Spatial recognition systems in semiconductor processing tools

Abstract

A method for sensor-assisted signal calibration of a semiconductor processing tool includes decoding multiple sensor measurements received from at least one ranging sensor of a sensor array associated with the semiconductor processing tool. The presence of an interfering object is detected within the access zone of the semiconductor processing tool based on the multiple sensor measurements. A deviation of at least one signal characteristic of the wireless signal from a pre-configured value is detected. A correlation is generated based on the deviation of at least one signal characteristic of the wireless signal and the presence of an interfering object. Mitigation measures associated with the wireless signal are performed at least in part based on the correlation.

Description

Technical Field

[0001] This application claims the benefit of priority of U.S. Patent Application No. 63 / 470,734, filed on June 2, 2023, which is hereby incorporated by reference in its entirety.

[0002] The present disclosure generally relates to methods and systems for performing detection of object placement or object movement in the vicinity of a signal transmission zone of semiconductor manufacturing equipment and for managing signal transmission (e.g., wireless connection) based on such detection. Some more specific examples relate to systems and methods for assisting in the calibration of wafer handling robots for semiconductor processing tools.

Background Art

[0003] A substrate processing system may be used to perform deposition, etching, and / or other processing of substrates such as semiconductor wafers. During processing, the substrate is placed on a substrate support within a processing chamber of the substrate processing system. A gas mixture containing one or more precursors may be introduced into the processing chamber, and plasma may be ignited to activate a chemical reaction.

[0004] A substrate processing system may include a plurality of substrate processing tools disposed within a manufacturing room. Each of the substrate processing tools may include a plurality of process modules. In some subprocesses, certain components are controlled by wireless communication. For example, systems such as an integrated wireless adaptive positioning system (APS) and related routines can be used for automated wafer handling sanity checks and calibration. Personnel access and interfering foreign objects disposed in the path between a wireless router and the APS can negatively affect the wireless connection and cause calibration and substrate processing disruptions.

[0005] The background art provided herein presents the general context of this disclosure. The inventors' research, to the extent described in this background art section, is not expressly or implicitly considered prior art to this disclosure, as is the case with any description that may not be considered prior art at the time of filing. [Overview of the project] [Means for solving the problem]

[0006] Some examples include ranging sensors for detecting when a substrate processing tool is accessed by human personnel or when an object is placed on (or near) the substrate processing tool. As used herein, the term “interfering object” refers to a human being (e.g., human personnel such as maintenance personnel servicing a substrate processing tool) and / or an object located in the vicinity of the substrate processing tool (e.g., within a pre-configured distance from the tool, placed on the surface of the tool, or within a specific space associated with the tool, such as an access zone). The sensor can provide detection and distance information that helps identify the interfering object and diagnose any potential faults caused by such personnel access or object placement (e.g., weakened communication signals, or another type of deviation of signal characteristics from a pre-configured or desired value). In some embodiments, mitigation measures are performed based on the detected fault. For example, suppose a weakened communication signal is detected due to the presence of an interfering object in the access zone of a substrate processing tool. In that case, mitigation measures are performed, which may include adjusting the signal strength, rerouting the affected signal, and generating a notification of the detected fault for communications in the access zone.

[0007] In some specific examples, at least one sensor is placed on a semiconductor manufacturing tool or module to detect and monitor the presence of interfering objects within a designated field of view (FoV) using ranging and distance measurement. The sensor can continuously record detection timestamps while the interfering object is within the FoV and track the location of the detected interfering object relative to the sensor. This information allows the operator to know, for example, when and where the interfering object (e.g., human personnel or non-human object) entered a particular space, how long it stayed there, the path and direction of the interfering object's movement, and which parts of the tool or component were accessed. In some examples, the captured data is used to optimize wireless connectivity and fine-tune signal transmission parameters. In some examples, the disclosed techniques optimize signal transmission using machine learning models or other training algorithms or feedback techniques.

[0008] In some embodiments, a system is provided for sensor-assisted signal calibration of a semiconductor processing tool. The system includes a sensor array comprising a plurality of distance sensors, the plurality of distance sensors located within an access zone of the semiconductor processing tool. The system further includes a controller communicatively coupled to the sensor array. The controller is configured to decode a plurality of corresponding sensor measurements received from the plurality of distance sensors. The controller is further configured to detect the presence of an interfering object within the access zone of the semiconductor processing tool based on the corresponding plurality of sensor measurements. The controller is further configured to detect a deviation of at least one signal characteristic of a wireless signal from a pre-configured value, the wireless signal propagating through the access zone, and the deviation being detected while the interfering object is present within the access zone. The controller is further configured to perform mitigation measures associated with the wireless signal based on the detection of the deviation.

[0009] In some embodiments, a system for sensor-assisted signal calibration of a semiconductor processing tool includes a sensor array comprising at least one range sensor. The at least one range sensor is located within the access zone of the semiconductor processing tool. The system includes a controller communicatively coupled to the sensor array. The controller is further configured to decode a plurality of sensor measurements received from at least one range sensor, to detect the presence of an interfering object within the access zone of the semiconductor processing tool based on the plurality of sensor measurements, to detect a deviation of at least one signal characteristic of the wireless signal from a pre-configured value, to generate a correlation based on the deviation of at least one signal characteristic of the wireless signal and the presence of an interfering object, and to perform mitigation measures associated with the wireless signal, at least in part, based on the correlation.

[0010] In some embodiments, a method for sensor-assisted signal calibration of a semiconductor processing tool includes decoding a plurality of sensor measurements received from at least one ranging sensor of a sensor array associated with the semiconductor processing tool. The method further includes detecting the presence of an interfering object within the access zone of the semiconductor processing tool based on the plurality of sensor measurements. The method further includes detecting a deviation of at least one signal characteristic of the wireless signal from a pre-configured value. The method further includes generating a correlation based on the deviation of at least one signal characteristic of the wireless signal and the presence of an interfering object. The method further includes performing mitigation measures associated with the wireless signal, at least in part, based on the correlation.

[0011] In some examples, systems are provided to assist in the calibration of wafer handling robots for semiconductor processing tools. An exemplary system comprises an autocalibration wafer, further described below, which includes a substrate having a first surface configured to contact the end effector of the wafer handling robot as the substrate is transported by the wafer handling robot, and a plurality of sensors supported by the substrate, each sensor having a downward field of view when the substrate is oriented with the first surface facing downward. The exemplary system further comprises an autocalibration controller, which is wirelessly connected to each of the plurality of sensors. Time-of-flight (TOF) ranging sensors are positioned to detect the presence of a person or object located in the “access zone” of the semiconductor processing tool, or to track their movement. A wireless connection controller is wirelessly connected to the autocalibration controller and the TOF ranging sensors and can transmit commands to the autocalibration wafer or components of the semiconductor processing tool. In some cases, feedback from a sensor or wireless device may be sent to one or more other wireless devices within an access zone or wireless environment to optimize communication and / or characterize the zone or environment.

[0012] In some examples, a TOF ranging sensor is a light-detecting and ranging (LIDAR) sensor, which can include a two-dimensional (2D) LIDAR sensor configured to measure the distance to an object and / or a three-dimensional (3D) LIDAR sensor configured to measure the 3D coordinates of an object. A LIDAR sensor works by emitting pulsed light waves into the area around the sensor. The emitted light waves are reflected from surrounding objects and environmental profiles and return to the sensor. The sensor measures the TOF for each pulse to calculate the distance the pulse has traveled and generates a measure of the distance to an object (e.g., using a 2D LIDAR sensor) or a measure of the object's position in a 3D environment (e.g., using a 3D LIDAR sensor).

[0013] Some examples include wireless environments that indicate or contain areas of influence of people (personnel) and / or objects that could disrupt or interfere with wireless communication between wireless devices such as an autocalibration wafer and a wireless router, or between other wireless communication devices. In some examples, a detection system can be used to track or detect personnel and / or objects located between the autocalibration wafer and the wireless router, as well as personnel and / or objects detected as "not between" wireless devices such as the autocalibration wafer and the wireless router. Counterintuitively, in some examples, personnel or objects "between" the autocalibration wafer and the wireless router can both help and hinder wireless communication. For example, a person or object intentionally (or unintentionally) placed between the autocalibration wafer and the wireless router (or other devices) may, in some circumstances, be beneficial in mitigating unwanted wireless communication by blocking signals from unwanted or undesirable devices that would otherwise interfere with communication between devices that are needed or desired. In some examples, the wireless environment can be modified to enhance wireless communication between devices.

[0014] In this specification, where context permits, the terms access zone (e.g., personnel access zone) and wireless environment may be used interchangeably. Broadly speaking, these terms are intended to define or indicate an area or region of influence of people (personnel) and / or objects that may disrupt or interfere with wireless communications between wireless devices (e.g., wireless signals passing through such an area). A monitored access zone and / or wireless environment may include aspects relating to the detection, measurement, and / or monitoring of objects and / or personnel (or one or the other) within the zone or environment. Objects may include parts or components of semiconductor processing tools or their components. Objects may be used directly or indirectly in relation to semiconductor processing tools.

[0015] Several embodiments are shown in the accompanying drawings as examples, not as limitations. [Brief explanation of the drawing]

[0016] [Figure 1] This is a plan view of an exemplary configuration of a semiconductor processing tool including six process modules. [Figure 2] This is a schematic diagram of a semiconductor processing tool according to one embodiment. [Figure 3] This is a schematic diagram of a semiconductor processing tool according to one embodiment. [Figure 4] This diagram illustrates an equipment front-end module (EFEM) adjacent to a vacuum transfer module (VTM) according to one embodiment. [Figure 5A] This figure shows an embodiment of the subject according to an exemplary design. [Figure 5B] This figure shows an embodiment of the subject according to an exemplary design. [Figure 6A] This figure shows further aspects of the subject according to exemplary embodiments. [Figure 6B] This figure shows further aspects of the subject according to exemplary embodiments. [Figure 6C] This figure shows further aspects of the subject according to exemplary embodiments. [Figure 6D] This figure shows further aspects of the subject according to exemplary embodiments. [Figure 7A] This figure shows a flowchart of the method according to an exemplary embodiment. [Figure 7B] This figure shows a flowchart of the method according to an exemplary embodiment. [Figure 8] This block diagram shows an example of a machine in which one or more exemplary embodiments may be implemented or controlled. [Figure 9] This figure shows an example of a wireless environment according to an exemplary embodiment. [Figure 10] This figure shows an embodiment of a wireless environmental controller according to an exemplary embodiment.

