Integrated substrate 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 this disclosure generally relate to systems and methods for performing reflectivity measurements, imaging, and / or other measurements, as well as integrated substrate measurement systems that may include reflectometers and / or imagers. [Background technology]

[0002] The manufacture of modern materials often involves various deposition techniques, such as chemical vapor deposition (CVD) or physical vapor deposition (PVD), in which one or more selected types of atoms are deposited onto a substrate (wafer) held in a low or high vacuum environment provided by a vacuum deposition chamber. Materials manufactured in this manner can include single crystals, semiconductor films, microcoatings, 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 material grown in the substrate processing system, as well as the uniformity of the process. The goal of uniformity leads to the monitoring and measurement of uniformity of the substrate and the manufacturing process. Improving the precision, reliability, and efficiency of such techniques presents several technical challenges. [Overview of the Initiative]

[0003] In the 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 hold a substrate in place. The first actuator can rotate the substrate holder about a first axis, and the second actuator can move the substrate holder linearly along a second axis. The first sensor can generate one or more first measurements or images of a first set of target locations on the substrate. The second sensor can generate one or more second measurements of a second set of target locations on the substrate. The processing device can determine an estimate of the position of the substrate on the substrate holder based on 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 of the second set of target locations. The rotation may 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 moves the substrate holder linearly along a second axis using a second actuator to compensate for the offset. The processing device can further determine a profile (e.g., a uniformity profile) across the substrate surface based on one or more second measurements of a second set of target positions.

[0004] In the exemplary embodiment, the system may 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 measuring device connected to the FI. The FI may include a robotic arm. The robotic arm can move the substrate from the process chamber to the optical measuring device. The optical measuring device may include a substrate holder for securing the substrate, a first actuator for rotating the substrate holder about a first axis, a second actuator for moving the substrate holder linearly 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 positions on the substrate, and a processing device for estimating an estimate of the offset of the center of the substrate relative to the center of the substrate holder based on one or more first measurements or images.

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

[0006] This disclosure is provided not as an limitation but as an example, and similar references are shown in the drawings of the accompanying drawings, which depict similar elements. Note that different references to “an” or “one” embodiments in this disclosure do not necessarily refer to the same embodiment, and such references mean at least one. [Brief explanation of the drawing]

[0007] [Figure 1] This is a schematic top view of an exemplary processing system according to one embodiment. [Figure 2A] This is a schematic cross-sectional view of an IR system having a rotary actuator and a linear actuator according to one embodiment. [Figure 2B] This is a top-down schematic diagram of the substrate holder of the IR system shown in Figure 2A, according to one embodiment. [Figure 3] This is a schematic diagram showing the rotational and linear coordinates of the substrate holder of an IR system according to one embodiment. [Figure 4] This is a schematic diagram of a substrate placed on a substrate holder according to one embodiment. [Figures 5A-5D] This is a series of graphs illustrating the transformation of a circular orbit in one embodiment. [Figure 6] This is a flowchart illustrating a method for performing a coordinate transformation to correct substrate and substrate holder offsets according to one embodiment. [Figure 7] This is a block diagram of a method for performing coordinate transformations to correct substrate and substrate holder offsets according to one embodiment. [Figure 8] This figure shows a model training workflow and model application workflow 817 for process drift determination and film thickness determination according to one embodiment. [Figure 9] This is a block diagram of an exemplary computing device capable of process drift and film thickness determination, operating according to one or more aspects of the present disclosure. [Modes for carrying out the invention]

[0008] 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), and other physical parameters related to the substrate processing results, across all or part of the surface of a substrate after it has been processed in a processing chamber. Process results, including film thickness, CD, CD bias, etc., can be monitored across one or more substrates for etching and deposition processes. Film thickness information, CD information, CD bias information, etc., can be used to monitor process drift, which may result in non-uniformity across the substrate surface. In embodiments, an integrated reflectivity measuring device or other integrated measuring and / or imaging device is a component of the device manufacturing system. The integrated measuring device can be used to measure the surface of a substrate while the substrate is still in the device manufacturing system. This allows for the detection of problems such as film non-uniformity 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 drift and measure the uniformity of the substrate after processing. The integrated measurement device in the embodiment has a small footprint due to its unique design, which allows the integrated reflectance measurement device to be integrated into or mounted on a transfer chamber or factory interface without consuming a significant amount of factory floor space. The integrated measurement device in the embodiment 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 users with information and / or warnings regarding film thickness issues, non-uniformity, process drift, CD, CD bias, and other physical parameters related to substrate processing results in a rapid, effective, and real-time manner, thereby enabling users to correct issues such as film thickness, CD, CD bias, non-uniformity, and process drift as they occur. In addition, aspects of the disclosure solve the above-mentioned problems and other problems by utilizing reflectivity measurement and / or other measurement systems that include a substrate support (e.g., a substrate holder) that moves both rotationally and linearly so that a sensor (e.g., an optical sensor) can capture point measurements across the entire surface of the substrate. The measurement system may also include a camera or other sensor available to locate the edge faces of a substrate fixed on the substrate support. The substrate support may be moved linearly until the camera and / or sensor detect an edge face. The substrate support may 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 movement of the substrate support to compensate for the offset of the substrate's center from the center of the substrate holder. Thus, measurements can be generated for many points on the substrate (e.g., over the entire surface of the substrate), and each measurement can be associated with a precise location on the substrate.

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

[0011] Based on images from a first sensor or camera and / or sensor measurements, it is possible to determine 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 to determine an estimate of the offset position of the substrate on the substrate holder. A coordinate transformation is determined between the center of the substrate and the center of the substrate holder (around which the substrate holder rotates) and can be applied to correct the offset. In some embodiments, the coordinate transformation can be applied during the rotation of the substrate holder to correct the offset.

[0012] In some embodiments, the second sensor may be the same as those described above, or a different sensor, and may be a reflectivity measurement sensor. The second sensor measures one or more target positions on the substrate and generates a measurement value of the reflectivity measurement method at the one or more target positions. In some embodiments, the substrate holder can be a vacuum substrate holder having a mass to provide a substrate holder with an inertia that allows for high-speed rotation. Alternatively, the substrate holder can 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, a particle number profile, an optical constant profile, etc. across the surface of the substrate can be determined based on the measurement values at 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, that is coupled to a factory interface or integrated within the factory interface. The measurement and / or imaging system can have a small 3D installation 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 installation area than other optional measurement systems. Additionally, the measurement and / or imaging system can have a higher throughput than other optical measurement systems (e.g., than other reflectivity measurement systems). In some embodiments, the measurement and / or imaging system can move the substrate holder in a plane by linearly moving the substrate holder in two directions (e.g., XY).

[0015] FIG. 1 is a top schematic view of an exemplary processing system 100 according to one embodiment. In some embodiments, processing system 100 can be an electronic device processing system configured to execute one or more processes on a substrate 102. In some embodiments, processing system 100 can be an electronic device manufacturing system. Substrate 102 can be any suitably rigid and dimensionally fixed flat article 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, and the like.

