrations, for example to address small tip, large tilt, and / or wobble.

[0043] Calibration can be used to calculate the sensor stage origin and transformation applied to the camera and / or sensor measurements. Calibration can ensure that the sensor and / or camera measurements are accurate with respect to absolute substrate coordinates. The optical head of the sensor (e.g., reflectometer) and camera can be adjusted in embodiments to place them approximately at the center of the axis of rotation (e.g., at the center of the chuck). In addition, alignment algorithms can add additional precision to the measurement position beyond the mechanical adjustment tolerances.

[0044] In some embodiments, a sensor such as an IR sensor, a laser sensor, or other light sensor may be used to detect the substrate edge in addition to or instead of the camera 208. The IR sensor, laser sensor, or other light sensor may include one or more light sources or light emitters and one or more detectors, or one or more light sources / detectors and one or more mirrors. In one embodiment, while the substrate is not placed between the light emitter (e.g., laser, IR emitter, or other light emitter) and the detector, or between the light source / detector and the mirror, the substrate is not detected by the detector. However, when the substrate is placed between the light source and the detector, or between the light source / detector and the mirror, the beam is broken and the substrate is detected. The r-θ coordinates accompanied by data on whether the substrate was detected at multiple r-θ coordinates can be used to determine that the substrate is off-center and / or the amount of offset. In an embodiment, a single light source and detector pair or a single light source / detector and mirror pair is used. Alternatively, multiple light source and detector pairs or multiple light source / detector and mirror pairs may be used. Multiple light source and detector pairs or light source / detector and mirror pairs can be arranged in a detection array in embodiments. When multiple light source and detector pairs or light source / detector and mirror pairs are used, the pairs can be arranged to provide a light curtain. The light curtain can provide multiple data points for detection of the substrate.

[0045] In some embodiments, the substrate includes a notch and / or a flat. Images from the camera 208 and / or sensor measurements from a sensor (e.g., an edge sensor such as an IR or laser sensor) can be used to determine the location of the flat and / or notch. An XY position can be determined on the substrate based on the determined location of the flat or notch. The XY position can be converted to an r-θ position, and the r-θ position can be converted to an XY position.

[0046] In some embodiments, rotating the substrate holder 206 with the rotary actuator 202 for measuring the target position causes an offset between the field of view of the sensor 210 and the target position of the substrate 214 due to the substrate not being centered on the substrate holder. In this case, the linear actuator 204 can linearly move the substrate holder 206 along a second axis to correct the offset. The sensor 210 can then measure the target position on the substrate 214. Once measurements of all the target points on the substrate have been measured, the processing device 212 can determine a uniformity profile across the surface of the substrate 214 based on the measurements.

[0047] The motion systems (e.g., linear actuator 204 and rotary actuator 206) of measurement and / or imaging system 200 may be synchronized with camera 208 and / or sensor 210, such as through digital trigger signals sent over an EtherCAT network. The drivers of the motion systems may be connected to a real-time control server, for example, over an EtherCAT network. The network, in an embodiment, enables linear actuator 204 and / or rotary actuator 206 to receive and act on commands at a rate of up to 1 kHz. The control network enables processing logic to translate coordinates and paths of measurements on the wafer.

[0048] In some embodiments, the processing device 212 can determine one or more coordinate transformations between the center of the substrate holder 206 (corresponding to the first axis 203 about which the substrate holder 206 rotates) and the center of the substrate 214, and the one or more coordinate transformations are applied during rotation of the substrate holder 206 to correct for the offset.

[0049] In some embodiments, to identify the center of the substrate 214, the sensor 210 can identify several points on the edge of the substrate 214 (either via measurements or via images from the camera 208) and use one or more techniques to fit those points to a circle, such as, for example, a regression analysis fit technique. In other words, the edge position of each image can be fed into an optimized circle fit algorithm to determine the circle. The notch angle can be found by combining the angle of the image with the most likely notch candidate and the location of the notch in the image. The fitted circle indicates the center of the substrate 214 and the notch angle indicates the angular offset.

[0050] In some embodiments, to identify the center and orientation of the substrate 214 with greater accuracy, the sensor 210 can measure the positions of several substrate alignment targets via images from the camera 208. The substrate alignment targets can include substrate marks, patterns, lines, edges, corners, and / or the like. One or more training images of the substrate alignment targets can be stored in the processing system 100. Images of the substrate alignment targets can be captured and compared to the training images to determine the translation. The embodiments can determine the translation with sub-pixel accuracy by an image registration algorithm using techniques such as Fast Fourier Transform (FFT) or Convolutional Neural Network (CNN) deep learning methods. The image registration translation between the substrate positioning targets and the training images forms the position measurement of the alignment target. The position measurements of multiple substrate alignment targets can be used to fit a coordinate transformation, thereby providing further accuracy. The measurements can be combined using a fitting algorithm (e.g., least squares) to refine the estimate of the substrate center and orientation.

[0051] Some embodiments can identify the center and orientation of the substrate using the edge and notch. Some embodiments can identify the center and orientation of the substrate using a substrate alignment target. Some embodiments can identify the center and orientation of the substrate in two steps, step 1 finding the edge and notch and step 2 finding the substrate alignment target.

[0052] In one embodiment, the sensor 210 and camera 208 (or other sensor) are mounted to a structure, which is then mounted to a plate. The chuck and actuators can also be mounted to the plate, so that the entire system, including the moving parts (e.g., chuck, actuators, etc.) and the measurement devices (e.g., camera and sensor) can all be mounted to the same plate, isolated from the outside environment. This provides vibration isolation from the outside environment (e.g., factory interface) to the sensor as well as the chuck, improving the accuracy of the measurements.