Mode for Carrying Out the Invention

[0017] The following description includes a system, method, technique, instruction sequence, and / or computing machine program product that implements an exemplary embodiment of the present disclosure. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. Further areas of applicability of the present disclosure will become apparent from the mode for carrying out the invention, the claims, and the drawings. The mode for carrying out the invention and the specific examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure.

[0018] The quantity, location, etc. of substrate processing tools in a manufacturing room may be restricted by the room size and the installation area of each tool. The installation area of a substrate processing tool is the floor area required for proper installation of the substrate processing tool. To maximize the floor area usage in a manufacturing room, multiple tools may be arranged in close proximity to each other. Depending on how close the tools are arranged to each other, wireless signal interference (e.g., a noisy wireless signal) may occur within a single tool. In some cases, the presence of interfering objects within the signal transmission zone of a substrate processing tool may also adversely affect the wireless connection. Systems and methods according to the principles of the present disclosure provide various configurations of processing tools and sensors for detecting and analyzing communication signals or objects that potentially interfere with overall signal transmission. In some embodiments, this information can then be used to maximize the wireless connection of one or more processing tool components. Some examples include using a ranging sensor or a TOF sensor (or one or more other types of sensors) to detect the presence of a human or an object regardless of whether distance measurement for obstacle analysis is available. In some examples, the sensor includes a proximity sensor or other object detection sensor that can characterize the wireless environment based on parameters such as wireless signal propagation, wireless signal strength, secure wireless signal reception, and other factors and / or characteristic evaluations. Some examples may include sensors that can detect specific types of materials such as metal and / or concrete that affect wireless signals.

[0019] Identifying interfering objects using cameras or other full-frame image capture / recording devices is outside the scope of this disclosure. Semiconductor manufacturing facilities often prohibit image capture / video recording within the manufacturing chamber due to the sensitivity of the manufacturing process. In addition, when objects are identified using image recognition processes, additional steps (e.g., image recognition, image noise filtering, etc.) are required to extract important parameters (e.g., distance to the transmitter), which can therefore slow down the entire analysis process. The ability to quickly and accurately identify what interfering objects are without intrusively capturing the background environment using image capture / recording devices is one of the advantages associated with the disclosed techniques.

[0020] Figure 1 shows a plan view of an exemplary configuration of a semiconductor processing tool 102 according to the present disclosure. The semiconductor processing tool 102 includes an equipment front-end module (EFEM) 104 configured to house at least a portion of a load lock 106. In one example, the transfer robot 112 of the EFEM 104 is located closer to the loading station 114 on the front wall (e.g., the first side) of the EFEM 104 than on the rear wall 116 (e.g., the second side). For example, the loading station 114 may correspond to a front opening integrated pod (FOUP).

[0021] As illustrated, the semiconductor processing tool 102 includes six process modules 108 (also called PMs). However, other configurations of the semiconductor processing tool 102 may include more than six process modules 108. For example, the length of the vacuum transfer module (VTM) 110 may be extended to accommodate additional process modules 108. Similarly, the VTM 110 may include vacuum transfer robots 118 having various configurations. For example, the semiconductor processing tool 102 includes three vacuum transfer robots 118. In the semiconductor processing tool 102, the vacuum transfer robots 118 are aligned with the longitudinal central axis of the VTM 110. Although shown having one or two arms, each vacuum transfer robot 118 may have a configuration including one, two, or more arms. In some examples, the vacuum transfer robots 118 may include two end effectors 124 on each arm, as shown in Figure 1. The semiconductor processing tool 102 may include one or more external storage buffers 120 configured to store one or more substrates during the processing stage. In some examples, one or more internal storage buffers 122 may be located within the VTM 110. In some examples, one or more of the external storage buffers 120 and / or internal storage buffers 122 may be replaced by process modules or other components.

[0022] In some examples, one or more of the EFEM104, load lock 106, VTM110, and process modules 108 may have a stacked configuration, in other words, a high aspect ratio. For example, each process module 108 may correspond to two (or more) process modules 108 in a vertically stacked configuration (i.e., one process module 108 positioned above / below another), each VTM110 may correspond to two (or more) VTMs in a vertically stacked configuration, each load lock 106 may correspond to two (or more) load locks 106 in a vertically stacked configuration, and each loading station 114 may correspond to two (or more) loading stations 114 in a vertically stacked configuration. The height of the EFEM104 may be increased to allow the vacuum transfer robot 118 to ascend and descend to different levels within the EFEM104 to access multiple levels of loading stations 114 and load locks 106.

[0023] In particular, this high aspect ratio of the vertically stacked arrangement may present challenges to establishing and maintaining good wireless connectivity between the wireless router and wireless devices such as APS, for example, as further described below. Furthermore, the path between the wireless router and APS, for example, personnel access and interfering foreign objects located in the upper access zone 128 or grid of the VTM 110, may negatively interfere with the wireless connectivity and cause failures. Systems and methods based on the principles of this disclosure provide various configurations of a processing tool and at least one sensor 126 to maximize the wireless connectivity of the processing tool components. In some embodiments, at least one sensor 126 is a TOF sensor. As used herein, the term “time-of-flight sensor” (or “TOF sensor”) refers to a sensor configured with time-of-flight techniques that calculate the distance between two points using the time it takes for a light quantum to travel between the two points. Some examples of at least one sensor 126 include a TOF sensor for detecting the presence and distance measurement (distance measurement) of a person or object for fault analysis, wireless connectivity optimization, and fine-tuning of board processing parameters such as calibration and board placement, which are described further below.

[0024] Even if Figure 1 shows at least one sensor 126 as a single sensor, the disclosure is not limited thereto, and multiple TOF sensors (and / or other types of sensors) can be used as at least one sensor 126. For example, Figure 9 shows a configuration in which multiple sensors are used to detect interfering objects, evaluate the location and / or movement patterns of the objects, evaluate the interference of the objects with wireless (or wired) signal communications, generate notifications of any detected interference, and / or take mitigation measures to reduce or prevent the interference.

[0025] In some examples, at least one sensor 126 is positioned on the rear wall 116 of the EFEM 104 overlooking the VTM 110. In some implementations, the field of view of at least one sensor 126 is configured and limited to match the length of the VTM 110 to avoid the inclusion of external noise or unwanted data. The semiconductor processing tool utilizes a wafer handling robot, such as a vacuum transfer robot 118, to move semiconductor wafers between various wafer stations, such as the process module 108 of the semiconductor processing tool 102. The wafer handling robot typically picks up semiconductor wafers from below using a blade-type or spatula-type end effector (such as an end effector 124), and since the semiconductor wafers are not securely fixed to the wafer handling robot end effector, there is often some degree of variation in the relative positioning between the end effector and the semiconductor wafer placed on it. In some examples, the variation is caused by the positioning accuracy of the robot, tolerances of all hardware specifying the position of the wafer stations, and changes in the position of those stations due to temperature changes caused by thermal expansion. Due to the sensitivity of semiconductor processing operations, it is typical to compensate for such variations when positioning semiconductor wafers using wafer handling robots, so that the wafers are placed within an acceptable tolerance range at the desired location within each processing station, for example, near the center of the processing station or concentrically. Modern semiconductor processing tools utilize active wafer centering (AWC) systems to assist in such wafer positioning.

[0026] One aspect of the present disclosure includes, in particular, an automated calibration system, e.g., the APS described above, which may be used with an AWC system (or similar apparatus) and / or a wafer handling robot to provide automated teaching of an AWC system and / or a wafer handling robot for semiconductor processing tools, and such a system may be used for automated teaching of a wafer handling robot under vacuum or atmospheric pressure, since the chamber in which teaching is performed may be sealed so as to be in normal semiconductor processing operation. In some examples, the AWC is a method for measuring the deviation of the wafer position at a given (x,y) position with respect to the robot position. It does not necessarily inform the robot of the location of the wafer station where the wafer is placed. In some examples, the APS teaches the robot where the wafer station is located. The APS does not inform the AWC.

[0027] Therefore, given the sealed access, automated teaching may be performed wirelessly or at least involve wireless communication. Such automated calibration systems can also allow various aspects of component or wafer arrangement to be evaluated and / or corrected as needed to conform to process requirements. Automated calibration systems may also be used to guide the placement of edge rings, which are nominally annular structures typically having an inner diameter slightly larger (sometimes smaller) than the outer diameter of the semiconductor processing wafer, thereby effectively "expanding" the diameter of the semiconductor wafer during processing. Edge rings have the effect of causing any "edge effects" that can degrade the process on the wafer, resulting in uniformity at the outer edge of the edge ring (where wafer uniformity is largely unaffected) rather than the semiconductor wafer itself.

[0028] At the heart of the automated calibration system is the automated calibration wafer, sometimes referred to as an APS wafer (such as the APS wafer 130 in Figure 1), which can collect a large amount of information from various onboard sensors, enabling its use as part of a fully automated teaching process. The sensors on the APS wafer 130 communicate wirelessly with one or more controllers (such as the controller 132 in Figure 1 and / or the controller 800 in Figure 8) and / or wireless routers 134 or process modules 108 to relay signals and data. Such automated calibration wafers may be used, for example, to perform diagnostic evaluations of components within semiconductor processing tools and to obtain information that allows the operation of semiconductor processing tools to be adjusted to improve wafer processing performance.

[0029] Generally speaking, an autocalibration wafer for a particular semiconductor processing tool may have a size and shape similar to the size and shape of the wafer and / or edge ring configured to be processed by the semiconductor processing tool, thereby enabling the autocalibration wafer (e.g., APS wafer 130) to be transported by the wafer handling robot of the semiconductor processing tool (see Figure 1) in much the same way that the wafer handling robot transports the semiconductor wafer during processing. Therefore, the autocalibration wafer may have a maximum height and diameter smaller than the smallest vertical and horizontal clearances of the passage of the semiconductor processing tool through which the wafer handling robot can transport the wafer.