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

[0017] The processing chambers 114, 116, and 118 can be adapted to perform any number of processes on the substrate 102. The same or different substrate processes may be performed in each of the processing chambers 114, 116, and 118. Examples of substrate processes include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, and metal or metal oxide removal. In one example, the PVD process may be performed in one or both of the process chambers 114, the etching process in one or both of the process chambers 116, and the annealing process in one or both of the process chambers 118. Other processes may be performed on the substrate within them. Each of the processing chambers 114, 116, and 118 may 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 may include one or more arms, each arm including one or more end effectors at the end of the arm. The end effectors may be configured to handle specific objects such as wafers. In some embodiments, the transfer chamber robot 112 is a Selective Compliance Assembly Robot Arm (SCARA) robot, such as a 2-link SCARA robot, a 3-link SCARA robot, a 4-link SCARA robot, etc.

[0019] The load lock 120 can also be coupled to the housing 108 and the transfer chamber 110. The load lock 120 can be configured to interface and couple to the transfer chamber 110 on one side and to the factory interface 106 on the other side. In some embodiments, the load lock 120 can have an environmentally controlled atmosphere that changes from a vacuum environment (where the substrates go to and are transferred from the transfer chamber 110) to an atmospheric pressure inert gas environment (where the substrates go to and are transferred from the factory interface 106) or close to an atmospheric pressure inert gas environment. 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 arranged at different vertical levels (e.g., up and down). In some embodiments, the pair of upper internal chambers are configured to receive processed substrates from the transfer chamber 110 for removal from the process tool 104, while the pair of lower internal chambers are 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 (e.g., etching or pre-cleaning) on ​​one or more substrates 102 received therein.

[0020] The factory interface 106 can be any suitable enclosure, such as an equipment front-end module (EFEM). The factory interface 106 can be configured to receive substrates 102 from a substrate carrier 122 (e.g., a front-opening unified pod (FOUP)) docked to various load ports 124 of the factory interface 106. A factory interface robot 126 (shown by a dotted line) can be configured to transfer the substrates 102 between the substrate carrier 122 (also called a container) and the load lock 120. In other and / or similar embodiments, the factory interface 106 can be configured to receive replacement parts from a replacement parts storage container 123. The factory interface robot 126 can 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 end effectors at the ends of each robot arm. The end effector can be configured to pick up and handle specific objects, such as wafers. Alternatively, or in addition, the end effector can be configured to handle objects such as process kit rings.

[0021] Any conventional robot type can be used with the factory interface robot 126. Transfers can be performed in any order or direction. In some embodiments, the factory interface 106 can be maintained in a slightly positive pressure non-reactive gas environment (e.g., nitrogen is used as the non-reactive gas).

[0022] The processing system 100 may include an integrated measurement and / or imaging system 101, which may be, for example, a reflectance measurement (IR) system. Reflectance measurement 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. In dielectric films, these intensity changes can be used to determine the thickness of the film. In addition, reflectance measurements can be used to detect CD, CD bias, and other physical parameters related to the 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 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 standalone system 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 its components can perform analytical measurements (e.g., thickness measurements) that can provide profiles across the surface of the substrate, such as thickness uniformity profiles, particle number profiles, CD profiles, CD uniformity profiles, optical constant profiles, material property profiles, etc. The measurement and / or imaging system 101 can provide feedback to the 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 count, material properties, surface roughness, etc., across the entire substrate after it has been processed in the chamber. Such measurements can be used to monitor process drift, out-of-spec film thickness, out-of-spec CD, CD bias, etc., for etching, deposition, and / or other processes. The measurement results can be used to quickly correct or adjust the process parameters of one or more process strategies performed in one or more process chambers to account for the determined process drift. In addition, the measurement results can be used to determine when maintenance should be performed on the 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 it may be a standalone reflectance measurement system, as described above, or it may be located elsewhere within the processing system 100, or it may be attached to the processing system 100.

[0024] The factory interface robot 126 can place a substrate on a substrate transfer lift (e.g., a lift pin) of the measurement and / or imaging system 101. In one 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 or a magnetic chuck. The measurement and / or imaging system 101 may include various covers and ventilation systems to maintain the cleaning substrate and environment.

[0025] Within the measurement and / or imaging system 101, the substrate holder can be translated by a linear actuator so that the edge face of the substrate is centered below the optical camera. The substrate and substrate holder can then be rotated by a rotary motor (actuator), allowing the optical camera or a first sensor (e.g., an IR sensor, a visible light sensor, etc.) to capture an image or measurement of the edge face of the substrate. The motion of the substrate holder around the axis of rotation may be called θ motion, the motion of the substrate holder along a linear axis may be called r motion, and a combination of the motion of the substrate holder around the axis of rotation and 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 will be described in more detail below. Moving the substrate holder in both linear (e.g., radial) and rotational motions minimizes the overall size of the measurement and / or imaging system 101 while still allowing the capture of measurements along the entire surface of the substrate. For example, in one embodiment, the measurement and / or imaging system 101 may have a width of approximately 16"–17" and a length of approximately 23"–24" and a height of approximately 25"–26". In some embodiments, the measurement and / or imaging system 101 can be realized using two linear actuators and without a rotary actuator (where the motion of such a system is called XY motion), however this may result in a measurement and / or imaging system with a larger footprint than the measurement and / or imaging system 101. In some embodiments, the measurement and / or imaging system 101 may have the capability to measure the film thickness, CD, CD bias, etc., of approximately 100 or more substrates per hour, thereby significantly improving throughput compared to conventional optical measurement systems that measure the entire surface of a substrate. For each substrate, hundreds to thousands (e.g., 3000) points on each substrate can be measured. The measurements can be used to determine the uniformity profile of the substrate.Therefore, in the embodiment, the measurement and / or imaging system 101 may be capable of determining the uniformity profile of the substrate within a duration between 20 and 50 seconds.

[0026] Using sensors (e.g., a reflectometer) of the measurement and / or imaging system 101, such as a second sensor, one or more target positions on the substrate can be measured while the substrate is moved by a linear actuator and a rotary motor. The one or more target positions may be determined based on an algorithm or set of instructions that specify the location of the target positions and the number of target positions to be measured. In some examples, the substrate may not be centered on the substrate holder. In such examples, the processing logic can determine a coordinate transformation to switch between a coordinate system centered on the substrate holder and a coordinate system centered on the substrate. An appropriate transformation can be applied to each position on the substrate to be measured. Thus, as the substrate holder (and therefore the substrate mounted on the substrate holder) rotates, the substrate holder (and therefore the substrate) also moves linearly according to the transformation, and as a result, the correct points on the substrate are measured.