[0053] In one embodiment, the one or more integrated reference targets 215 are attached to one end of the substrate holder 206. In one embodiment, the one or more integrated reference targets 215 include a silicon reference target and a silicon dioxide reference target. The one or more integrated reference targets 215 can be used to maintain calibration of the sensor 210. In one embodiment, at least one of the one or more integrated reference targets 215 includes a coating (e.g., of silicon dioxide) having a known thickness, one or more optical properties, one or more material properties, particle count, etc. The sensor 210 can periodically measure the thickness and / or other properties of the coating on the integrated reference target 215. The measured thickness and / or other properties can then be compared to the known thickness and / or other properties of the coating. If the measured thickness and / or other properties do not match the known thickness and / or other properties, a determination can be made that the sensor 210 is not properly calibrated. In one embodiment, a difference between the measured thickness and the known thickness is determined. This difference can then be compared to one or more difference thresholds. If the difference is less than a difference threshold, an adjustment factor can be determined and added to future measurements of the sensor 210. If the difference is greater than a difference threshold, maintenance can be scheduled for the sensor 210. Similar calculations can be performed for other measured and known properties of films on reference targets.

[0054] In one embodiment, the one or more integrated reference targets 215 include a calibration target at a fixed position on the substrate holder 206. The calibration target can be a bare metal or silicon target. In one embodiment, the calibration target is a stainless steel target. The calibration target can cause a spectral reflection toward the sensor 210 when it has a first specific position relative to the sensor 210 (e.g., when it is directly underneath the sensor 210) and / or can cause a spectral reflection toward the camera 208 when it has a second specific position relative to the camera 208. The calibration target rotates with the chuck and can be used to position and / or calibrate the center of the sensor head of the sensor 210 relative to the center of the sensor head of the camera 208. The R and θ values ​​of the substrate holder 206 associated with the sensor measurements of the sensor 210 that result in the spectral reflection can be used to determine where the sensor 210 is relative to the calibration target. Similarly, the R and θ values ​​of the substrate holder 206 associated with the camera measurements of the camera 208 that result in an image in which the calibration target is at the center of the image can be used to determine where the camera 208 is relative to the calibration target. The r-θ values ​​associated with the spectral reflectance of the sensor 210 and the r-θ values ​​associated with the center calibration target image of the camera 208 can be used together to determine the relative positions of the camera 208 and the sensor 210. In one embodiment, the controller 212 rotates and linearly moves the substrate holder to position the substrate holder such that the target is located and centered within the image of the camera 208. The substrate holder 206 is then translated such that the target is directly under the head of the sensor 210. This can be accomplished by moving the substrate holder until the sensor 210 reading has a maximum value. The distance between the position of the substrate holder where the target was at the center of the image generated by the camera and the position of the substrate holder where the target was directly under the sensor 210 can then be determined.

[0055] In some embodiments, the IR system 200 includes one or more diffusers 270 positioned above the substrate holder 206 (and above the substrate 214 when the substrate 214 is placed on the substrate holder 206). The one or more diffusers 270 can flow a gas (e.g., clean dry air, nitrogen, and / or another gas) toward the substrate 214 to prevent particles from migrating onto the substrate 214. The diffuser 270 can flow the gas toward the substrate at any angle, such as, for example, a 90 degree angle, an obtuse angle, and / or an acute angle. In some embodiments, the IR system 200 further includes an exhaust 272 for exhausting the gas (e.g., the gas output by the diffuser 270) and / or particles displaced by the gas from the enclosure of the IR system 200. The exhaust 272 can, in embodiments, pull particles away from the substrate 214.

[0056] FIG. 2B is a top-down schematic view of the substrate holder 206 of the IR system 200 of FIG. 2A according to one embodiment. The substrate holder 206, in an embodiment, can rotate clockwise and / or counterclockwise about an axis at the center of the substrate holder 206. As shown, the substrate holder 206 includes one or more integrated reference targets 215. In one embodiment, the substrate holder 206 includes multiple integrated stops (e.g., wafer stops) 250 to prevent the substrate 214 from moving off the substrate holder 206 in the event of loss of a holding force, such as vacuum pressure or electrostatic force. In one embodiment, the substrate holder 206 includes three integrated stops disposed around the periphery of the substrate holder 206. The stops 250 can be adjustable, such that the distance of each of the stops from the center of the substrate holder 206 can be adjusted. In one embodiment, the stops 250 are evenly disposed around the periphery of the substrate holder 206.

[0057] FIG. 3 is a top-down schematic diagram depicting the rotational coordinates of a substrate holder of an IR system 300 according to one embodiment. The IR system 300 may be the same as or similar to the IR system 101 of FIG. 1 and / or the IR system 200 of FIG. 2. The substrate holder may be the same as or similar to the substrate holder 206 of FIGS. 2A-2B. The substrate holder may be positioned on a stage, and the stage may be rotated about a rotational axis (such as the first axis 203) to rotate the substrate holder as well. In addition, the stage may be moved about a linear axis, such as the second axis 205, to move the substrate holder as well. These motions may cause the substrate holder, and thus the substrate secured to the substrate holder, to move according to an r-θ motion. In other words, the rotational motion may cause the substrate holder to rotate azimuthally, while the linear motion may cause the substrate holder to move radially, thereby allowing a full range of motion of the substrate holder and measurement of the entire surface of the substrate.

[0058] The motion system that drives the stage can be synchronized with the camera and / or reflectometer sensor via a digital trigger signal. The digital trigger signal can be transmitted over a network (e.g., an EtherCAT network or other type of suitable network, etc.). The network can allow for sending trigger signals to control the motion system at a particular rate (e.g., 1 kilohertz (kHz)).