[0030] As stated above, an autocalibration wafer may contain various sensors. The number and type of sensors may vary depending on the specific functions provided by the autocalibration wafer. It will be understood that an autocalibration wafer may be configured to provide any, some, or all of the sensors / functions described herein.

[0031] In addition to the various sensors that an autocalibration wafer may contain, the autocalibration wafer may also contain various components for controlling and acquiring data from those sensors, for wirelessly communicating with other components (such as the controller 132 of the semiconductor processing tool 102 in Figure 1, and / or the wireless router 134, and / or the process module 108), and / or for storing and / or manipulating the data collected from the sensors. Thus, such an autocalibration wafer may be linked or wirelessly connected to the controller or router of the semiconductor processing tool, introduced into the semiconductor processing tool, and then, through actions triggered by one or both of the controllers (or multiple controllers) of the autocalibration wafer and the controllers (or multiple controllers) of the semiconductor processing tool, perform various sensing and data acquisition operations during various stages of a calibration or placement routine performed by the semiconductor processing tool. As will be apparent from the examples described in more detail below, such a calibration or placement routine may be performed by the semiconductor processing tool with little or no human supervision.

[0032] A first controller carried on an autocalibration wafer may also be communicatively connected to a first wireless communication interface, such as Wi-Fi, Bluetooth, or another wireless communication interface, and as a result, commands and / or data may be transmitted between the first controller and, therefore, the autocalibration wafer. For example, a semiconductor processing tool interfaced with an autocalibration wafer may include a second controller having one or more second processors and one or more second memories. The second controller may then be communicatively connected to a second wireless communication interface, which may be configured to interface with the first wireless communication interface of the autocalibration wafer. Thus, the autocalibration wafer may be able to communicate wirelessly with the semiconductor processing tool, and information, commands, and other data may be transmitted between the autocalibration wafer and the semiconductor processing tool.

[0033] Any interruption of the wireless connection between wirelessly connected components and an automated calibration wafer can have significant adverse effects. For example, the presence of personnel positioned in the path between semiconductor processing tools and an automated calibration wafer, and any interfering foreign objects, can interfere with the wireless connection and cause calibration and placement failures. Embodiments of this disclosure utilize one or more object detection sensors and analysis systems to gain insight into the cause of signal / connection interference. Object detection and diagnosis based on such detections provided by embodiments of this disclosure enable information-based analysis of failures, leading to improved calibration and placement routines.

[0034] Figure 2 depicts a schematic diagram of a semiconductor processing tool using an autocalibrated wafer. Figure 2 shows a portion of the semiconductor processing tool, such as semiconductor processing tool 102 in Figure 1. The depicted portion of the semiconductor processing tool includes two wafer stations: a first wafer station 202 and a second wafer station 204. However, the tool may also include further wafer stations. Each wafer station corresponds to a location where one or more wafers may be placed during various operations performed by the semiconductor processing tool. Wafer stations may be located, for example, in one or more process chambers of the tool (e.g., in process module 108), on a VTM (e.g., VTM 110), in a buffer used to store wafers before or after processing (e.g., external storage buffer 120, or internal storage buffer 122), in an airlock or load lock (e.g., load lock 106) that allows wafers to be transferred between environments of different pressures, in a load port (e.g., located in loading station 114), in a front opening integrated pod (FOUP) that can be docked to a load port, and so on.

[0035] In Figure 2, the first wafer station 202 is provided by a semiconductor processing chamber, while the second wafer station 204 is provided by a docking station dedicated to storing the autocalibration wafer 206 (however, such a dedicated docking station may not be included in some implementations). The docking station may have features (not shown) for charging the autocalibration wafer 206, or may be configured to interface with various aspects of the autocalibration wafer 206. In some implementations, the second wafer station 204 (docking station) may be located within (or mounted on) a VTM (e.g., VTM 110) to allow access by a wafer handling robot (e.g., vacuum transfer robot 118) within the VTM, which may then be trained using the autocalibration wafer 206. In other implementations, the second wafer station 204 (docking station) may be located in an EFEM (e.g., EFEM104) or another location at or near atmospheric pressure, in which case the automatically calibrated wafer 206 may be first retrieved using a wafer handling robot located in the EFEM and then transferred to another wafer handling robot located in the VTM.

[0036] The first wafer station 202 may have an associated wafer support 208 (although the second wafer station 204 does not have a wafer support, it may have one that can receive an autocalibrated wafer 206 when placed therein). In some examples, the wafer stations may be associated with an AWC system 210, which may enable the acquisition of wafer center position measurements when a wafer is introduced into or removed from the associated wafer station. In this example, the AWC system 210 is associated with the first wafer station 202 and includes two vertically oriented optical beam sensors (represented by dots in the AWC system 210) that can detect when the edge of a wafer crosses either optical beam. The AWC system 210 may be used to determine the center position of a wafer supported by an end effector (e.g., end effector 124) of a wafer handling robot (e.g., vacuum transfer robot 118) of a tool (e.g., semiconductor processing tool 102) relative to a specific known reference frame, thereby enabling decisions regarding any positioning corrections that may need to be made before the wafer is placed in the desired position. As shown in Figure 2, the wafer handling robot 212 supports the edge ring 214 on the end effector 216 in preparation for placing the edge ring 214 on the wafer support 208. The autocalibration wafer 206 is temporarily stored in the second wafer station 204 during this time. Various calibrations, such as eccentricity and wafer placement techniques, may be implemented using the autocalibration wafer 206 of the substrate processing tool and wirelessly connected components such as the controller 218, as described above.

[0037] Figure 3 shows a schematic diagram of a semiconductor processing tool 302 according to one embodiment. The semiconductor processing tool 302 includes an EFEM 304 and a load lock 306 positioned between the EFEM 304 and the VTM 310. The VTM 310 houses a wafer handling robot (not visible on the top surface or under the housing of the VTM 310 shown in Figure 3). The wafer handling robot may include, for example, a vacuum transfer robot 118 (Figure 1) or a wafer handling robot 212 (Figure 2). Other wafer handling robots are also possible. The semiconductor processing tool 302 includes a loading station 312.

[0038] As illustrated, the semiconductor processing tool 302 includes 10 stacking process modules 308. However, other configurations of the semiconductor processing tool 302 may include more or fewer stacking process modules 308 than 10. For example, the length of the VTM 310 may be extended to accommodate additional stacking process modules 308.

[0039] The EFEM304 has a rear wall 314. In the illustrated example, at least one sensor 316 (e.g., a TOF sensor) is mounted on the rear wall 314 to supervise and monitor an access zone 318 located above the housing of the VTM310. The access zone 318 may be a three-dimensional access zone defined, for example, by the dimensions shown in parentheses in Figure 4. In other examples, the access zone 318 may be defined by a plane, line, or specific point location associated with the semiconductor processing tool 302, or the stacking process module 308, or other components of the semiconductor processing tool 302. At least one sensor 316 may be provided with one or more fixed or movable positions located at an alternative sensor position 324, for example, as shown in Figure 3, but other positions are also possible in relation to the optimization of wireless connectivity, which will be further described below. At least one sensor 316 may include an array of sensors provided at one or more positions associated with the semiconductor processing tool 302 and / or its components. The sensor array may include multiple wirelessly interconnected time-of-flight sensors, each sensor positioned in different locations within, on, or around the semiconductor processing tool 302 or its components (for example, as shown in Figure 9). In some examples, at least one sensor 316 is wirelessly connected to a wireless connectivity controller 320 and / or at least one remote wireless router 322. In some embodiments, at least one sensor 316 includes an array of sensors connected to each other and / or to the controller via physical signal transmitters such as wires.

[0040] As illustrated, one or more of the EFEM 304, load lock 306, VTM 310, and stacked process modules 308 may have a stacked configuration, in other words, a high aspect ratio and at least part of the access zone 318. For example, each of the stacked process modules 308 may correspond to two (or more) stacked process modules 308 in a vertically stacked configuration (i.e., one stacked process module 308 is positioned above / below another), each of the VTM 310 may correspond to two (or more) VTM 310 in a vertically stacked configuration, each of the load lock 306 may correspond to two (or more) load lock 306 in a vertically stacked configuration, and each of the loading stations 312 may correspond to two (or more) loading stations 312 in a vertically stacked configuration. The height of the EFEM304 may be increased to allow the wafer handling robot within the VTM310 to ascend and descend to different levels to access multiple levels of loading stations 312 and load locks 306.

[0041] Figure 4 depicts an EFEM 402 positioned adjacent to a VTM 404 in an exemplary configuration of a semiconductor processing tool. At least one sensor 406 is mounted on the rear wall 408 of the EFEM 402. Other mounting locations for at least one sensor 406 on or within the semiconductor processing tool are also possible. At least one sensor 406 has a field of view 414. At least one sensor 406 is positioned so that the field of view 414 of at least one sensor 406 can monitor the intrusion of personnel and / or objects into an access zone 416 located above the VTM 404. The assigned dimensions (e.g., length, width, height) or area size of the field of view 414 may correspond to the dimensions of the EFEM 402, and / or the dimensions of the VTM 404 (e.g., the VTM length 412 of the VTM 404), and / or the dimensions of the access zone 416, and / or the mode of tool density described above. In some implementations, the field of view 414 of at least one sensor 406 is configured and restricted to match the dimensions of the VTM 404 and / or access zone 416 in order to avoid inclusion of external noise or collection of unwanted data from irrelevant zones.