[0027] During the setup of the measurement and / or imaging system 101, the linear actuator and rotary motor can be calibrated to determine the position of the reflectometer relative to the motion axis of the linear actuator and / or rotary motor. In addition, the substrate holder can have one integrated reference target (or a number of integrated reference targets) which can be measured by a second sensor (e.g., a reflectometer) and / or captured by a first sensor (e.g., a camera) to obtain a reference measurement / image. The integrated reference target can be positioned on the substrate holder and / or a stage supporting the substrate holder in a position not covered by the substrate, and its position can be rotated and / or otherwise changed 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 a number of reference targets, which may be made of different materials. For example, one reference target may include bare silicon, and another may include silicon with a silicon dioxide layer of known thickness. The sensor can periodically measure the thickness of the integrated reference target and compare the measured thickness with 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 can determine that the sensor is producing an inaccurate measurement. In some embodiments, a linear offset can be determined based on a determined difference between the measured thickness and the known thickness of the integrated reference target. Alternatively, a nonlinear offset may be determined. A linear or nonlinear offset can 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 and the known thickness of the reference target exceeds a difference threshold. If this difference is below the difference threshold, adjustments can be applied as described above. If this difference is greater than or equal to the difference threshold, the measurement and / or imaging system 101 can be scheduled for maintenance. The measurement and / or imaging system 101 may perform calibration of a second sensor (e.g., a reflectometer) during, in the course of, or both of the measurement in order to determine an appropriate offset to apply to the measurement.

[0029] In one embodiment, an integrated reference target can induce a spectral reflection toward a second sensor (e.g., a reflectometer) as it scans across the reference target during stage setup. Processing logic can determine where the second sensor is located relative to the target based on the position where the spectral reflection is captured (e.g., the (r,θ) coordinate). A similar process can be determined for a first sensor (e.g., a camera). This can provide two coordinate systems, both referenced by the target. Thus, the relationship between the first sensor coordinate system and the second sensor coordinate system can be determined via the reference target.

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

[0031] The processing system 100 may further include a system controller 128. The system controller 128 may be and / or include a computing device such as a personal computer, server computer, programmable logic controller (PLC), or microcontroller. The system controller 128 may include one or more processing devices that can be general-purpose processing devices such as a microprocessor or central processing unit. 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 that implements other instruction sets or combinations of instruction sets. The processing device may also be one or more dedicated processing devices such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or a network processor. The system controller 128 may include data storage devices (e.g., one or more disk drives and / or solid-state drives), main memory, static memory, a network interface, and / or other components. The system controller 128 can execute instructions to perform any one or more of the methods and / or embodiments described herein. Instructions can be stored in a computer-readable storage medium, which may include main memory, static memory, secondary storage, and / or processing devices (during instruction execution). In an embodiment, the system controller performs the method of Figure 5 by executing instructions on the system controller 128. The system controller 128 can also be configured to allow input and display of data, operation commands, etc., by a human operator.

[0032] Figure 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 can correspond to the measurement and / or imaging system 101 in Figure 1 in an embodiment. The rotary actuator 202 can be a motor, a rotary actuator (e.g., an electric rotary actuator), etc. The linear actuator 204 can be an electric linear actuator that can 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, electrostatic chuck, magnetic chuck, mechanical chuck (e.g., 4-jaw chuck, 3-jaw chuck, edge / ring clamp chuck, etc.) or other type of chuck. The substrate holder 206 can also be a plate or other surface having a substrate shape pocket and / or a set of pins or other features (e.g., three pins) surrounding the substrate to prevent the substrate from shifting relative to the substrate holder 206 during movement of the substrate holder 206, or may include such a feature. The substrate holder 206 can secure the substrate 214 (e.g., a wafer). In one embodiment, the substrate holder 206 includes an edge clamp that clamps the substrate from the edge face. In one 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, mechanical chuck, magnetic chuck, etc.

[0034] The rotary actuator 202 can rotate the substrate holder 206 around the first axis 203. The rotary actuator 202 can be controlled by a servo controller and / or servo motor, thereby enabling precise control of the rotational position, speed, and / or acceleration of the rotary actuator and, therefore, the substrate holder 206. The substrate holder 206 can have a mass between 1.0 kg and 2.0 kg, thereby achieving a rotational speed of 6000 deg / sec. 2 and 14000 deg / sec 2 Rotational acceleration between the two axes becomes possible. The linear actuator 204 can move the substrate holder 206 linearly along the second axis 205. The linear actuator 204 can be controlled by a servo controller and / or a servo motor 222, thereby enabling precise control of the linear position, velocity, and acceleration of the linear actuator 204 and therefore the substrate holder 206.

[0035] 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. Camera 208 can be an optical camera, an infrared camera, or another suitable type of camera. Alternatively, camera 208 may be replaced with another type of sensor. Sensor 210 can also be positioned above the substrate holder 206 and can measure at least one target position on the substrate at a time. Depending on the sensor type of sensor 210, the measurement can be a reflectance measurement, optical property measurement, particle count measurement, CD measurement, roughness measurement, surface geometric shape measurement, and / or other types of measurement. Camera 208 (or other sensor) and 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 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 external dimensions of the measuring and / or imaging system to be more compact compared to a measuring and / or imaging system that moves the chuck in a rectangular (XY) motion system (for example, having a first dimension between 20 and 28 inches, a second dimension between 22 and 28 inches, and a third dimension between 14 and 20 inches, with the first, second, and third dimensions being perpendicular to each other). In addition, by moving the chuck using the r-θ motion system, substrates can be processed at a rate of up to 100 or more substrates per hour (for example, uniformity profiles can be determined). In other words, the processing device 212 may be able to generate thickness measurements of the entire substrate 214 and, in embodiments, determine the uniformity profile of the substrate 214 within a duration between 20 and 50 seconds.

[0037] In some embodiments, the processing device 212 can determine, based on one or more images or other measurements of the substrate 214 generated by the camera 208 or other sensors, whether the substrate 214 is 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). The substrate 214 may not be centered on the substrate holder 206 when it is first 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 a second axis 205 so that the substrate holder 206 is positioned on the transfer station 218. The transfer station 218 can be positioned on a lift mechanism 216 (or a set of lift pins), which can move the transfer station 218 up and down vertically (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 can be moved in a second direction along the second axis 205 until the sensor 210 detects that the end face of the substrate 214 is at the target position.

[0038] The substrate holder 206 can be rotated 360 degrees, and images or other measurements (optionally referred to as edge images or periphery images) can be generated during the rotation of the substrate holder 206. One or more of the measurements and / or images can be obtained using the substrate holder at different θ values, and the location where the edge is detected can vary. The detected change in the edge can indicate that the substrate (which may be a circular substrate) is off-center. In addition, the determined change in the detected edge can be used to calculate the amount of offset. Since the camera, sensor, and stage are synchronized, periphery image scanning can be achieved in 3 to 6 seconds without stopping the stage, in the embodiment.

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

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

[0041] In one embodiment, the parameter (r,θ) determines the offset of the substrate relative to the stage. Using these parameters, the motion system can generate 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 of the substrate in space while sending commands to move the motors mounted on the substrate holder 206. In one embodiment, the motion system runs real-time control software connected (e.g., via an EtherCAT network) to the motion drives of the linear and rotary actuators so that it can calculate trajectories in arbitrary space. The controller 212 can calculate the corrected trajectory and transmit the commanded position to the motion drive in real time (e.g., at a rate of 1 kHz).