[0059] The substrate holder is aligned in radial and angular coordinates (e.g., polar coordinates) (r 0 ,θ 0 ), which can be equivalently located at an initial position defined by the Cartesian coordinates (x 0 ,y 0 The camera and reflectometer can be positioned above the substrate holder (direction along the axis of rotation) and the radial distances can be expressed as R c and R r Therefore, the substrate holder is rotated at an initial angle θ 0and by rotating the substrate holder from the initial linear coordinate r 0 By linearly moving the substrate from the substrate holder, the entire surface of the substrate holder, and thus the substrate secured by the substrate holder, can be scanned.

[0060] Figure 4 is a schematic diagram of a substrate 414 placed on a substrate holder 406 according to one embodiment. The substrate holder 406 may be the same as or similar to the substrate holder 206 of the IR system 200 of Figure 2. In some embodiments, there may be an error in placing the substrate 414 on the substrate holder 406, such that the center of the substrate 414 is offset from the center of the substrate holder 406.

[0061] In one embodiment, a first coordinate system can be defined with its origin at the center of the substrate holder 406, i.e., (p 0 ) c = [0,0,0], where the first value corresponds to a first axis (e.g., the x-axis), the second value corresponds to a second axis (e.g., the y-axis) perpendicular to the first axis, and the third value corresponds to an angle measured from the first axis to the second axis. In addition, the second coordinate system has its origin at the center of the substrate 414, i.e., (p 0 ) w = [0,0,0]. If there is an error in the placement of the substrate 414, the center of the substrate 214 can be expressed in the first coordinate system as [x offset ,y offset ,θ offset ]=[x 0 ,y 0 ,θ 0 ].

[0062] Any point p 1 =[x 1 ,y 1 ] c is expressed as (p 1 )c=[x 1 ,y 1 ]c, and in the second coordinate system (p 1 ) w =[x 1 ,y1 ] w The coordinate transformation between the first coordinate system and the second coordinate system can be expressed as:

number

[0063]

number

number

[0064]

number

number

[0065] In addition, point p 1 is the general transformation [x,y]=[rcosθ,rsinθ] and the inverse transformation

number

[0066] 5A-5D are a series of graphs illustrating an example of how a placement offset of a substrate on a substrate holder can be cancelled with a circular trajectory, according to one embodiment. FIG 5A shows a substrate holder 504 and a circular trajectory 502 that can be scanned over the substrate by a fixed sensor pointed at the substrate holder 504 from above. When the substrate is perfectly placed on the substrate holder (x offset ,y offset ,θ offsetare all zero), the target scan trajectory could be completed by only a single rotation of the substrate holder after linear motion to place the sensor at the correct radius above the substrate. FIG. 5B shows the same substrate holder 502, but for illustration purposes a substrate 506 is placed on the same substrate holder 504 with a greatly exaggerated offset. A circular trajectory 508 still shows the target trajectory on the substrate 506 to be scanned by the sensor. If the substrate holder 504 were to have the same motion as it did for FIG. 5A, instead of scanning the target trajectory 512 shown in FIG. 5C (r=75 mm for all θ) over the substrate, the substrate holder 504 would scan the trajectory shown by trajectory 510 shown in FIG. 5C, which is substantially offset from the target trajectory. FIG. 5D shows how the placement offset can be cancelled by moving the stage linear axis in conjunction with the rotation axis. The linear axis is moved in such a way that the sensor sees what is represented by the trajectory 516 on the substrate holder in FIG. 5D, so that a target circular trajectory on the substrate (as shown in FIGS. 5A-5B) can be captured by the sensor. This is because the trajectory 516 is offset from the center of the substrate holder 506 by an amount equal to, but in the opposite direction to, the placement offset of the substrate on the substrate holder 506 shown in FIG. 5B. When the substrate holder 206 or 406 is rotated about the first axis by the rotation motor, a given point on the substrate holder follows a circular trajectory (assuming there is no simultaneous linear motion). The motion system (e.g., as described with respect to FIG. 3) can calculate the trajectory of any point in the second coordinate system of the substrate while sending commands to move the motors and / or actuators attached to the stage. In some embodiments, the motion system can also calculate any spatial trajectory by executing real-time control software (e.g., algorithms) that can be connected to and send control signals to the motion drivers via a network (e.g., an EtherCAT network).

[0067] As previously described, the motion system driving the stage can be synchronized with the camera and / or reflectometer sensor via a digital trigger signal. The digital trigger signal can be transmitted over a network (e.g., an EtherCAT network or other type of suitable network, etc.). The network can allow for sending trigger signals to control the motion system at a particular rate (e.g., 1 kilohertz (kHz)). The motion software can calculate a corrected trajectory and send the signal to the motion driver in real time.

[0068] Additional calibrations may be performed with respect to the center (e.g., axis of rotation) of the substrate holder (equivalently the axis of rotation of the stage) and the camera and / or reflectance measurement sensor. For example, in some embodiments, the camera may be misaligned with respect to the axis of rotation of the substrate holder and may benefit from correction. In some embodiments, there may be errors in the mounting of the camera, which also requires correction. In either case, a transformation may be applied to the camera measurements and / or the sensor measurements to correct for any misalignment. These calibrations may ensure that the sensor reflectometer measurements are accurate with respect to the coordinate system of the substrate (second coordinate system). The sensor and camera optical head are adjusted to be approximately centered on the axis of rotation, but the alignment algorithms described above may provide additional precision to the measurement position beyond the mechanical tolerances for the adjustments.

[0069] Additional calibrations may be performed for boresight correction between the reflectometry sensor and the camera. These calibrations may calculate the stage origin of the reflectometry sensor and apply appropriate transformations to the measurements made by the camera. These calibrations ensure that the measurements made by the reflectometry sensor are accurate and aligned with respect to the coordinates of the substrate and / or substrate holder (e.g., depending on how the calibration is performed). The sensor reflectometer and camera optical head may have corresponding adjustment mechanisms, but the alignment and calibration algorithms described above may add further precision to the measurements.