[0042] In some implementations, at least one sensor 406 (hereinafter including at least one sensor 126 and at least one sensor 316 collectively) has ranging and detection capabilities. At least one sensor 406 may include a LiDAR sensor. At least one sensor 406 can determine the distance between the at least one sensor 406 and a detected person and / or object within the field of view 414. Using an algorithm, at least one sensor 406 can, in some implementations, determine the distance between the detected person or object and the walls or features of adjacent components, such as EFEM 402 or VTM 404 (or other components), which are located within the field of view 414 or define or seal the access zone 416. At least one sensor 406 may be included in an array of sensors or provided as one of a set of multiple or interconnected time sensors surrounding the access zone 416. In some embodiments, at least one sensor 406 includes an array of multiple TOF sensors that can provide a mosaiced or combined field of view 414. Considering certain drawbacks, the use of a camera as a sensor in the wireless connection methods described herein is outside the scope of this disclosure.

[0043] In some embodiments, at least one sensor 406 is a 2D LIDAR sensor, and the 2D LIDAR sensor is configured to measure the distance to an object (e.g., single distance measurement). In some embodiments, at least one sensor 406 is a 3D LIDAR sensor, and the 3D LIDAR sensor is configured to measure the 3D coordinates of an object (e.g., spatial coordinates in a 3D coordinate system including the x, y, and z axes). In some examples, at least one sensor 406 is positioned on a semiconductor processing tool or module to detect and monitor the presence of a person or object within a designated field of view using ranging and distance measurement. At least one sensor 406 can track the position of the detected person or object relative to the sensor by continuously (or periodically) recording detection timestamps while the person or object is within the field of view. The information allows the semiconductor processing tool to obtain information such as when and where maintenance personnel or objects entered a particular location (such as an access zone 416), how long they stayed there, the direction of movement, and which parts of the semiconductor processing tool or component were accessed. Data captured and generated by at least one sensor 406 can be used to generate analysis regarding access to and movement of personnel and / or objects within the field of view 414 or the monitored access zone 416. In embodiments where at least one sensor 406 is a 2D LIDAR sensor, only the distance to an object is measured. In some embodiments, at least one sensor 406 includes a plurality of 2D LIDAR sensors positioned near the access zone 416, and the 2D LIDAR sensors can be used to determine the position (e.g., the spatial coordinates of an object) of an object positioned within the access zone 416. In some embodiments, at least one sensor 406 may be a 3D LIDAR sensor, and the 3D LIDAR sensors can be used to determine the spatial coordinates of an object positioned within the access zone 416.

[0044] For example, with respect to Figures 5A and 5B, the movement of a person 518 entering an access zone 416, which is monitored via (e.g.) a ladder 520, can be tracked and recorded. The output of at least one sensor 406 can be used to detect the presence of a person 518 entering and moving within the access zone 416, and to track their movement and associated distance (or position), as shown in the exemplary person detection graph 502 of Figure 5B. The y-axis of the person detection graph 502 represents the detected distance of the person 518 from the EFEM 402 within the access zone 416 above the VTM 404. Other access zones defined by other areas or components of the substrate processing are also possible. The corresponding field of view 414 may be adjusted or configured to monitor such access zones accordingly. The x-axis of the person detection graph 502 represents time within the access zone 416, indicating the period of time during which the person 518 at the detected position enters the access zone 416 monitored by at least one sensor 406, moves or remains stationary within it, and / or leaves it.

[0045] In graph zone 504, there is no detection of the presence or movement of person 518. At graph point 514, the presence of person 518 within the monitored access zone 416 is detected. In graph zone 506, person 518 is tracked by at least one sensor 406 as they move toward EFEM 402. In graph zone 508, person 518 is stationary (no movement is detected). The ranging capability of at least one sensor 406 is used, for example, to detect that person 518 is located near EFEM 402 while stationary. Other modes or examples of ranging and position detection are also possible. In graph zone 510, person 518 is again moving, but this time it is detected as moving toward ladder 520. At graph point 516 (corresponding to the y value as graph point 514, i.e., distance from EFEM 402), person 518 is detected as leaving access zone 416. In graph zone 512, no further presence or movement is detected. In the embodiment where at least one sensor 406 is a single 2D LIDAR sensor, only one person within the sensor's FoV can be detected. However, if at least one sensor 406 includes multiple 2D LIDAR sensors (for example, arranged across each other around the boundary of the access zone 416), multiple people can be detected when entering the access zone 416. Alternatively, if at least one sensor 406 includes a 3D LIDAR sensor, multiple people can similarly be detected when entering the access zone 416.

[0046] Further detection examples are provided in Figures 6A to 6D. In each graph depicted in these figures (i.e., the interfering object detection graph 602, the interfering object detection graph 604, the interfering object detection graph 606, and the “all quiet” graph 608, respectively), the y-axis represents the detected distance of the person 518 or object 610 from the EFEM 402 within the access zone 416 above the VTM 404. The x-axis represents the time within the access zone 416 (detected, for example, by at least one sensor 406), indicating the period between the person 518 or object 610 at the detected location entering the access zone 416 monitored by at least one sensor 406, moving within it or remaining stationary, and / or leaving the access zone 416. In embodiments where at least one sensor 406 is a 2D LIDAR sensor, the distance to the person 518 or object 610 can be determined. In embodiments where at least one sensor 406 includes multiple 2D LIDAR sensors or a single 3D LIDAR sensor, the spatial coordinates of a person 518 or an object 610 can be determined. In some embodiments, the use of multiple 2D LIDAR sensors or 3D LIDAR sensors as at least one sensor 406 can ensure object detection coverage for the entire access zone 416 without the presence of dead zones.

[0047] In the interfering object detection graph 602, object 610 (such as a package, box, or maintenance tool) is located within the access zone 416. Graph point 612 indicates that the presence of object 610 has been detected by at least one sensor 406. In graph zone 614, a flat line in the interfering object detection graph 602 indicates that no further movement is detected. Object 610 is stationary. The ranging capability of at least one sensor 406 determines that object 610 remains stationary for a period of time (e.g., between approximately 30 and 225 seconds, given by the x-axis value) at a distance of approximately 3.25 meters from EFEM 402 (given by the y-axis value). In some examples, the extended stationary characteristic of object 610 (represented, e.g., by a relatively long flat line in the interfering object detection graph 602) may help distinguish object 610 from a moving person 518 that may be present in the access zone 416.

[0048] Similarly, in the interfering object detection graph 604 (Figure 6B), neither the presence nor movement of an interfering object is detected in graph zone 615. A person 518 (having object 610) is detected entering access zone 416, as indicated by graph point 616. In graph zone 618, the person 518 moves to a position approximately 1.4 meters from EFEM 402. The speed of movement may be indicated, for example, by a short period of time provided by the x-axis value. Positional information is represented by a value reflected on the y-axis. The data underlying the tracking and analysis of such interfering objects (e.g., objects and / or people) is captured and generated by at least one sensor 406. In graph zone 620, the interfering object (e.g., person 518) remains stationary, and in graph zone 622, the interfering object leaves the surface of VTM 404 and moves away from the side of EFEM 402 (without picking up object 610, which may be a tool). At graph point 624, the presence of object 610 (left behind by person 518 before leaving the surface of VTM404) is detected. Object 610 remains stationary for the duration of graph zone 626. Vertical graph portion 628 indicates the detection of person 518 (e.g., at graph point 630) (e.g., person 518 is returning to pick up object 610). In graph zone 631, person 518 moves to the exact location where object 610 was previously left (e.g., graph zones 626 and 632 are associated with the same distance to EFEM402). In graph zone 632, the interfering object (e.g., person 518 with object 610) remains stationary. In graph zone 634, the interfering object (e.g., person 518 with object 610) leaves the surface of VTM404 and moves away from the side of EFEM402 (without picking up object 610, which could be a tool). At graph point 636, the interfering object (e.g., person 518 possessing object 610) is detected to be leaving access zone 416. No further presence or movement is detected in graph zone 638.

[0049] In the interfering object detection graph 606 (Figure 6C), in graph zone 639, neither the presence nor movement of person 518 or object 610 is initially detected. At graph point 640, the presence of person 518 (in this example) entering access zone 416 is detected. Graph zone 641 indicates that person 518 moved toward EFEM402 from a position approximately 4 to 3 meters away from EFEM402 (as given by the y-axis value). Graph zone 642 indicates (or represents) that person 518 remained stationary for 25 to 100 seconds (i.e., a period of 75 seconds). Graph zone 643 indicates that person 518 moved toward EFEM402 from a position approximately 3 to 2 meters away from EFEM402 (as given by the y-axis value). Graph zone 644 indicates that the person remained at a position of 2 meters (e.g., adjacent to the first stacking process module 308) for the period indicated on the x-axis. Graph zone 645 indicates that person 518 has moved again to another location near EFEM 402 (for example, adjacent to the second stacked process module 308), approximately 1.25 meters away from EFEM 402, and remained stationary there for the duration of graph zone 646. In graph zone 647, person 518 moves toward ladder 520, and in graph zone 648, exits access zone 416. No further presence is detected in graph zone 649.

[0050] Figure 6C further includes a wireless signal strength graph 650 correlated with the movement of the interfering object, as shown by the interfering object detection graph 606. More specifically, a wireless transceiver (not shown in Figure 6C) can be placed within the internal space of the EFEM 402 (e.g., in the vicinity of at least one sensor 406). The wireless signal transmitted by (or communicated for reception by) such a transceiver within the access zone 416 has a signal strength proportional to the proximity of the interfering object to the EFEM 402. As shown by graph 650, the wireless signal strength decreases as the interfering object moves closer to the EFEM 402 and increases as the interfering object moves away from the EFEM 402 and out of the access zone 416. In some embodiments, the wireless signal strength (or one or more other signal characteristics of the wireless signal traversing the access zone) can be monitored periodically (e.g., while the interfering object is detected as stationary or moving within the access zone using at least one sensor 406). Mitigation measures can be taken based on the detected wireless signal strength. For example, suppose the signal strength falls below a first threshold. In this case, the transceiver can be instructed to increase the transmit power or adjust the signal propagation path (for example, by switching antennas or changing the directivity of the antenna panel). Suppose the signal strength further deteriorates and falls below a second threshold. In this case, one or more functions of the substrate processing tool can be suspended until the signal strength increases to an optimal level or until the interfering object is no longer detected. In some embodiments, mitigation measures may include generating notifications of detected deviations in the signal characteristics.