[0042] In some embodiments, additional calibration is performed to determine one or more of the following: 1. The origin of the camera stage / chuck, defined by the rotation center of the stage / chuck. 2. Poor alignment of the camera with the rotation center of the stage / chuck. 3. Camera mounting error, 4. Boresight correction between the sensor and the camera, and 5. For example, one or more additional calibrations to address small inclines (tips), large inclines (tilts), and / or sway.

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

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

[0045] In some embodiments, the substrate includes notches and / or flat areas. The locations of the flat areas and / or notches can be determined using images from camera 208 and / or sensor measurements from sensors (e.g., edge sensors such as IR or laser sensors). XY positions can be determined on the substrate based on the determined locations of the flat areas or notches. XY positions can be converted to r-θ positions, and r-θ positions can be converted to XY positions.

[0046] In some embodiments, when the substrate holder 206 is rotated by the rotary actuator 202 to measure the target position, an offset occurs between the field of view of the sensor 210 and the target position on the substrate 214 due to the substrate not being centered on the substrate holder. In this case, the linear actuator 204 can be used to 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 have been taken for all target points on the substrate, the processing device 212 can determine a uniformity profile across the surface of the substrate 214 based on the measurements.

[0047] The motion systems of the measurement and / or imaging system 200 (e.g., linear actuator 204 and rotary actuator 206) can be synchronized with the camera 208 and / or sensor 210 via digital trigger signals transmitted over an EtherCAT network. The drivers of the motion systems may be connected to a real-time control server via an EtherCAT network, for example. The network allows, in embodiments, the 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 allows the processing logic to transform the 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 on which the substrate holder 206 rotates) and the center of the substrate 214, and one or more coordinate transformations are applied during the rotation of the substrate holder 206 to correct the offset.

[0049] In some embodiments, to identify the center of the substrate 214, the sensor 210 identifies several points on the edge face of the substrate 214 (either via measurements or via images from the camera 208), and these points can be fitted to a circle using one or more techniques, such as a regression analysis fitting technique. In other words, the edge face position in each image can be fed into an optimized circle fitting algorithm to determine the circle. The notch angle can be found by combining the angle of the image with the most likely notch candidate with 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. Substrate alignment targets may include substrate marks, patterns, lines, edges, corners, and / or similar features. 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 with the training images to determine their translation. Embodiments may determine the translation with sub-pixel accuracy using an image registration algorithm employing techniques such as the Fast Fourier Transform (FFT) method or a Convolutional Neural Network (CNN) deep learning method. The image registration translation between the substrate alignment targets and the training images forms the position measurements of the alignment targets. A number of position measurements of substrate alignment targets can be used to fit coordinate transformations, thereby providing further accuracy. The measurements can be combined using a fitting algorithm (e.g., least squares) to refine estimates of the substrate center and orientation.

[0051] In some embodiments, the center and orientation of the substrate can be identified using end faces and notches. In some embodiments, the center and orientation of the substrate can be identified using a substrate alignment target. In some embodiments, the center and orientation of the substrate can be identified in two steps: step 1, finding the end faces and notches, and step 2, finding the substrate alignment target.

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

[0053] In one embodiment, one or more integrated reference targets 215 are mounted on one end of a substrate holder 206. In one embodiment, one or more integrated reference targets 215 include a silicon reference target and a silicon dioxide reference target. One or more integrated reference targets 215 can be used to maintain the 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 number, 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, it can be determined that the sensor 210 is not properly calibrated. In one embodiment, the difference between the measured thickness and the known thickness is determined. This difference can then be compared to one or more difference thresholds. If this difference is below the difference threshold, an adjustment factor can be determined and added to future measurements of sensor 210. If this difference exceeds the difference threshold, maintenance of sensor 210 can be scheduled. Similar calculations can be performed for other measured and known properties of the film on the reference target.

[0054] In one embodiment, one or more integrated reference targets 215 include a calibration target in 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 induce 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 below the sensor 210), and / or can induce 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 position of the sensor 210 relative to the calibration target can be determined using the R and θ values ​​of the substrate holder 206 related to the sensor measurement of the sensor 210 that yields the spectral reflection. Similarly, the position of the camera 208 relative to the calibration target can be determined using the R and θ values ​​of the substrate holder 206 related to the camera measurements of the camera 208, which yield an image where the calibration target is centered. The relative positions of the camera 208 and the sensor 210 can be determined by using together the r-θ value related to the spectral reflectance of the sensor 210 and the r-θ value related to the centered calibration target image of the camera 208. In one embodiment, the controller 212 positions the substrate holder by rotating and linearly moving it so that the target is positioned and centered in the image of the camera 208. The substrate holder 206 is then translated so that the target is directly below the head of the sensor 210. This can be achieved by moving the substrate holder until the reading of the sensor 210 has its maximum value. The distance between the position of the substrate holder where the target was centered in the image produced by the camera and the position of the substrate holder where the target was directly below 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). One or more diffusers 270 can direct a gas (e.g., clean-drying air, nitrogen, and / or another gas) toward the substrate 214 to prevent particles from moving onto the substrate 214. The diffusers 270 can direct the gas toward the substrate at any angle, for example, a 90-degree angle, an obtuse angle, and / or an acute angle. In some embodiments, the IR system 200 further includes a discharge unit 272 for discharging the gas (e.g., the gas output by the diffusers 270) and / or particles moved by the gas from the enclosure of the IR system 200. In embodiments, the discharge unit 272 can pull particles away from the substrate 214.

[0056] Figure 2B is a top-down schematic diagram of the substrate holder 206 of the IR system 200 of Figure 2A according to one embodiment. In one embodiment, the substrate holder 206 can rotate clockwise and / or counterclockwise about an axis located 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 a plurality of integrated stoppers (e.g., wafer stoppers) 250 to prevent the substrate 214 from moving out of the substrate holder 206 in the event of a loss of retaining force, such as vacuum pressure or electrostatic force. In one embodiment, the substrate holder 206 includes three integrated stoppers arranged around the substrate holder 206. The stoppers 250 can be adjustable, allowing adjustment of the distance of each stopper from the center of the substrate holder 206. In one embodiment, the stoppers 250 are evenly distributed around the outer circumference of the substrate holder 206.

[0057] Figure 3 is a top-down schematic diagram showing the rotational coordinates of the 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 in Figure 1 and / or the IR system 200 in Figure 2. The substrate holder may be the same as or similar to the substrate holder 206 in Figures 2A and 2B. The substrate holder can be positioned on a stage, and the stage can be rotated about an axis of rotation (such as the first axis 203) to rotate the substrate holder in the same way. In addition, the stage can be moved about a linear axis (such as the second axis 205) to move the substrate holder in the same way. These motions allow the substrate holder, and by extension the substrate fixed to the substrate holder, to move according to r-θ motion. In other words, rotational motion allows the substrate holder to rotate by an azimuth angle, while linear motion allows the substrate holder to move radially, thereby enabling movement of the substrate holder over its entire range and measurement of the entire surface of the substrate.