[0070] 6 is a flow diagram of a method 600 for performing coordinate transformation to correct for substrate and substrate holder offset according to one embodiment. In some embodiments, method 600 may be performed by processing logic executed by a processor of a computing device. In some embodiments, method 600 may be performed by a processing device.

[0071] Method 600 begins by reading the actual position of the stage to which the substrate holder is fixed. The stage can be rotated about a first axis by a rotary actuator (motor) and moved along a second axis by a linear actuator, so that the stage and substrate holder can be moved in an r-θ motion. To move the stage, the motor and / or actuator can be rotated in discrete amounts (counts such as encoder counts) (e.g., the motor can rotate in steps of one-tenth, one-fifth, one-third, etc. of a full rotation) (block 602). This can be determined based on commands from a drive system (motion drive system). This motion is translated into an actual motion of the stage (block 604). For example, a given step of the motor can correspond to moving the stage by a specific amount (rotation by a specific number of degrees / radians, physical linear translation by a specific distance). Coordinate transformations can be applied to compensate for the center of the substrate being offset from the center of the substrate holder. In addition, the mutual offset between the stage, reflectometry sensor, and camera is calibrated and centered with respect to the selected point (block 606). Processing logic may determine a target trajectory including the position and velocity of the substrate for the camera and reflectometry sensor to obtain images and measurements of the substrate, respectively (block 608). Processing logic may apply an inverse coordinate transformation to the target trajectory to determine the rotational and linear motion of the stage to be applied to replicate the desired trajectory. The inverse coordinate transformation may represent the actual motion of the stage (block 610). Processing logic may convert the actual motion back to motor counts (block 612). The motor counts may be sent to the drive system. The method 600 then ends.

[0072] The method 600 may be repeated for each target location on the substrate to be measured.

[0073] 7 is a block diagram of a method 700 for performing coordinate transformation to correct for substrate and substrate holder offset according to one embodiment. In some embodiments, the method 700 may be performed in processing logic executed by a processor of a computing device. In some embodiments, the method 700 may be performed in a processing device. In some embodiments, the method 700 may be performed in the processing system 100. In some embodiments, the method 700 may be performed in the measurement and / or imaging system 200 of FIG. 2A.

[0074] Method 700 begins by processing logic receiving a substrate on a substrate holder of an optical measurement device (block 702). In some embodiments, the optical measurement device may be part of the processing system 100 of FIG. 1. In some embodiments, the optical measurement device may be either all or part of the IR system 200 of FIG. 2A. The processing logic determines that the substrate is not centered on the substrate holder (block 704) and / or the position of the substrate on the substrate holder (e.g., the offset of the center of the substrate relative to the center of the substrate holder). The amount of offset between the substrate and the substrate holder is [x 0 ,y 0 ,θ 0 ]. Processing logic measures one or more target locations on the substrate (block 706). Processing logic may measure the one or more target locations using a sensor. Processing logic determines a uniformity profile across the surface of the substrate based on the measurements of the one or more target locations.

[0075] In some embodiments, to measure one of the one or more target locations on the substrate, the processing logic rotates the substrate holder about a first axis. The rotation of the substrate holder about the first axis causes an offset between the field of view of the sensor and the target location due to the substrate not being centered on the substrate holder. The processing logic can correct the offset by linearly moving the substrate holder along a second axis.

[0076] In some further embodiments, the processing logic determines one or more coordinate transformations between a first center of the substrate holder and a second center of the substrate. The first center of the substrate holder corresponds to a first axis about which the substrate holder rotates. The processing logic applies at least one of the one or more coordinate transformations to the substrate holder during the rotation to compensate for the offset.

[0077] In some further embodiments, the processing logic identifies one or more points corresponding to the edge of the substrate and performs a circle fitting of the one or more points of the edge of the substrate. The processing logic may perform this step to identify a (second) center of the substrate.

[0078] In some further embodiments, the processing logic uses the uniformity as an input to a machine learning model. The processing logic obtains one or more outputs of the machine learning model. In some embodiments, the one or more outputs are indicative of process drift of the measurement device. The method 700 ends.

[0079] 8 illustrates a model training workflow 805 and a model application workflow 817 for process drift determination, film thickness determination, and / or substrate offset determination according to one embodiment. The model training workflow 805 and the model application workflow 817 may be performed by processing logic executed by a processor of a computing device. One or more of these workflows 805, 817 may be performed, for example, by one or more machine learning modules implemented on the processing device and / or other software and / or firmware executing on the processing device.

[0080] A model training workflow 805 is for training one or more machine learning models (e.g., deep learning models) to perform one or more of the determination, prediction, correction, etc. tasks related to process drift determination (e.g., determining parameter corrections to account for process drift occurrences), film thickness determination, and / or substrate offset determination (also referred to as substrate center determination), CD determination, CD bias determination, etc. A model application workflow 817 is for applying one or more trained machine learning models to perform one or more of the determination tasks. One or more of the machine learning models can receive target reflectance measurement data 810 (e.g., of a reference target integrated on the substrate holder 206 of FIGS. 2A-2B), substrate reflectance measurement data 812 (or other measurement data), and / or image data 814 (or other measurement data).

[0081] Various machine learning outputs are described herein. A particular number and configuration of machine learning models are described and illustrated. However, it should be understood that the number and type of machine learning models used and the configuration of such machine learning models may be varied to achieve the same or similar end result. Thus, the machine learning model configurations described and illustrated are merely examples and should not be construed as limiting.