[0051] A “completely quiet” situation may be represented, for example, by graph 608 (Figure 6D). No interfering object (e.g., person 518 or object 610) is detected by at least one sensor 406 as entering, moving, or remaining stationary within the access zone 416, or exiting the access zone.

[0052] In some cases, the detected presence, location, and / or movement of interfering objects (e.g., person 518 or object 610) within access zone 416 may correlate with detected failures in calibration or placement routines within semiconductor processing tools, as described above. For example, the timestamp of detected presence, location, or movement may coincide with the timestamp of a failure in calibration, board placement, and / or wireless connectivity. Other correlations or coincidences with such failures, or other types of failures, are also possible. In some cases, the captured and correlated data can be used to optimize and fine-tune selected parameters related to tool calibration, board placement, and wireless connectivity, for example, in training machine learning models or "teaching" robots, AWC, or APS.

[0053] Some examples of correlations include wireless connectivity parameters such as wireless frequency, wireless channel, wireless signal strength, wireless coverage area, and wireless dead zone. Wireless connectivity parameters, or at least their optimization or measurement, correlate in some examples with the detected time, date, presence, duration, location, or movement of people or objects within an access zone (such as access zone 128, and / or access zone 318, and / or access zone 416). Correlation may be performed, for example, by the wireless connectivity controller 320 in Figure 3. In some examples, sensors, and / or wireless connectivity controllers, and / or wireless routers are tunable or configurable to adjust the values ​​of wireless connectivity parameters in real time.

[0054] The wireless connection controller 320 can communicate with and / or acquire data from one or more sensors (such as at least one sensor 126 and / or at least one sensor 316 and / or at least one sensor 406). The wireless connection controller 320 can also communicate with and / or acquire data from one or more wireless routers (such as wireless router 134 or at least one remote wireless router 322). The wireless connection controller 320 can also communicate with and / or acquire data from other controllers (such as controller 218 and / or controller 800 (Figure 8)). These communications and data acquisition may occur synchronously or asynchronously with the relevant components in the process of outputting wireless connection results, wireless connection analysis, etc., enabling optimization or at least improvement of wireless connection parameters, correlations, recommendations, and / or other outputs.

[0055] In some examples, the wireless connection controller 320 performs a correlation between wireless connection parameters and the positions of sensors (for example, related to the positions of at least one sensor 126 and / or at least one sensor 316 and / or at least one sensor 406). In some examples, the wireless connection controller 320 performs a correlation between wireless connection parameters and the positions or configurations of semiconductor processing tools (such as semiconductor processing tool 102 and / or semiconductor processing tool 302). In some examples, the wireless connection controller 320 performs a correlation between wireless connection parameters and the positions or configurations of EFEMs (such as EFEM 104 and / or EFEM 304 and / or EFEM 402). In some examples, the wireless connection controller 320 performs a correlation between wireless connection parameters and the positions or configurations of VTMs (such as VTM 110 and / or VTM 310 and / or VTM 404).

[0056] In some embodiments, the disclosed functions can be configured and performed by one or more additional controllers operating in conjunction with (or instead of) the wireless connectivity controller 320. Such one or more additional controllers may include a system controller or off-the-tool controller for the semiconductor processing tool 302 (e.g., a computing device for a field technician, such as a tablet or smartphone).

[0057] In some examples, with respect to Figure 1, the wireless connection controller 320 correlates wireless connection parameters with the location or configuration of other components or semiconductor processing tools, including, but not limited to, the load lock 106, process module 108, transfer robot 112, loading station 114, rear wall 116, vacuum transfer robot 118, external storage buffer 120, internal storage buffer 122, end effector 124, APS wafer 130, controller 132, and wireless router 134.

[0058] In some examples, with respect to Figure 2, the wireless connection controller 320 correlates wireless connection parameters with the location or configuration of other components or semiconductor processing tools, including, but not limited to, the first wafer station 202, the second wafer station 204, the autocalibrated wafer 206, the wafer support 208, the AWC system 210, the wafer handling robot 212, the edge ring 214, the end effector 216, and the controller 218.

[0059] In some examples, with respect to Figure 3, the wireless connection controller 320 correlates wireless connection parameters with the location or configuration of other components or semiconductor processing tools, including, but not limited to, the load lock 306, the stacking process module 308, the loading station 312, the rear wall 314, at least one sensor 316, the access zone 318, at least one remote wireless router 322, and alternative sensor locations 324.

[0060] In some examples, with respect to Figure 4, the wireless connection controller 320 correlates wireless connection parameters with the position or configuration of other components or semiconductor processing tools, such as, but not limited to, at least one sensor 406, a rear wall 408, a top surface 410, a VTM length 412, a field of view 414, and an access zone 416.

[0061] In some examples, the wireless connection controller 320 correlates wireless connection parameters with other aspects, including, but not limited to, tool footprint, tool spacing, tool pitch, tool density in the manufacturing chamber, the number or location of substrate processing tools and / or process modules per unit area of ​​the manufacturing chamber, external wireless devices (such as smartphones or laptops carried by maintenance personnel), and the external presence of human personnel and / or other interfering objects. In some examples, the analysis of wireless connection data acquired and processed by the sensor and wireless connection controller, including the generation of one or more of the correlations described above, notifies of the reconfiguration or reorientation of the semiconductor processing tool or its components to maximize the output or routine of the semiconductor processing tool or its components. The routine may include calibration or wafer positioning, as described above.

[0062] The reoriented or reconfigured components may include any one or more of the components and correlations described above and / or further described in relation to the attached drawings. In some examples, the reconfiguration or reoriented of a tool or component is performed and the correlations are “re-run” to detect improvements in one or more wireless connectivity parameters. Based on such analysis, wireless connectivity parameters may be modified or affected to result in improvements or changes to other wireless connectivity parameters. The “re-run” correlations can form part of a machine learning process to train a machine learning model to process and / or provide feedback that notifies the wireless connectivity parameters, or the real-time reoriented or reconfiguration of tool components to improve or maximize the efficiency or accuracy of calibration, processing, or placement routines, for example, by communicating with the wireless connectivity controller 320. For example, based on data captured by sensors and processed by the wireless connectivity controller, the height or position of a process module may be reconfigured to maximize the signal strength of a wireless signal that is generally transmitted to or received by an access zone, or explicitly transmitted to or received by a wafer handling robot or calibration tool. Other examples are possible.

[0063] In further embodiments, the field of view 414 of at least one sensor 406 may be configured to be less than, equal to, or greater than the VTM length 412. Other field of view configurations are also possible. In some examples, the field of view 414 is configured to be equal to the VTM length 412, and as a result, potential (or actual) detection of a person 518 or object 610 by at least one sensor 406 outside the field of view 414 is not reported to the wireless connectivity controller 320. Only detections within the field of view 414 are reported. This field of view configuration can reduce noise and improve the accuracy of correlation based on adjusted data collection. In some examples, to facilitate and simplify operation, only a single aspect, such as length or component dimensions, is used to determine or establish the field of view 414 of the sensor. This can prevent the need to set up sensor angles and special sensor mountings. In other examples, the aspects are more complex and can generate more insightful wireless connectivity data and higher correlations. In some examples, the sensor mounting or field of view is adjustable and / or retrofittable during use after installation and commissioning. For example, default sensor mounting and configuration may be provided by the semiconductor processing tool supplier and then modified or altered by the customer or semiconductor device manufacturer after the tools are supplied and installed to accommodate various and / or different manufacturing processes and techniques. In some examples, one or more sensors may be mounted fixedly to a mounting connector or slider (i.e., the sensing direction cannot be tilted). In some examples, one or more sensors may be mounted via a ball socket joint to allow for changes in the sensing direction (i.e., the sensing direction can be tilted).

[0064] For example, in some examples involving LIDAR sensors, the field of view is conical. Some examples include a narrow 4° field of view. In some examples, the resolution of the field of view is adjustable in real time. In some examples, the LIDAR sensors described herein can be configured to detect a 12-inch x 12-inch package on the floor of the access zone above the VTM.

[0065] This example excludes the use of imaging cameras as “sensors” in the wireless connectivity techniques described herein. For privacy and / or confidentiality reasons, some tool operators prohibit the use of such cameras within manufacturing plants, particularly cameras pointing from the outside at areas around semiconductor processing tools. The use of sensors conveniently and adequately satisfies this need and can capture relevant wireless connectivity data (such as the presence, distance, and / or range of people / objects) without the need to capture irrelevant images or other unwanted information. Conventional cameras also typically cannot adequately identify the presence and location data of people / objects. In contrast, sensors such as LIDAR sensors can provide detailed presence and location data of targets.

[0066] Several examples of this allow for the extraction of tight correlations, enabling tool manufacturers to, for example, block off empty space around installed tools or increase tool density within a manufacturing room (manufacturing plant) while optimizing wireless connectivity between connected devices. Some examples incorporate feedback from sensors to adjust or manage the transmission of wireless signals to calibration wafers. Thus, in addition to knowing that a detected person or object is interfering with a given wireless signal, some examples use feedback data in real time to improve connectivity between the router and the APS sensor or calibration wafer. The information captured by the sensor correlates, in some examples (using a wireless signal strength graph such as graph 650 in Figure 6C, for example) with the wireless signal strength in a favorable or positive feedback loop.

[0067] Several examples allow for fault detection or cause of tool downtime. For instance, calibration or wafer placement errors, under the correlation and matching techniques described herein, could actually, more precisely, be due to the presence of obstacles / people in the access zone causing signal disruption, as opposed to suspected (but false) faults of the placement robot itself.

[0068] In some embodiments, a system is provided for sensor-assisted signal calibration of a semiconductor processing tool. The system includes a sensor array comprising a plurality of distance sensors (e.g., TOF sensor 936 in Figure 9). The plurality of distance sensors are located within an access zone of the semiconductor processing tool (e.g., access zone 928). The system further includes a controller (e.g., wireless connection controller 320) communicatively coupled to the sensor array. The controller is configured to decode a plurality of corresponding sensor measurements received from the plurality of distance sensors. The controller is further configured to detect the presence of an interfering object within the access zone of the semiconductor processing tool based on the corresponding sensor measurements. The controller is further configured to detect a deviation of at least one signal characteristic of the wireless signal from a pre-configured value, as the wireless signal propagates through the access zone. The deviation is detected while the interfering object is present within the access zone. The controller is further configured to perform mitigation measures associated with the wireless signal based on the detection of the deviation.