[0058] The motion system driving the stage can be synchronized with a 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 suitable network). The network can enable the transmission of the trigger signal for controlling the motion system at a specific rate (e.g., 1 kilohertz (kHz)).

[0059] The substrate holder can be positioned at an initial location defined by radial and angular coordinates (e.g., polar coordinates) (r0, θ0), which can also be expressed equivalently in Cartesian coordinates (x0, y0). The camera and reflectometer can be positioned above the substrate holder (along the axis of rotation), with the radial distance being R, respectively. c and R rTherefore, by rotating the substrate holder from the initial angle θ0 and linearly moving the substrate holder from the initial linear coordinate r0, the entire surface of the substrate holder, and thus the substrate fixed by the substrate holder, can be scanned.

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

[0061] In one embodiment, the first coordinate system can be defined such that the origin is at the center of the substrate holder 406, i.e., (p0) c = [0,0,0], where the first value corresponds to the first axis (e.g., the x-axis), the second value corresponds to the second axis perpendicular to the first axis (e.g., the y-axis), and the third value corresponds to the angle measured from the first axis towards the second axis. In addition, the second coordinate system can be defined in the same manner as the first coordinate system, except that the origin is at the center of the substrate 414, i.e., (p0) w = [0,0,0]. If there is an error in the placement of the substrate 414, the center of the substrate 214 can be represented in the first coordinate system as [x offset , y offset , θ offset = [x0,y0,θ0].

[0062] Any point p1 = [x1,y1] c can be represented as (p1)c = [x1,y1]c in the first coordinate system and as (p1) w = [x1,y1] w in the second coordinate system. The coordinate transformation between the first coordinate system and the second coordinate system can be

Equation

[0063]

number

number

[0064]

number

number

[0065] In addition, point p1 is a general transformation [x,y]=[rcosθ,rsinθ] and an inverse transformation

number

[0066] Figures 5A to 5D are a series of graphs illustrating an example of how the placement offset of a substrate on a substrate holder can be canceled out by a circular trajectory in one embodiment. Figure 5A shows a substrate holder 504 and a circular trajectory 502 that can be scanned on the substrate by a fixed sensor directed from above toward the substrate holder 504. The substrate is perfectly positioned on the substrate holder (x offset ,y offset ,θ offset If all values ​​are zero, the target scanning trajectory can be completed by a single rotation of the substrate holder after a linear motion to place the sensor at the correct radius on the substrate. Figure 5B shows the same substrate holder. 504For illustrative purposes, the substrate 506 is positioned on the same substrate holder 504 with a greatly exaggerated offset. The circular trajectory 508 still indicates 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 Figure 5A, instead of scanning the target trajectory 512 shown in Figure 5C (r=75mm for all θ) on the substrate, the substrate holder 504 would scan the trajectory indicated by trajectory 510 shown in Figure 5C, which is substantially deviated from the target trajectory. Figure 5D shows how the positional offset can be canceled by moving the linear axis of the stage in conjunction with the rotation axis. The linear axis is moved in such a way that the sensor can see what is represented by trajectory 516 on the substrate holder in Figure 5D, thereby allowing the sensor to capture the target circular trajectory on the substrate (as shown in Figures 5A-5B). This is because the trajectory 516 is offset from the center of the substrate holder 506 by an amount equal to the positional offset of the substrate on the substrate holder 506 shown in Figure 5B, but in the opposite direction. When the substrate holder 206 or 406 is rotated by a rotary motor around a first axis, a given point on the substrate holder follows a circular trajectory (assuming there is no simultaneous linear motion). A motion system (e.g., as described with respect to Figure 3) can calculate the trajectory of any point in the second coordinate system of the substrate while sending commands to move motors and / or actuators mounted on a stage. In some embodiments, the motion system can also calculate trajectories in arbitrary space by running real-time control software (e.g., algorithms) that can connect to the motion driver via a network (e.g., an EtherCAT network) and send control signals.

[0067] As previously explained, 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 suitable network). The network can enable the transmission of trigger signals for controlling the motion system at a specific rate (e.g., 1 kilohertz (kHz)). Motion software can calculate the corrected trajectory and transmit that signal to the motion driver in real time.

[0068] Additional calibration may be performed with respect to the center of the substrate holder (e.g., the axis of rotation) (equivalently the axis of rotation of the stage) and the camera and / or reflectance measuring sensor. For example, in some embodiments, the camera may be misaligned with respect to the axis of rotation of the substrate holder, and can benefit from the correction. In some embodiments, there may be errors in the mounting of the camera, which also require correction. In any case, conversions can be applied to the camera measurements and / or sensor measurements to correct any misalignment. These calibrations can ensure that the sensor reflectance meter measurements are accurate with respect to the coordinate system of the substrate (a second coordinate system). The sensor and camera optical head are adjusted to be positioned approximately at the center of the axis of rotation, but the alignment algorithm described above can provide additional precision in the measurement position beyond the mechanical tolerance for adjustment.

[0069] Additional calibration may be performed for boresight correction between the reflectance sensor and the camera. These calibrations can calculate the origin of the reflectance sensor stage and apply appropriate transformations to the measurements taken by the camera. These calibrations ensure that the measurements taken by the reflectance sensor are accurate and aligned with the coordinates of the substrate and / or substrate holder (depending, for example, 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 can add further precision to the measurements.

[0070] Figure 6 is a flowchart of a method 600 that performs a coordinate transformation to correct substrate and substrate holder offsets according to one embodiment. In some embodiments, method 600 can be performed by processing logic executed by a processor of a computing device. In some embodiments, method 600 can 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 r-θ motion. To move the stage, the motor and / or actuator can be rotated by discrete quantities (counts such as encoder counts) (e.g., the motor can be rotated in steps such as 1 / 10, 1 / 5, 1 / 3 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 the 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 (a rotation of a specific number of degrees / radians, a physical linear translation of a specific distance). A coordinate transformation can be applied to compensate for the center of the substrate being offset from the center of the substrate holder. In addition, the mutual offsets between the stage, the reflectance sensor, and the camera are calibrated and centered relative to a selected point (block 606). The processing logic can determine a target trajectory, including the position and velocity of the substrate, so that the camera and reflectance sensor can obtain images and measurements of the substrate, respectively (block 608). The processing logic can apply an inverse coordinate transformation to the target trajectory to determine the rotation and linear motion of the stage that should be applied to reproduce the desired trajectory. The inverse coordinate transformation can represent the actual motion of the stage (block 610). The processing logic can convert the actual motion back into a motor count (block 612). The motor count can be sent to the drive system. Method 600 then ends.

[0072] Method 600 can be repeated for each target position on the substrate to be measured.

[0073] Figure 7 is a block diagram of a method 700 that performs a coordinate transformation to correct substrate and substrate holder offsets according to one embodiment. In some embodiments, method 700 can be performed by processing logic executed by a processor of a computing device. In some embodiments, method 700 can be performed by a processing device. In some embodiments, method 700 can be performed by a processing system 100. In some embodiments, method 700 can be performed by a measurement and / or imaging system 200 of Figure 2A.