[0082] In an embodiment, one or more machine learning models are trained to perform one or more of the following tasks: Each task may be performed by a separate machine learning model. Alternatively, a single machine learning model may perform each of the tasks or a subset of the tasks. Additionally or alternatively, different machine learning models may be trained to perform different combinations of the tasks. In one example, one or several machine learning models may be trained, where the trained machine learning (ML) model is a single shared neural network with multiple shared layers and multiple higher-level separate output layers, each of which outputs a different prediction, classification, discrimination, etc. Tasks that one or more trained machine learning models may be trained to perform are as follows: a. Process Drift Determination - When a substrate is being processed in a processing chamber, several variables may change or drift as a function of time. For example, there may be temperature variations, deposition / etch rate variations, etc., which may affect the film thickness uniformity (e.g., uniformity profile) across the surface substrate being processed. In addition, chemical buildup, which may occur over longer time scales, may affect the uniformity profile across the surface of the substrate. The model will receive the target reflectometry data and determine the parameters to be adjusted for calibration of the sensor for reflectometry measurements. b. Film Thickness Determination - In some embodiments, it is desirable for the film thickness across the surface of the substrate to be as uniform as possible. To efficiently measure the uniformity profile of the substrate, a sensor measures several target locations on the substrate. The number of locations and the locations of the locations need to be optimally determined to obtain an accurate representation of the uniformity profile. A machine learning model can receive measurements of one or more target locations and determine that the number and locations of the one or more target locations are sufficient to provide an accurate representation of the uniformity profile. c. Measurement Determination - A sensor measures a number of target locations on a substrate. The number of locations and the locations of the locations can be measured to determine optical constants, roughness, film thickness, material properties, particles, etc. A machine learning model can receive the measurements of the one or more target locations and determine that the number and locations of the one or more target locations are sufficient to give an accurate representation of the uniformity profile. d. Substrate offset / substrate center determination in IR systems - One or more images can be generated by the camera as long as the substrate holder has one or more r,θ coordinate settings. These images and / or the r,θ coordinate settings of the substrate holder are input into a trained ML model, which outputs an indication of the substrate center. The ML model can output the offset of the substrate center relative to the substrate holder center (e.g., x,y offset or r,θ offset) and / or a transformation function for transforming between the r,θ coordinate system of the substrate center and the r,θ coordinate system of the substrate holder center. e. Substrate offset determination for a substrate in a process chamber - Using sensor-inferred data such as film thickness, CD, etc. at multiple target locations on a substrate, a substrate map (e.g., a wafer map) can be generated. Data from the substrate map can be input into a trained ML model, which can output an estimate of the offset of the placement of the substrate in a process chamber in which the substrate was previously processed.

[0083] One type of machine learning model that can be used to perform some or all of the above tasks is an artificial neural network, such as a deep neural network. An artificial neural network generally includes a feature representation component with a classifier or regression layer that maps features to a desired output space. A convolutional neural network (CNN), for example, hosts many layers of convolutional filters. Pooling is performed and nonlinearities are addressed in the lower layers, on top of which a multi-layer perceptron is typically added to map the top layer features extracted by the convolutional layers to a decision (e.g., classification output). Deep learning is a class of machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks can be trained in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, with different layers learning different levels of representations corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract synthetic representation. In particular, the deep learning process can teach itself which features to best place at which level. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, deep learning systems have a substantial credit allocation path (CAP) depth. A CAP is a chain of transformations from input to output. A CAP describes the potential causal relationships between inputs and outputs. In feedforward neural networks, the CAP depth can be the depth of the network, which can be the number of hidden layers plus one. In recurrent neural networks, where signals may propagate through layers more than once, the CAP depth is potentially unlimited.

[0084] Training a neural network can be accomplished with supervised learning methods, which involve feeding a training dataset consisting of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the output and the label values), and using techniques such as deep gradient descent and backpropagation to adjust the network weights across all layers and nodes such that the error is minimized. In many applications, repeating this process across many labeled inputs in the training dataset results in a network that can generate the correct output when presented with inputs that differ from those present in the training dataset.

[0085] In the model training workflow 805, a training data set including hundreds, thousands, tens of thousands, hundreds of thousands, or more target reflectance measurement data 810 (e.g., uniformity profile determination at different time points using different numbers and locations of target locations on a substrate given a particular set of process conditions), substrate reflectance measurement data 812 (e.g., one or more thickness profiles obtained by taking different numbers and / or locations of target locations on the substrate), and / or image data 814 of a substrate on a substrate holder (optionally accompanied by position coordinates such as linear and rotational positions of the substrate holder) should be used to form a training data set. The data can include, for example, uniformity profiles determined using a given number of measurements, each associated with a particular target location. This data can be processed to generate one or more training data sets 836 for training one or more machine learning models. The machine learning model may be trained to, for example, automatically calibrate the sensor between and / or during measurements of the substrate, and / or to automatically compensate for process variations that may affect the measured uniformity profile of the substrate (such as changing conditions in a processing chamber, substrate placement offset from the center of the substrate holder, etc.), and / or to determine the offset of the substrate center relative to the substrate holder center.

[0086] In one embodiment, generating the one or more training data sets 836 includes collecting one or more target substrate reflectometry data and / or collecting one or more images of the substrate on the substrate holder at different offsets, orientations, and / or substrate holder orientations / positions. The labels used may depend on what a particular machine learning model is trained to do. For example, to train a machine learning model to perform process drift determination, the training data set 836 may include data labels indicating reflectometry measurements of a reference target over time. To train an ML model to determine a center offset (e.g., determine the center of the substrate), the labels may include an offset between the substrate center and the substrate holder center. As described in other embodiments, the process recipe and / or process results may be represented as vectors and the process rates may be represented as one or more matrices.

[0087] To perform the training, the processing logic inputs the training data set 836 to one or more untrained machine learning models. Before inputting the first input to the machine learning model, the machine learning model can be initialized. The processing logic can train the untrained machine learning model based on the training data set to generate one or more trained machine learning models that perform the various operations described above. The training can be performed by inputting one or more of the target reflectance measurement data 810 and the substrate reflectance measurement data 812 to the machine learning model one by one.