[0069] In some embodiments, in order to implement mitigation measures, the controller is further configured to adjust at least one signal characteristic of the wireless signal to reach a pre-configured value.

[0070] In some embodiments, in order to implement mitigation measures, the controller is further configured to generate a notification of the presence of a nuisance object detected within the access zone and to trigger the communication of the notification within the access zone.

[0071] In some embodiments, the range-measuring sensors include at least a first two-dimensional (2D) light-detecting and ranging (LIDAR) sensor, a second 2D LIDAR sensor, and a third 2D LIDAR sensor, all located within an access zone.

[0072] In some embodiments, the controller is further configured to decode a plurality of corresponding sensor measurements in order to determine measurements from a first 2D LIDAR sensor, a second 2D LIDAR sensor, and a third 2D LIDAR sensor.

[0073] In some embodiments, the measurement includes a first distance from a first 2D LIDAR sensor to the interfering object, a second distance from a second 2D LIDAR sensor to the interfering object, and a third distance from a third 2D LIDAR sensor to the interfering object.

[0074] In some embodiments, the controller is further configured to determine the spatial coordinates of an interfering object within the access zone based on a first distance, a second distance, and a third distance. The controller is further configured to trigger a rerouting of the wireless signal based on the spatial coordinates of the interfering object.

[0075] In some embodiments, the range sensors include three-dimensional (3D) light detection and ranging (LIDAR) sensors, and the controller is further configured to decode the corresponding sensor measurements to determine the measurement from the 3D LIDAR sensor.

[0076] In some embodiments, the controller is further configured to determine the spatial coordinates of interfering objects within the access zone based on measurements from a 3D LiDAR sensor, and to trigger a rerouting of the wireless signal based on the spatial coordinates of the interfering objects.

[0077] In some embodiments, the controller is further configured to detect if the deviation of at least one signal characteristic of the wireless signal from a pre-configured value is greater than a threshold, and to correct at least one process performed by a semiconductor processing tool based on the detection that the deviation is greater than the threshold.

[0078] In some embodiments, the access zone is at least partially defined by the walls of the vacuum transfer module (VTM) and instrument front-end module (EFEM) of the semiconductor processing tool.

[0079] In some embodiments, the controller is further configured to decode a plurality of corresponding sensor measurements to determine wireless connectivity parameters associated with a semiconductor processing tool. The wireless connectivity parameters include one or more of the following: wireless frequency, wireless channel, wireless signal strength, wireless coverage area, and wireless signal dead zone.

[0080] In some embodiments, the controller is further configured to generate a correlation between wireless connection parameters and the detected presence or movement of an interfering object within the access zone. The controller is further configured to generate instructions associated with at least one process performed by a semiconductor processing tool, the instructions being based on the correlation.

[0081] In some embodiments, the controller is further configured to perform fine-tuning of the wafer calibration routine or placement routine of the semiconductor processing tool based on instructions.

[0082] In some embodiments, the controller is further configured to generate a correlation between wireless connection parameters and embodiments of the components of a semiconductor processing tool, and to generate instructions associated with at least one process performed by the semiconductor processing tool, the instructions being based on the correlation.

[0083] In some embodiments, the components of a semiconductor processing tool include one or more groups of components, including a vacuum transfer module (VTM), an equipment front-end module (EFEM), a load lock, a process module, a transfer robot, a loading station, an EFEM rear wall, a VTM top wall, a wafer handling robot, a vacuum transfer robot, an external storage buffer, an internal storage buffer, an automatic calibration controller, a wireless connectivity controller, and an end effector for the wafer handling robot.

[0084] In some embodiments, the configuration of the components of the semiconductor processing tool includes one or more of a group of configurations, including the location of the components, the dimensions or size of the components, the number of components, the configuration of the components, and the tool density, which is affected by the configuration of the components.

[0085] Figure 7A shows a method 700A for sensor-assisted signal calibration of a semiconductor processing tool according to an exemplary embodiment. In operation 702A, multiple sensor measurements received from at least one ranging sensor of a sensor array associated with the semiconductor processing tool are decoded. In operation 704A, the presence of an interfering object within the access zone of the semiconductor processing tool is detected based on the multiple sensor measurements. In operation 706A, a deviation of at least one signal characteristic of the wireless signal from a pre-configured value is detected. In operation 708A, a correlation is generated based on the deviation of at least one signal characteristic of the wireless signal and the presence of an interfering object. In operation 710A, mitigation measures associated with the wireless signal are performed at least in part based on the correlation.

[0086] In some embodiments, the implementation of mitigation measures further includes adjusting at least one signal characteristic of the wireless signal to reach a pre-configured value.

[0087] In some embodiments, the implementation of mitigation measures further includes generating a notification of the presence of a nuisance object detected within the access zone and inducing the communication of the notification within the access zone.

[0088] In some embodiments, the distance sensor includes at least one distance sensor, at least one first two-dimensional (2D) light detection and distance measuring (LIDAR) sensor, a second 2D LIDAR sensor, and a third 2D LIDAR sensor, all located within the access zone.

[0089] In some embodiments, method 700A further includes decoding a plurality of sensor measurements to determine measurements from a first 2D LIDAR sensor, a second 2D LIDAR sensor, and a third 2D LIDAR sensor.

[0090] In some embodiments, the measurements include a first distance from a first 2D LiDAR sensor to the interfering object, a second distance from a second 2D LiDAR sensor to the interfering object, and a third distance from a third 2D LiDAR sensor to the interfering object.

[0091] In some embodiments, method 700A further includes determining the spatial coordinates of an interfering object within an access zone based on a first distance, a second distance, and a third distance. In some embodiments, method 700A further includes causing a rerouting of the wireless signal based on the spatial coordinates of the interfering object.

[0092] In some embodiments, the multiple distance sensors include three-dimensional (3D) light detection and ranging (LIDAR) sensors. In some embodiments, method 700A further includes decoding the multiple sensor measurements to determine the measurement from the 3D LIDAR sensor.

[0093] In some embodiments, method 700A further includes determining the spatial coordinates of an interfering object within an access zone based on measurements from a 3D LiDAR sensor, and causing a rerouting of the wireless signal based on the spatial coordinates of the interfering object.

[0094] In some embodiments, method 700A further includes detecting that the deviation of at least one signal characteristic of a wireless signal from a pre-configured value is greater than a threshold, and correcting at least one process performed by a semiconductor processing tool based on the detection that the deviation is greater than a threshold.

[0095] Figure 7B shows a method 700B, according to an exemplary embodiment, that assists in the calibration of a wafer handling robot for semiconductor processing tools. In operation 702A, method 700B provides an autocalibration wafer, which is sized to be transported by a wafer handling robot and includes a substrate having a first surface configured to contact the end effector of the wafer handling robot when the substrate is transported by the wafer handling robot, and a plurality of imaging sensors supported by the substrate, each imaging sensor having a downward field of view when the substrate is oriented with the first surface facing downward.

[0096] In operation 704B, method 700B connects an autocalibration controller to each of the multiple imaging sensors in a communicative manner. In operation 706B, method 700B determines an access zone within a semiconductor processing tool. In operation 708B, method 700B positions time-of-flight ranging sensors to detect the presence of a person or object located within the access zone or to track their movement. In operation 710B, method 700B provides a wireless connectivity controller and connects the wireless connectivity controller to the autocalibration controller and the time-of-flight ranging sensors in a communicative manner. In operation 712B, method 700B transmits commands from the wireless connectivity controller to the autocalibration wafer or components of the semiconductor processing tool.

[0097] In some examples, the time-of-flight ranging sensor is a LiDAR sensor. In some examples, the access zone is at least partially defined by the walls of the vacuum transfer module (VTM) and instrument front-end module (EFEM) of the semiconductor processing tool. In some examples, method 700B further includes transmitting presence detection or movement data captured by the time-of-flight ranging sensor to a wireless connection controller, and processing the wireless connection parameters of the semiconductor processing tool in the wireless connection controller.

[0098] In some examples, the wireless connectivity parameters include one or more of a group of wireless connectivity parameters, including wireless frequency, wireless channel, wireless signal strength, wireless coverage area, and wireless dead zone. In some examples, Method 700B further includes configuring a wireless connectivity controller to generate a correlation between the wireless connectivity parameters and the detected presence or tracked movement of a person or object within an access zone.

[0099] In some examples, Method 700B further includes configuring a wirelessly connected controller to send commands to an autocalibration controller based on correlation. In some examples, Method 700B further includes fine-tuning a wafer calibration or placement routine based on commands.

[0100] In some examples, Method 700B further includes configuring a wireless connection controller to generate a correlation between wireless connection parameters and embodiments of components of a semiconductor processing tool.

[0101] In some examples, the components of a semiconductor processing tool include one or more groups of components, such as a vacuum transfer module (VTM), an equipment front-end module (EFEM), a load lock, a process module, a transfer robot, a loading station, an EFEM rear wall, a VTM top wall, a wafer handling robot, a vacuum transfer robot, an external storage buffer, an internal storage buffer, an autocalibration controller, a wireless connectivity controller, and an end effector for the wafer handling robot.

[0102] In some examples, the configuration of components of a semiconductor processing tool includes one or more of a group of configurations, including the location of the components, the dimensions or size of the components, the number of components, the configuration of the components, and the tool density, which is affected by the configuration of the components.