[0074] Method 700 begins with processing logic that receives a substrate on a substrate holder of an optical measuring device (block 702). In some embodiments, the optical measuring device may be part of the processing system 100 in Figure 1. In some embodiments, the optical measuring device may be all or part of the IR system 200 in Figure 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 may be specified by [x0, y0, θ0]. The processing logic measures one or more target positions on the substrate (block 706). The processing logic may measure one or more target positions using sensors. Based on the measurements of one or more target positions, the processing logic determines a uniformity profile across the surface of the substrate.

[0075] In some embodiments, to measure one of one or more target positions 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 sensor's field of view and the target position because the substrate is not centered on the substrate holder. The processing logic can compensate for this offset by moving the substrate holder linearly 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 on which the substrate holder rotates. The processing logic applies at least one of the one or more coordinate transformations to the substrate holder during rotation to compensate for the offset.

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

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

[0079] Figure 8 shows 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 the 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 in the processing device, and / or other software and / or firmware running in the processing device.

[0080] The 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 following tasks: determination, prediction, and correction of tasks related to process drift determination (e.g., determination of parameter corrections to account for process drift occurrence), film thickness determination, and / or substrate offset determination (also called substrate center determination), CD determination, CD bias determination, etc. The 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 may receive target reflectance measurement data 810 (e.g., of a reference target integrated on the substrate holder 206 in Figures 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 certain number and configuration of machine learning models are described and illustrated. However, it should be understood that the number and types of machine learning models used, and the configurations of such machine learning models, may be modified to achieve the same or similar final results. Therefore, the configurations of machine learning models described and illustrated are merely examples and should not be interpreted as limiting.

[0082] In embodiments, 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 or a subset of the tasks. In addition, or alternatively, different machine learning models may be trained to perform different combinations of tasks. In one example, one or more machine learning models may be trained, and the trained machine learning (ML) model is a single shared neural network having a number of shared layers and a number of higher-level distinct output layers, each outputting a different prediction, classification, identification, etc. The tasks that one or more trained machine learning models may be trained to perform are as follows: a. Process Drift Determination - As the substrate is processed in the processing chamber, several variables may change or drift as a function of time. For example, temperature fluctuations, deposition / etching rate fluctuations, etc., may occur, and these can affect the uniformity of film thickness (e.g., uniformity profile) across the surface substrate being processed. In addition, chemical accumulation, which may occur over longer time scales, can affect the uniformity profile across the substrate surface. The model will receive target reflectance measurement data and determine the parameters that should be adjusted for sensor calibration for reflectance measurements. b. Film Thickness Determination - In some embodiments, it is desirable that the film thickness across the substrate surface be as uniform as possible. To efficiently measure the uniformity profile of the substrate, the sensor measures several target locations on the substrate. The number and location of these locations must 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 location of one or more target locations are sufficient to give an accurate representation of the uniformity profile. c. Measurement Determination - The sensor measures several target positions on the substrate. The number and location of these positions can be measured to determine optical constants, roughness, film thickness, material properties, particles, etc. A machine learning model can receive measurements of one or more target positions and determine if the number and location of one or more target positions 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 to a trained ML model, which outputs a representation 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 the conversion between the r,θ coordinate system of the substrate center and the r,θ coordinate system of the substrate holder center. e. Determining the substrate offset of a substrate in a process chamber - A substrate map (e.g., a wafer map) can be generated using sensor-inferred data such as film thickness, CD, etc., at numerous target locations on the substrate. Data from the substrate map is input to a trained ML model, which can output an estimate of the offset of the substrate's placement in the process chamber where the substrate was previously processed.

[0083] One type of machine learning model that can be used to perform some or all of the tasks described above is an artificial neural network, such as a deep neural network. An artificial neural network generally contains feature representation components that have classifier or regression layers that map features to a desired output space. A convolutional neural network (CNN), for example, hosts a large number of layers of convolutional filters. Pooling is performed, and nonlinearity is dealt with in the lower layers, and on top of that, a multilayer perceptron is generally added to map the top layer features extracted by the convolutional layers to a decision (e.g., a classification output). Deep learning is a class of machine learning algorithms that use a multilayer cascade 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 supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) ways. A deep neural network contains a hierarchy of layers, where different layers learn different levels of representation corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract composite representation. In particular, the deep learning process can learn on its own which features are best placed at which level. "Deep" in "deep learning" refers to the number of layers to which the data is transformed. More precisely, a deep learning system has a de facto credit assignment path (CAP) depth. A CAP is a chain of transformations from input to output. A CAP describes the latent causal relationship between the input and output. In a feedforward neural network, the CAP depth can be the network depth, which can be the number of hidden layers plus 1. In a recurrent neural network where a signal can propagate through layers two or more times, the CAP depth is potentially unlimited.

[0084] Training a neural network can be achieved using supervised learning methods, which involve feeding the network a training dataset consisting of labeled inputs, observing its outputs, defining the error (by measuring the difference between the output and the labeled value), and adjusting the network's weights across all layers and nodes to minimize the error using techniques such as deep gradient descent and backpropagation. In many applications, repeating this process across many labeled inputs in the training dataset results in a network that can produce the correct output when presented with an input different from the inputs present in the training dataset.

[0085] In the model training workflow 805, the training dataset should be formed using training datasets containing hundreds, thousands, tens of thousands, hundreds of thousands, or more target reflectance measurement data 810 (e.g., determination of uniformity profiles at different time points using a different number and locations of target positions 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 a different number and / or locations of target positions on the substrate), and / or image data 814 of the substrate on a substrate holder (optionally accompanied by position coordinates such as linear and rotational positions of the substrate holder). The data may include, for example, uniformity profiles determined using a given number of measurements, each associated with a specific target position. This data can be processed to generate one or more training datasets 836 for training one or more machine learning models. The machine learning model is trained to enable, for example, automatic calibration of the sensor during and / or while the substrate is being measured, and / or automatic compensation for process variations that may affect the measured uniformity profile of the substrate (such as changes in conditions within the processing chamber, offset substrate placement from the center of the substrate holder, etc.), and / or determination of the offset of the substrate center relative to the center of the substrate holder.

[0086] In one embodiment, generating one or more training datasets 836 involves collecting one or more target substrate reflectivity measurement 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 dataset 836 may include data labels indicating reflectivity measurement values ​​of a reference target over time. To train an ML model to determine a central offset (e.g., determining 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, process policies and / or process results may be represented as vectors, and process rates may be represented as one or more matrices.