[0088] Machine learning models process inputs to generate outputs. An artificial neural network includes an input layer consisting of values ​​of data points. The next layer is called a hidden layer, where the nodes in the hidden layer each receive one or more of the input values. Each node includes parameters (e.g., weights) to apply to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer can be another hidden layer or an output layer. In either case, the nodes in the next layer receive the output values ​​from the nodes in the previous layer, and each node applies weights to those values ​​and then generates its own output value. This can be done at each layer. The final layer is the output layer, where there is one node for each class, prediction, and / or output that the machine learning model can produce.

[0089] Thus, the output may include one or more predictions or inferences. For example, the output predictions or inferences may include one or more corrections to the substrate reflectance measurement data (e.g., corrections based on sensor calibration). The processing logic may cause the substrate to be processed using the updated strategy (e.g., with the identified corrections) and receive an updated thickness profile. The processing logic may compare the updated thickness profile to a target thickness profile to determine whether a threshold criterion is met (e.g., the thickness values ​​measured across the surface of the wafer are within a target threshold window). The processing logic may determine an error (i.e., a classification error) based on a difference between the updated thickness profile and the target thickness profile. The processing logic adjusts weights of one or more nodes in the machine learning model based on the error. An error term or delta may be determined for each node of the artificial neural network. Based on the error, the artificial neural network adjusts one or more of the parameters (weights for one or more inputs of the node) for one or more of its nodes. The parameters may be updated in a backpropagation manner such that the nodes in the top layer are updated first, followed by the nodes in the next layer, and so on. An artificial neural network includes multiple layers of "neurons," each of which receives as inputs values ​​from neurons in the previous layer. The parameters of each neuron include weights associated with the values ​​received from each of the neurons in the previous layer. Adjusting the parameters can thus include adjusting the weights assigned to each of the inputs of one or more neurons in one or more layers of the artificial neural network.

[0090] After the model parameters are optimized, model validation can be performed to determine whether the model has been improved and to determine the current accuracy of the deep learning model. After one or more rounds of training, the processing logic can determine whether a stopping criterion has been met. The stopping criterion can be a target level of accuracy, a target number of images processed from the training dataset, a target amount of change in parameters relative to one or more previous data points, combinations thereof, and / or other criteria. In one embodiment, the stopping criterion is met when at least a minimum number of data points have been processed and at least a threshold accuracy has been achieved. The threshold accuracy can be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met when the accuracy of the machine learning model stops improving. If the stopping criterion is not met, further training is performed. If the stopping criterion is met, the training can be completed. Once the machine learning model is trained, a reserved portion of the training dataset can be used to test the model.

[0091] As an example, a machine learning model (e.g., process drift determiner 867) is trained to determine changes in process parameters over time. Similar processes can be performed to train machine learning models to perform other tasks described above. A large number (e.g., hundreds to millions) of target reflectance measurement data sets can be collected and sensor calibration data 869 can be determined.

[0092] Once the one or more trained machine learning models 838 are generated, they can be stored in the model storage 845 and can be added to the process drift determiner 867, the measurement determiner 864, the substrate offset determiner 880, and / or the offset determiner 865. The process drift determiner 867, the measurement determiner 864, the substrate offset determiner 880, and / or the offset determiner 865 can then be used by the one or more trained machine learning models 838 and additional processing logic.

[0093] In the apply model workflow 817, according to one embodiment, the input data 862 may be input to a process drift determiner 867, which may include a trained neural network. Based on the input data 862, the process drift determiner 867 outputs information indicative of parameters that may need to be adjusted to calibrate the sensor.

[0094] According to one embodiment, the input data 862 may include one or more images of the substrate on the substrate holder. The input data 862 may further include the position and orientation of the substrate holder (e.g., as r,θ coordinates in a coordinate system centered on the substrate holder). In one embodiment, the position and orientation are provided as a linear position (e.g., from a linear encoder associated with a linear actuator) and a rotational position (e.g., from a rotary encoder associated with a rotary actuator). The input data 862 may be input to a substrate offset determiner 865, which may output a substrate center. In one embodiment, the offset determiner 865 outputs a substrate offset 870 indicating an offset between the substrate holder center and the substrate center. Based on the substrate offset 870, the controller may determine a coordinate transformation to convert between the substrate holder centered coordinate system and the substrate centered coordinate system. These transformations may be used to determine how to move the linear actuator during a substrate holder rotation to ensure that a point at a target radius stays under a sensor during measurement of a point at a radius on the substrate.

[0095] According to one embodiment, the input data 862 may be input to a measurement determiner 864 (which may be a film thickness determiner, a CD deviation determiner, a specific count determiner, an optical constant determiner, etc.), which may include a trained neural network. In some embodiments, sensor calibration data 869 may be input to the measurement determiner 864. Based on the input data 862, the measurement determiner 864 may generate one or more outputs related to critical dimensions, film thickness, optical constants, particle counts, etc., also referred to as measurement information 8866. In one embodiment, the measurement information 866 output output by the measurement determiner 864 includes a map of measurement values ​​across the substrate. In one embodiment, the measurement information 866 output by the measurement determiner 866 includes a map of measurement values ​​(e.g., CD deviation from a nominal value on the surface of the substrate, CD bias values, film thickness, etc.).

[0096] According to one embodiment, the input data 862 may be input to a substrate offset determiner 880, which may include a trained neural network. In some embodiments, the sensor calibration data 869 may be input to the substrate offset determiner 880. Based on the input data 862, the substrate offset determiner 880 may generate an output indicative of the offset that a previously processed substrate had while it was in the process chamber that processed the substrate. This information may be used to determine if the robot arm is placing the substrate incorrectly in the process chamber, if the lift pins are misaligned in the process chamber, etc.