[0103] Figure 8 is a block diagram showing an example of a machine or controller 800 in which one or more exemplary embodiments described herein may be implemented or controlled. In alternative embodiments, the controller 800 may operate as a standalone device or be connected to other machines (e.g., networked). In a networked configuration, the controller 800 may operate as a server machine, a client machine, or both in a server-client network environment. In one example, the controller 800 may function as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. Furthermore, although only a single controller 800 is shown, the term “machine” (controller) should also be interpreted to include any set of machines (controllers) that individually or collectively execute a set of instructions to perform any one or more of the methods described herein, via cloud computing, software-as-a-service (SaaS), or other computer cluster configurations, etc. In some examples, referring to Figure 8, a non-temporary machine-readable medium, when read by the controller 800, includes an instruction 824 that causes the controller to control an operation in a method that includes at least the non-limiting exemplary operations described herein.

[0104] In some examples, a non-temporary computer-readable storage medium, when executed by a computer, includes instructions that cause the computer to communicate with an autocalibration controller to communicate with each of several imaging sensors on an autocalibration wafer, to communicate with a time-of-flight ranging sensor to detect the presence of a person or object located in an access zone within a semiconductor processing tool or to track its movement, to handle communication between a wireless connection controller and the autocalibration controller and the time-of-flight ranging sensor, and to cause the computer to send commands from the wireless connection controller to components of the autocalibration wafer or semiconductor processing tool.

[0105] Examples may include, or be operated by, logic, several components or mechanisms, as described herein. A circuit configuration is a collection of circuits implemented on a tangible entity that includes hardware (e.g., simple circuits, gates, logic, etc.). Components of a circuit configuration may be flexible with respect to changes in time and the underlying hardware. A circuit configuration includes components that can perform specified operations, either individually or in combination. In one example, the hardware of a circuit configuration may be designed immutably (e.g., hardwired) to perform a particular operation. In another example, the hardware of a circuit configuration may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) that include a computer-readable medium that is physically modified (e.g., magnetically, electrically, by the movable arrangement of immutable mass particles, etc.) to encode instructions for a particular operation. When connecting physical components, the underlying electrical properties of the hardware components are changed (e.g., from insulator to conductor, or vice versa). Instructions enable embedded hardware (e.g., an execution unit or loading mechanism) to create components of a circuit configuration within the hardware via variable connections to perform a specific part of an operation during operation. Thus, computer-readable media are communicatively coupled to other components of the circuit configuration while the device is operating. In one example, any one of the physical components may be used by two or more components of two or more circuit configurations. For example, during operation, an execution unit may be used at one point in a first circuit of a first circuit configuration and then reused at a different point in time by a second circuit of the first circuit configuration or a third circuit of the second circuit configuration.

[0106] The machine (e.g., computer system) controller 800 may include a hardware processor 802 (e.g., a central processing unit (CPU), a hardware processor core, or any combination thereof), a graphics processing unit (GPU) 832 (graphics processing unit), main memory 804, and static memory 806, some or all of which can communicate with each other via an interlink 818 (e.g., a bus). The controller 800 may further include a display device 808, an alphanumeric input device 810 (e.g., a keyboard), and a user interface (UI) navigation device 812 (e.g., a mouse or other user interface). In one example, the display device 808, the alphanumeric input device 810, and the UI navigation device 812 may be touchscreen displays. The controller 800 may further include a mass storage device 814 (e.g., a drive unit), a signal generation device 816 (e.g., a speaker), a network interface device 820, and one or more sensors 830 such as a Global Positioning System (GPS) sensor, a compass, an accelerometer, or another sensor. The controller 800 may include an output controller 828, such as a serial (e.g., Universal Serial Bus (USB)), parallel, or other wired or wireless (e.g., infrared (IR), near-field communication (NFC)) connection, for communicating with or controlling one or more peripheral devices (e.g., printers, card readers, etc.).

[0107] The mass storage device 814 may include a machine-readable medium 822 in which one or more sets of data structures or instructions 824 (e.g., software) that embody or are utilized by any one or more of the techniques or functions described herein are stored. The instructions 824 may also reside entirely or at least partially in the main memory 804, static memory 806, hardware processor 802, or GPU 832 during their execution by the controller 800, as illustrated. In one example, one or any combination of the hardware processor 802, GPU 832, main memory 804, static memory 806, or mass storage device 814 may constitute the machine-readable medium 822.

[0108] Although the machine-readable medium 822 is shown as a single medium, the term “machine-readable medium” may include a single medium or multiple mediums configured to store one or more instructions 824 (e.g., a centralized or distributed database, and / or associated caches and servers).

[0109] The term “machine-readable medium” may include any medium that can store, encode, or carry instructions 824 for execution by the controller 800, causing the controller 800 to execute one or more of the techniques of the Disclosure, or that stores, encodes, or carries data structures used by or associated with such instructions 824. Examples of non-limiting machine-readable mediums may include solid-state memory, as well as optical and magnetic media. In one example, an aggregated machine-readable medium includes a machine-readable medium 822 having a plurality of particles having constant (e.g., stationary) mass. Thus, an aggregated machine-readable medium is not a transient propagating signal. Specific examples of aggregated machine-readable mediums may include non-volatile memory such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, as well as CD-ROM and DVD-ROM disks. Instruction 824 may also be transmitted or received over the communication network 826 using a transmission medium via the network interface device 820.

[0110] Figure 9 shows a plan view of an exemplary system for characterizing a wireless environment 934 around a semiconductor processing tool 902 according to one embodiment of the present disclosure. The semiconductor processing tool 902 includes an EFEM 904, a load lock 906, a processing module 908, a VTM 910, a transfer robot 912 for the EFEM 904 (located between the front and rear walls 916 of the EFEM 904 and the loading station 914), a vacuum transfer robot 918, one or more external storage buffers 920, and one or more internal storage buffers 922. Although shown to have one or two arms, each of the vacuum transfer robots 918 may have a configuration including one, two, or more arms. In some examples, the vacuum transfer robots 918 may include two end effectors 924 on each arm, as shown in Figure 9.

[0111] In some examples, the wireless environment 934 includes a wireless router 932 attached to the EFEM 904, and wireless signals communicated through the wireless router 932 can pass through the access zone 928. The wireless environment 934 may also include other moving or stationary sensors wirelessly connected to one another. For example, the moving sensors or set of moving sensors in the wireless environment 934 may include one or more APS wafers 930, as described above herein.

[0112] In some embodiments, the wireless environment 934 may include fixed sensors such as the wireless environment sensor 926, a material sensor 938, and one or more TOF sensors such as TOF sensors 936A, 936B, 936C, 936D, 936E, and 936F (collectively, TOF sensor 936). Even when Figure 9 shows the wireless environment 934 including a single wireless environment sensor, a single material sensor, and six TOF sensors, the disclosure is not limited thereto, and other sets and / or arrangements of fixed and moving sensors are possible based on desired sensor coverage, space constraints, or implementation considerations.

[0113] In some embodiments, the wireless environment sensor 926 has appropriate circuit configuration, logic, interface, and / or code and is configured to measure radio frequency (RF) noise in the wireless environment 934. In some embodiments, the wireless environment sensor 926 is located in the vicinity of the access zone 928 (e.g., within a pre-configured distance from it). The wireless connection controller 320 (or another controller used by the semiconductor processing tool 902) can use sensor data from the wireless environment sensor 926 to detect the presence (or change thereof) of RF interference in the access zone 928, as well as the presence of other wireless devices or sensors. In some embodiments, if the detected RF interference exceeds a threshold level, mitigation measures can be taken. Exemplary mitigation measures include generating a notification of the elevated RF interference level and adjusting one or more wireless signal characteristics of the signal being communicated through the access zone 928 (e.g., increasing signal power, adjusting signal modulation, adjusting antenna directivity, etc.).

[0114] In some embodiments, the material sensor 938 is configured to have appropriate circuit configuration, logic, interfaces, and / or code to detect objects within its FoV and to perform material analysis to determine one or more materials from which the detected object is composed. In some embodiments, the material sensor 938 is configured to perform spectroscopic analysis to detect the material composition of the object(s) within the FoV.

[0115] In some embodiments, the TOF sensor 936 is similar to one or more sensors 126, one or more sensors 316, or one or more sensors 406 described herein. In some embodiments, at least three of the TOF sensors 936 may be two-dimensional LiDAR sensors and may be positioned at pre-configured distances from the access zone 928. For example, TOF sensors 936A, 936B, and 936E may be two-dimensional LiDAR sensors that can be used to detect interfering objects within the access zone 928 and generate sensor measurements that can be used to determine corresponding distances between each sensor and the interfering object. The corresponding distances can be used (for example, by the wireless connection controller 320 or another controller used by the semiconductor processing tool 902) to determine the spatial coordinates of the interfering object (for example, when the interfering object is stationary), as well as the movement path and trajectory coordinates (for example, when the interfering object is moving within the access zone 928). Such information is then used to implement mitigation measures, which may include generating notifications, adjusting one or more wireless signal characteristics of wireless signals communicated taking access zone 928, recalibrating or reconfiguring at least one process performed by semiconductor processing tool 902 (e.g., a process based on wireless signals affected by the presence / movement of interfering objects in access zone 928), fault analysis, optimizing wireless connectivity, and fine-tuning substrate processing parameters.

[0116] In some embodiments, at least one of the TOF sensors 936 (e.g., TOF sensor 936B) may be a 3D LiDAR sensor and may be positioned at a pre-configured distance from the access zone 928. For example, TOF sensor 936B may be used to detect interfering objects within the access zone 928 and generate sensor measurements including the spatial coordinates of the detected interfering objects.

[0117] In some embodiments, one or more of the TOF sensors 936A to 936F are positioned near the access zone (e.g., TOF sensors 936A, 936B, and 936E) as well as near the surrounding area of ​​the semiconductor processing tool 902. For example, TOF sensors 936C, 936D, 936B, 936E, and 936F can also be used to detect the presence of interfering objects in the surrounding area around the semiconductor processing tool 902, which is outside the access zone 928 but within the wireless environment 934. If an interfering object is detected in such a surrounding area, corresponding mitigation measures (or a combination of measures) can be taken.