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

[0088] A machine learning model processes inputs and produces outputs. An artificial neural network includes an input layer consisting of data point values. The next layer is called a hidden layer, and each node in the hidden layer receives one or more of the input values. Each node contains parameters (e.g., weights) to apply to the input values. Thus, each node essentially inputs the input values ​​into a multivariable function (e.g., a nonlinear mathematical transformation) to produce output values. The next layer can be another hidden layer or an output layer. In either case, the nodes in the next layer receive output values ​​from the nodes of the previous layer, each node applies weights to those values, and then produces its own output values. This can be done in 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] Therefore, the output may include one or more predictions or inferences. For example, the output prediction or inference may include one or more corrections to the substrate reflectivity measurement data (e.g., corrections based on sensor calibration). The processing logic may process the substrate using the updated policy (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 value measured across the wafer surface is within the target threshold window). The processing logic determines an error (i.e., classification error) based on the difference between the updated thickness profile and the target thickness profile. The processing logic adjusts the 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 this error, the artificial neural network adjusts one or more parameters (weights for one or more inputs of the node) for one or more of its nodes. Parameters may be updated using backpropagation, 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 consists of numerous layers of "neurons," each layer receiving values ​​as input from the 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. Therefore, tuning parameters can involve 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 have been optimized, model validation can be performed to determine whether the model has 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 the stopping criteria have been met. The stopping criteria can be a target level of accuracy, a target number of processed images from the training dataset, a target change in parameters relative to one or more previous data points, a combination 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, training can be completed. Once the machine learning model is trained, the model can be tested using a reserved portion of the training dataset.

[0091] For example, a machine learning model (e.g., a process drift decisioner 867) is trained to determine changes in process parameters over time. A similar process can be performed to train a machine learning model, thereby enabling it to perform other tasks described above. A large set of (e.g., hundreds to millions) target reflectance measurement data can be collected, and sensor calibration data 869 can be determined.

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

[0093] In the model application workflow 817, according to one embodiment, input data 862 may be input to a process drift decisioner 867 which may include a trained neural network. Based on the input data 862, the process drift decisioner 867 outputs information indicating 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 can output the center of the substrate. In one embodiment, the offset determiner 865 outputs a substrate offset 870 indicating the offset between the center of the substrate holder and the center of the substrate. Based on the substrate offset 870, the controller can determine coordinate transformations for converting between the coordinate system of the substrate holder center and the coordinate system of the substrate center. These transformations can be used to determine how to move the linear actuator during rotation of the substrate holder in order to ensure that the point of the target radius remains below the sensor during measurement of the point at the radius on the substrate.

[0095] According to one embodiment, input data 862 may be input to a measurement decisioner 864 (which may be a film thickness decisioner, a CD deviation decisioner, a specific count decisioner, an optical constant decisioner, etc.) that can include a trained neural network. In some embodiments, sensor calibration data 869 may be input to the measurement decisioner 864. Based on the input data 862, the measurement decisioner 864 provides measurement information 866It can generate one or more outputs related to limit dimensions, film thickness, optical constants, particle number, etc., also known as metric values. In one embodiment, the measurement information 866 output output by the measurement decisioner 864 includes a map of measurements across the substrate. In one embodiment, the measurement decisioner 864 The measurement information 866 output by the system includes a map of the measured values ​​(e.g., deviation of CD from the standard value on the substrate surface, CD bias value, film thickness, etc.).

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

[0097] Figure 9 shows a block diagram of an exemplary computing device capable of process drift and film thickness determination, operating according to one or more embodiments of the present disclosure. In various exemplary examples, various components of the computing device 900 may represent various components of the computing device, controller, and / or control panel (e.g., similar elements described in relation to Figures 1 to 3).

[0098] The exemplary computing device 900 may be connected to other computer devices in a local area network (LAN), intranet, extranet, and / or the internet. The computing device 900 may operate with server capabilities 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, a switch or bridge, or any device capable of executing (sequentially or otherwise) a set of instructions that specify the actions to be performed by such device. Furthermore, although only a single exemplary computing device is shown, the term “computer” should also be understood to include any set of computers that individually or collectively execute one or more sets of instructions to perform any one or more of the methods discussed herein.

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

[0100] The processing device 902 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, etc. More specifically, the processing device 902 can be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIM) microprocessor, a processor that implements other instruction sets, or a processor that implements a combination of instruction sets. The processing device 902 can also be one or more dedicated processing devices, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. According to one or more aspects of this disclosure, the processing device 902 can be configured to execute instructions that implement methods 600 to 800 shown in Figures 6 to 8.

[0101] The exemplary computing device 900 may further include a network interface device 908 that can be communicatively coupled to a network 920. The exemplary computing device 900 may further include a video display 910 (e.g., a liquid crystal display (LCD), a touchscreen, or a cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard), a cursor control device 914 (e.g., a mouse), and an acoustic 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-temporary machine-readable storage medium) 928 in 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 the execution of methods 600 to 800 shown in Figures 6 to 8.

[0103] The executable instruction 922 may also reside entirely or at least partially in the main memory 904 and / or the processing device 902 while it is being executed by the exemplary computing device 900, and the main memory 904 and the processing device 902 also constitute computer-readable storage media. The executable instruction 922 may also be transmitted or received over a network via the network interface device 908.

[0104] Although the computer-readable storage medium 928 is shown as a single medium in Figure 9, the term “computer-readable storage medium” should be interpreted to include a single medium or a number of mediums (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of operational instructions. The term “computer-readable storage medium” should also be interpreted to include any medium capable of storing or encoding a set of instructions for machine execution that causes a machine to execute any one or more of the methods described herein. Accordingly, the term “computer-readable storage medium” should be interpreted to include, but not limited to, solid-state memory and optical and magnetic media.

[0105] Some of the detailed explanations above are presented by algorithms and symbolic representations of operations on data bits in computer memory. These descriptions and representations of algorithms are the means used by those skilled in the data processing technique to communicate the nature of the work most efficiently to others skilled in the technique. Here, an algorithm is generally considered to be a sequence of self-consistent steps that produce a desired result. These steps require the physical manipulation of physical quantities. While not always necessary, these quantities often take the form of electrical or magnetic signals that can be stored, transferred, combined, compared, and otherwise manipulated. For reasons of common use, it is sometimes convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc.

[0106] However, it should be kept in mind that all these and similar terms are associated with appropriate physical quantities and are merely convenient labels applied to those quantities. Unless otherwise specified, as will be evident from the following discussion, discussions throughout this specification using terms such as “identify,” “determine,” “store,” “regulate,” “cause,” “receive,” “compare,” “measure,” “correct,” “apply,” “use,” “obtain,” “replace,” and “execute” are understood to refer to the operations and processes of a computer system or similar electronic computing device that manipulate and convert data represented as physical (electronic) quantities in computer system registers and memory to other data similarly represented as physical quantities in computer system memory or registers or other such information storage, transmission, or display devices.

[0107] Examples of the present disclosure also relate to apparatus for performing the methods described herein. The apparatus may be specifically constructed for a target purpose or may be a general-purpose computer system 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 are not limited to, any type of disk, including optical disks, compact disk read-only memory (CD-ROM), and magneto-optical disks, each coupled to a computer system bus, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), 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.