[0097] 9 illustrates a block diagram of an example computing device capable of process drift and film thickness determination, operating in accordance with one or more aspects of the present disclosure. In various illustrative examples, various components of computing device 900 may represent various components of a computing device, a controller, and / or a control panel (e.g., similar elements described in connection with FIGS. 1-3).

[0098] The exemplary computing device 900 may be connected to other computer devices in a local area network (LAN), an intranet, an extranet, and / or the Internet. The computing device 900 may operate in the capacity of a server in a client-server network environment. The computing device 900 may be a personal computer (PC), a set-top box (STB), a server, a network router, switch or bridge, or any device capable of executing (sequentially or otherwise) a set of instructions that specify actions to be taken by the device. Furthermore, although only a single exemplary computing device is shown, the term "computer" should also be understood to include any collection of computers that individually or collectively execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0099] The exemplary computing device 900 may include a processing device 902 (also referred to as a processor or CPU), a main memory 904 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., a data storage device 918), which may communicate with each other via a bus 930.

[0100] The processing device 902 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, and the like. More specifically, the processing device 902 may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIM) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 902 may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, and the like. According to one or more aspects of the present disclosure, the processing device 902 may be configured to execute instructions implementing the methods 600-800 illustrated in FIGS. 6-8.

[0101] The example computing device 900 may further include a network interface device 908 that may be communicatively coupled to a network 920. The example computing device 900 may further include a video display 910 (e.g., a liquid crystal display (LCD), touch screen, or cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard), a cursor control device 914 (e.g., a mouse), and an audio signal generating device 916 (e.g., a speaker).

[0102] The data storage device 918 may include a machine-readable storage medium (or more specifically a non-transitory machine-readable storage medium) 928 on which one or more sets of executable instructions 922 are stored. According to one or more aspects of the present disclosure, the executable instructions 922 may include executable instructions related to performing the methods 600-800 illustrated in FIGS.

[0103] The executable instructions 922 may also reside, completely or at least partially, within the main memory 904 and / or within the processing device 902 during execution thereof by the exemplary computing device 900, with the main memory 904 and the processing device 902 also constituting computer-readable storage media. The executable instructions 922 may also be transmitted or received over a network via the network interface device 908.

[0104] Although computer readable storage medium 928 is illustrated in FIG. 9 as a single medium, the term "computer readable storage medium" should be interpreted to include a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of operating instructions. The term "computer readable storage medium" should also be interpreted to include any medium that can store or encode a set of instructions for execution by a machine that causes the machine to perform any one or more of the methods described herein. Thus, the term "computer readable storage medium" should be interpreted to include, but is not limited to, solid state memories, and optical and magnetic media.

[0105] Some portions of the preceding detailed descriptions are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. These steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0106] It should be borne in mind, however, that all these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise indicated, and as will be apparent from the discussion that follows, discussions throughout this specification utilizing terms such as "identify," "determine," "store," "adjust," "cause," "receive," "compare," "measure," "correct," "apply," "use," "obtain," "replace," "perform," and the like, will be understood to refer to operations and processes of a computer system or similar electronic computing device that manipulate and transform data represented as physical (electronic) quantities in the computer system's registers and memory to other data similarly represented as physical quantities in the computer system's memory or registers or other such information storage, transmission or display device.

[0107] Examples of the present disclosure also relate to an apparatus for carrying out the methods described herein. The apparatus may be specially constructed for the target purpose, or may be a general-purpose computer system that is selectively programmed by a computer program stored in the computer system. Such computer programs may be stored in computer-readable storage media, such as, but not limited to, any type of disk, including optical disks, compact disk read-only memories (CD-ROMs), and magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic disk storage media, optical storage media, flash memory devices, other types of machine-accessible storage media, or any type of media suitable for storing electronic instructions, each of which is coupled to a computer system bus.

[0108] The methods and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to build more specialized apparatus to perform the steps of the methods. Structures for a variety of these systems are provided as described in the following description. In addition, the scope of the present disclosure is not limited to any particular programming language. It will be appreciated that a variety of programming languages ​​can be used to implement the teachings of the present disclosure.

[0109] The foregoing description sets forth numerous specific details, such as examples of specific systems, components, methods, and the like, to provide a good understanding of some embodiments of the present disclosure. However, it will be apparent to those skilled in the art that at least some embodiments of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods have not been described in detail or have been presented in a simple block diagram format to avoid unnecessarily obscuring the present disclosure. Thus, the specific details described are merely exemplary. It is intended that certain implementations may differ from these exemplary details and still be within the scope of the present disclosure.

[0110] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment. In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". When the term "about" or "approximately" is used herein, it is intended to mean that the accuracy of the stated nominal value is within ±10%.

[0111] Although the operations of the methods herein are shown and described in a particular order, the order of operations of each method may be changed such that certain operations may be performed in the reverse order, or such that certain operations may be performed at least in part concurrently with other operations. In alternative embodiments, instructions of separate operations or sub-operations may be in an intermittent and / or alternating manner.

[0112] It should be understood that the above description is intended to be illustrative, and not limiting. Many other embodiments will be apparent to those skilled in the art upon reading and understanding the above description. The scope of the present disclosure should therefore be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. a substrate holder for fixing the substrate; a first actuator for rotating the substrate holder about a first axis; a second actuator for linearly moving the substrate holder along a second axis; a first sensor for generating one or more first measurements or images of a first plurality of target locations on the substrate; a second sensor for generating one or more second measurements of a second plurality of target locations on the substrate; and 1. A processing device, comprising: determining an estimate of a position of the substrate on the substrate holder based on the one or more first measurements or images of the first plurality of target locations; causing the first actuator to rotate the substrate holder about the first axis for measuring one of the second plurality of target positions, the rotation causing an offset between a field of view of the second sensor and the target position on the substrate due to the substrate not being centered on the substrate holder; causing the second actuator to linearly move the substrate holder along the second axis to correct the offset; determining a profile across a surface of the substrate based on the one or more second measurements of the second plurality of target locations produced by the second sensor; and A processing device for performing 13. An apparatus comprising:

2. The processing device further comprises: determining one or more coordinate transformations between a first center of the substrate holder corresponding to the first axis about which the substrate holder rotates and a second center of the substrate; applying the one or more coordinate transformations during the rotation to correct for the offset; The apparatus of claim 1 for performing the following:

3. 3. The apparatus of claim 2, wherein the first sensor is a camera configured to capture one or more images of at least one of an edge of the substrate or one or more features on the substrate, and the processing device is further for determining the second center of the substrate using the one or more images of the edge of the substrate or at least one of the one or more features of the substrate.