[0108] The methods and displays presented herein are not inherently related to any particular computer or other device. Various general-purpose systems may be used with the program in accordance with the teachings herein, or it may be convenient to construct more specialized devices for performing the steps of the method. Various structures for these systems will arise as described below. In addition, the scope of this disclosure is not limited to any particular programming language. It will be understood that various programming languages ​​can be used to carry out the teachings of this disclosure.

[0109] The foregoing description includes numerous specific details, such as examples of particular systems, components, methods, etc., in order 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 can be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in a simple block diagram format to avoid unnecessarily obscuring the present disclosure. Thus, the specific details described are merely illustrative. Certain embodiments may differ from these exemplary details but are still intended to be within the scope of the present disclosure.

[0110] Throughout this specification, any reference to “one embodiment” or “one embodiment” means that the specific features, structure, or characteristics described in relation to the embodiment are included in at least one embodiment. Therefore, occurrences of the phrase “in one embodiment” or “in one 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 inclusive “or” rather than exclusive “or.” Where the terms “about” or “approximately” are used herein, this is intended to mean that the accuracy of the presented nominal value is within ±10%.

[0111] Although the operations of the methods described herein are shown and described in a specific order, the order of operations of each method may be modified so that certain operations can be performed in reverse order, or so that certain operations can be performed at least partially concurrently with other operations. In another embodiment, the instructions or suboperations of separate operations may be in an intermittent and / or alternating manner.

[0112] It should be understood that the above description is intended to be illustrative, not restrictive. Many other embodiments will be apparent to those skilled in the art upon reading and understanding the above description. Therefore, the scope of this disclosure should be determined by reference to the appended claims and the entire scope of equivalents to which such claims are granted.

Claims

1. A circuit board holder for securing the circuit board, 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 measurement values ​​or images of a plurality of first target positions on the substrate, A second sensor for generating one or more second measurement values ​​of a second plurality of target positions on the substrate, A processing device, Based on the one or more first measured values ​​or images of the first plurality of target positions, an estimated value of the position of the substrate on the substrate holder is determined. For the purpose of measuring one of the second plurality of target positions, the first actuator rotates the substrate holder about the first axis, wherein the rotation causes an offset between the 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. In order to correct the offset, the second actuator moves the substrate holder linearly along the second axis, Based on one or more second measurements of the second plurality of target positions generated by the second sensor, a profile is determined across the surface of the substrate. A processing device for performing this task A device including a device.

2. The processing device further, Determining one or more coordinate transformations between the first center of the substrate holder corresponding to the first axis on which the substrate holder rotates and the second center of the substrate, Based on the one or more coordinate transformations, the trajectories corresponding to the r-θ motion commands for the first actuator and the second actuator are calculated, To correct the offset, apply one or more coordinate transformations during the rotation. The apparatus according to claim 1, which is for performing the following.

3. The apparatus according to claim 2, wherein the first sensor is a camera configured to capture one or more images of at least one of the end face 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 at least one of the end face of the substrate or one or more features of the substrate.

4. The processing device further, Identifying multiple points corresponding to the end face of the substrate, To fit the plurality of points on the end face to a circle The apparatus according to claim 1, which is for the purpose of

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

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

7. The processing device further, The process involves moving the substrate holder along the second axis in a first direction to position the substrate holder at the transfer station, wherein the substrate is received on the substrate holder, and the substrate holder is positioned at the transfer station. The substrate holder is moved in a second direction along the second axis so that the end face of the substrate is detected by the first sensor. The apparatus according to claim 1, which is for performing the following.

8. The apparatus according to claim 1, wherein the second sensor is a reflectance measuring sensor, and the one or more second measured values ​​include the measured values ​​of a reflectance measuring method.

9. The apparatus according to 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, and the first dimension, the second dimension, and the third dimension are perpendicular to each other.

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

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

12. A reference target attached to the substrate holder, comprising a film having known properties, It further includes, The second sensor is further for generating measurements of the reference target, The apparatus according to claim 1, wherein the processing device further comprises a second sensor for calibrating the second sensor by comparing the measured value of the reference target with the known characteristics of the reference target.

13. Reference target mounted on the aforementioned substrate holder It further includes, 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 for generating one or more fourth measurements of the reference target at a second position of the substrate holder using the second sensor. The apparatus according to claim 1, wherein the processing device is for calibrating the position of the second sensor relative to the first sensor based on the one or more third measurement values ​​and the one or more fourth measurement values.

14. One or more diffusers positioned above the substrate holder, wherein the one or more diffusers are for directing gas toward the substrate, The device has an outlet that discharges the gas to the outside from the enclosure. The apparatus according to claim 1, further comprising:

15. The apparatus according to claim 1, further comprising one or more integrated stoppers on the substrate holder for preventing the substrate from coming out of the substrate holder while the substrate holder is moving, wherein the one or more integrated stoppers include at least one of a substrate-shaped pocket, one or more guardrails, or a plurality of protrusions.

16. Transfer chamber and process chamber and A load lock connected to the transfer chamber, A factory interface connected to the load lock, wherein the factory interface includes a robotic arm, An optical measuring device connected to the factory interface, wherein the robot arm is for moving the substrate from the process chamber to the optical measuring device, and the optical measuring device is A substrate holder for fixing the aforementioned 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 measurement values ​​or images of a first plurality of target positions on the substrate, A second sensor for generating one or more second measurement values ​​of a second plurality of target positions on the substrate, and A processing device, Based on the one or more first measurement values, an estimated value of the position of the substrate on the substrate holder is determined, For measuring one target position among the second plurality of target positions, the first actuator rotates the substrate holder about the first axis, wherein the rotation causes an offset between the 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. In order to correct the offset, the second actuator moves the substrate holder linearly along the second axis, Based on one or more second measurements of the second plurality of target positions generated by the second sensor, a profile is determined across the surface of the substrate. Processing device for performing Optical measuring devices and A system that includes this.

17. The system according to claim 16, wherein the optical measuring device is mechanically isolated from the factory interface and the external environment in order to isolate the optical measuring device from vibration.

18. The processing device is Determining one or more coordinate transformations between the center of the substrate holder corresponding to the first axis on which the substrate holder rotates and the center of the substrate, Based on the one or more coordinate transformations, the trajectories corresponding to the r-θ motion commands for the first actuator and the second actuator are calculated, Applying one or more coordinate transformations during the rotation of the substrate holder around the first axis to correct the offset between the center of the substrate holder and the center of the substrate, To do further, The system according to claim 16.

19. Receiving the substrate on the substrate holder of the optical measurement device, To estimate the position of the substrate on the substrate holder, Determining one or more coordinate transformations between the first center of the substrate holder corresponding to the first axis on which the substrate holder rotates and the second center of the substrate, Measuring multiple target positions on the substrate using a sensor, wherein measuring one of the multiple target positions is: Based on the one or more coordinate transformations, the trajectories corresponding to the r-θ motion commands for the first actuator and the second actuator are calculated. Rotating the substrate holder about the first axis, wherein the rotation causes 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. The offset is corrected by moving the substrate holder linearly along the second axis. Including measuring, The processing device determines a profile across the surface of the substrate based on the measured values ​​of the multiple target positions. Methods that include...

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