4. The processing device further comprises: identifying a plurality of points corresponding to an edge surface of the substrate; fitting the points of the end face to a circle; and The device according to claim 1 .

5. 2. The apparatus of claim 1, wherein the processing device is for determining a notch angle representing an angular component of the offset based on the one or more first measurements or images produced by the first sensor.

6. The apparatus of claim 1 , wherein the substrate holder is a vacuum chuck having a mass between 1.0 and 2.0 kilograms.

7. The processing device further comprises: moving the substrate holder in a first direction along the second axis to position the substrate holder at a transfer station, wherein the substrate is received on the substrate holder while the substrate holder is positioned at the transfer station; moving the substrate holder in a second direction along the second axis to cause an edge of the substrate to be detected by the first sensor; The apparatus of claim 1 for performing the following:

8. The apparatus of claim 1 , wherein the second sensor is a reflectometry sensor and the one or more second measurements include a reflectometry measurement.

9. 2. The apparatus of claim 1, wherein the apparatus has a first dimension between 20 inches and 28 inches, a second dimension between 22 inches and 28 inches, and a third dimension between 14 inches and 20 inches, the first dimension, the second dimension, and the third dimension being perpendicular to one another.

10. 2. The apparatus of claim 1, wherein the substrate is a wafer, the profile is a full wafer uniformity profile, and the processing device determines the full wafer uniformity profile of the wafer within a duration between 20 seconds and 50 seconds.

11. The apparatus of claim 1 , wherein the at least one of the first sensor or the second sensor comprises an infrared sensor, an ultraviolet sensor, a visible light sensor, or a combination thereof.

12. a reference target attached to the substrate holder, the reference target comprising a film having known properties; Further comprising: the second sensor is further for generating a measurement of the reference target; The apparatus of claim 1 , wherein the processing device is further for calibrating the second sensor by comparing the measurements of the reference target to the known characteristics of the reference target.

13. A reference target attached to the substrate holder. Further comprising: the apparatus is for generating one or more third measurements of the reference target at a first position of the substrate holder using the first sensor and generating one or more fourth measurements of the reference target at a second position of the substrate holder using the second sensor; The apparatus of claim 1 , wherein the processing device is for calibrating a position of the second sensor relative to the first sensor based on the one or more third measurements and the one or more fourth measurements.

14. one or more diffusers positioned above the substrate holder, the one or more diffusers for directing gas toward the substrate; an exhaust for exhausting the gas outside the enclosure of the device; The apparatus of claim 1 further comprising:

15. 10. The apparatus of claim 1, further comprising one or more integrated stops on the substrate holder to prevent the substrate from dislodging from the substrate holder during movement of the substrate holder, the one or more integrated stops comprising at least one of a substrate-shaped pocket, one or more guard rails, or a plurality of protrusions.

16. A transfer chamber; A process chamber; a load lock connected to the transfer chamber; a factory interface connected to the load lock, the factory interface including a robotic arm; and an optical measurement device connected to the factory interface, the robot arm for moving a substrate from the process chamber to the optical measurement device, the optical measurement device comprising: a substrate holder for fixing the substrate; a first actuator for rotating the substrate holder about a first axis; a second actuator for linearly moving the substrate holder along a second axis; a first sensor for generating one or more first measurements or images of a first plurality of target locations on the substrate; a second sensor for generating one or more second measurements of a second plurality of target locations on the substrate; and a processing device for determining an estimate of an offset of a center of the substrate relative to a center of the substrate holder based on the one or more first measurements. an optical measurement device, Including, the system.

17. The system of claim 16 , wherein the optical measurement device is mechanically isolated from the factory interface and the external environment to isolate the optical measurement device from vibrations.

18. The processing device further comprises: causing the first actuator to rotate the substrate holder about the first axis for measuring one of the second plurality of target positions, the rotation causing an offset between a field of view of the second sensor and the target position on the substrate due to the substrate not being centered on the substrate holder; causing the second actuator to linearly move the substrate holder along the second axis to correct the offset; determining a profile across a surface of the substrate based on the one or more second measurements of the second plurality of target locations; and The system of claim 16 for performing the following:

19. determining one or more coordinate transformations between the center of the substrate holder corresponding to the first axis about which the substrate holder rotates and the center of the substrate; applying the one or more coordinate transformations during a rotation of the substrate holder about the first axis to compensate for an offset between the center of the substrate holder and the center of the substrate; and further comprising a processing device for performing 17. The system of claim 16.

20. Receiving a substrate on a substrate holder of an optical measurement device; estimating a position of the substrate on the substrate holder; measuring a plurality of target locations on the substrate using a sensor, the measuring a target location of the plurality of target locations comprising: rotating the substrate holder about a first axis, the rotation causing an offset between the field of view of the sensor and the target position on the substrate due to the substrate not being centered on the substrate holder; correcting the offset by linearly moving the substrate holder along a second axis; measuring the determining, by a processing device, a profile across a surface of the substrate based on measurements of the plurality of target positions; A method comprising:

Images