[0118] In some embodiments, the moving and stationary sensors within the wireless environment 934 described above can be networked together (e.g., as part of a wired or wireless network), and as a result, sensor data from multiple different sensors can be continuously (or periodically) monitored to evaluate the presence of interfering objects or changes in the characteristics of one or more wireless signals traversing the wireless environment 934 used by the semiconductor processing tool 902. Corresponding mitigation measures (or measures) can be taken based on sensor inputs from one or more of the networked sensors within the wireless environment 934. In some embodiments, the networked sensors can provide information such as tool configuration and access permissions for personnel and / or objects to optimize the wireless connectivity within the wireless environment 934, which may affect the propagation of desired wireless signals. This information may be used once during tool setup or may be required before all wireless sensor data collection periods.

[0119] The systems and methods based on the principles of this disclosure provide various configurations of processing tools and sensors to maximize the wireless connectivity of processing tool components. It will be understood that any interruption of the wireless connectivity between wirelessly connected components and an autocalibrated wafer can have significant adverse effects. For example, the presence of personnel and interfering foreign objects positioned in the path between the semiconductor processing tool and the autocalibrated wafer can interfere with the wireless connectivity and cause failures in calibration and placement. The provision of wireless environment sensors 926 and other sensors, as described above, enables insight into this interference, allows for characterization of the wireless environment 934 and analysis based on fault information, leading to improvements in calibration and placement routines as well as improvements in manufacturing room configuration and tool density.

[0120] With respect to Figure 10, in some implementations, the wireless environment controller 1002 receives input from one or more environment devices and / or sensors, as illustrated. The devices and / or sensors may include the wireless router 1006, the wireless environment sensor 1008, the fixed sensor 1010, and the mobile sensor 1012. These sensors may or may not be wireless themselves, but some examples are sensors that characterize the wireless environment around or part of a semiconductor processing tool, such as the wireless environment 934 of the semiconductor processing tool 902 in Figure 9. The wireless environment controller 1002 communicates with the wireless router 1006, the mobile sensor 1012 such as the APS, and the system computer 1004 such as the system computer. Some examples of interaction include the wireless environment controller 1002 instructing the system computer 1004 to delay the execution of the APS until wireless environment parameters meet a certain level. Another example of interaction is the wireless environment controller 1002 instructing the wireless router 1006 to increase the transmit power, change the wireless channel, or change the signal modulation. The wireless environment controller 1002 can notify the moving sensor 1012 (such as an APS) or the stationary sensor 1010 to send smaller information packets to its wireless controller, or delay sending information until it moves to a location within the less-interfering wireless environment 934. In some examples, the wireless router 1006 can also function as a wireless environment sensor 1008.

[0121] In some embodiments, the wireless environment controller 1002 is implemented as part of a semiconductor processing tool. In other embodiments, the wireless environment controller 1002 is an off-the-tool controller that can be configured as a standalone device or as part of another computing device (e.g., a field technician's tablet or smartphone).

[0122] While examples are given with respect to specific exemplary embodiments or methods, it will be apparent that various modifications and changes may be made to these embodiments without departing from a broader range of embodiments. Therefore, this specification and the drawings should be noted as illustrative, not restrictive. The accompanying drawings, forming part of this specification, illustrate, not restrictive, specific embodiments in which the subject matter may be practiced. The illustrated embodiments are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived from them so that structural and logical substitutions and modifications may be made without departing from the scope of this disclosure. Therefore, the embodiments for carrying out this invention should not be construed as restrictive, and the scope of various embodiments, together with the entire scope of equivalents to which the accompanying claims are entitled, is defined only by such claims.

[0123] Such embodiments of the subject matter of the present invention may be referred to herein individually and / or collectively by the term “invention” for convenience only, and without the intention to spontaneously limit the scope of this application to any single invention or inventive concept if more than one is disclosed. Therefore, while specific embodiments are illustrated and described herein, it should be understood that any configuration intended to achieve the same objective may be used instead of the specific embodiments illustrated. This disclosure is intended to cover any adaptations or variations of various embodiments. Combinations of the above embodiments and other embodiments not expressly described herein will be apparent to those skilled in the art upon consideration of the above description. [Explanation of Symbols]

[0124] 102 Semiconductor processing tool, 104 Equipment front-end module (EFEM), 106 Load lock, 108 Process module, 110 Vacuum transfer module (VTM), 112 Transfer robot, 114 Loading station, 116 Rear wall, 118 Vacuum transfer robot, 120 External storage buffer, 122 Internal storage buffer, 124 End effector, 126 Sensor, 128 Access zone, 130 APS wafer, 132 Controller, 134 Wireless router, 202 First wafer station, 204 Second wafer station, 206 Auto-calibration wafer, 208 Wafer support, 210 AWC system, 212 Wafer handling robot, 214 Edge ring, 216 End effector, 218 Controller, 302 Semiconductor processing tool, 304 EFEM, 306 Load lock, 308 Process module, 310 VTM, 312 Loading station, 314 rear wall, 316 sensor, 318 access zone, 320 wireless connection controller, 322 remote wireless router, 324 sensor position, 402 EFEM, 404 VTM, 406 sensor, 408 rear wall, 410 top, 412 VTM length, 414 field of view, 416 access zone, 502 personnel detection graph, 504 graph zone, 506 graph zone, 508 graph zone, 510 graph zone, 512 graph zone, 514 graph point, 516 graph point, 518 person, 520 ladder, 602 interfering object detection graph, 604 interfering object detection graph, 606 interfering object detection graph, 608 "all quiet" graph, 610 object, 612 graph point, 614 graph zone, 615 graph zone, 616 graph point, 618 Graph zone, 620 Graph zone, 622 Graph zone, 624 Graph point, 626 Graph zone, 628 Vertical graph portion, 630 Graph point, 631 Graph zone, 632 Graph zone, 634 Graph zone, 636 Graph point, 638 Graph zone, 640 Graph point, 641 Graph zone, 642 Graph zone, 643 Graph zone, 644 Graph zone, 645 Graph zone, 646 Graph zone, 647 Graph zone, 648 Graph zone, 649 Graph zone, 650Wireless signal strength graph, 700A method, 702A operation, 704A operation, 706A operation, 708A operation, 710A operation, 700B method, 704B operation, 706B operation, 708B operation, 710B operation, 712B operation, 800 controller, 802 hardware processor, 804 main memory, 806 static memory, 808 display device, 810 alphanumeric input device, 812 UI navigation device, 814 mass storage device, 816 signal generation device, 818 interlink, 820 network interface device, 822 machine-readable media, 824 instructions, 826 communication network, 828 output controller, 830 sensor, 832 graphics processing unit (GPU), 902 semiconductor processing tool, 904 EFEM, 906 load lock, 908 Processing module, 910 VTM, 912 Transfer robot, 914 Loading station, 916 Rear wall, 918 Vacuum transfer robot, 920 External storage buffer, 922 Internal storage buffer, 924 End effector, 926 Wireless environment sensor, 928 Access zone, 930 APS wafer, 932 Wireless router, 934 Wireless environment, 936 TOF sensor, 936A TOF sensor, 936B TOF sensor, 936C TOF sensor, 936D TOF sensor, 936E TOF sensor, 936F TOF sensor, 938 Material sensor, 1002 Wireless environment controller, 1004 System computer, 1006 Wireless router, 1008 Wireless environment sensor, 1010 Fixed sensor, 1012 Moving sensor

Claims

1. A system for sensor-assisted signal calibration of semiconductor processing tools, wherein the system is A sensor array comprising at least one distance measuring sensor, wherein the at least one distance measuring sensor is located within the access zone of the semiconductor processing tool, A controller that is communicatively coupled to the sensor array, wherein the controller Decode the multiple sensor measurements received from the at least one distance measuring sensor. Based on the multiple sensor measurements, the presence of an interfering object in the access zone of the semiconductor processing tool is detected. Detect the deviation of at least one signal characteristic of the wireless signal from a pre-configured value, A correlation is generated based on the deviation of the at least one signal characteristic of the wireless signal and the presence of the interfering object. Based at least partially on the aforementioned correlation, mitigation measures associated with the wireless signal are implemented. A controller and A system that includes these features.

2. In order to implement the aforementioned mitigation measures, the controller, Adjust the at least one signal characteristic of the wireless signal to reach the pre-configured value. The system according to claim 1, further configured as follows.

3. In order to implement the aforementioned mitigation measures, the controller, Generate a notification of the presence of the interfering object detected within the access zone. The notification communication within the aforementioned access zone The system according to claim 1, further configured as follows.

4. The system according to claim 1, wherein the at least one distance measuring sensor includes at least a first two-dimensional (2D) light detection and distance measuring (LIDAR) sensor, a second 2D LIDAR sensor, and a third 2D LIDAR sensor, all located within the access zone.

5. The aforementioned controller To determine the measured values ​​from the first 2D LiDAR sensor, the second 2D LiDAR sensor, and the third 2D LiDAR sensor, the multiple sensor measurements are decoded. The system according to claim 4, further configured as follows.

6. The aforementioned measurement values ​​are The first distance from the first 2D LIDAR sensor to the interfering object, The second distance from the second 2D LiDAR sensor to the interfering object, and The third distance from the third 2D LiDAR sensor to the interfering object. The system according to claim 5, including the above.

7. The aforementioned controller Based on the first distance, the second distance, and the third distance, the spatial coordinates of the obstructing object within the access zone are determined. Based on the spatial coordinates of the interfering object, cause the wireless signal to be rerouted. The system according to claim 6, further configured as follows.

8. The at least one distance measuring sensor includes a three-dimensional (3D) light detection and distance measuring (LIDAR) sensor, and the controller is To determine the measurement values ​​from the 3D LiDAR sensor, the multiple sensor measurement values ​​are decoded. The system according to any one of claims 1 to 7, further configured as follows.

9. The aforementioned controller Based on the measurements from the 3D LiDAR sensor, the spatial coordinates of the interfering object within the access zone are determined. Based on the spatial coordinates of the interfering object, cause the wireless signal to be rerouted. The system according to claim 8, further configured as follows.

10. The aforementioned controller It is detected that the deviation of at least one signal characteristic of the wireless signal from the pre-configured value is greater than a threshold. Based on the detection that the deviation is greater than the threshold, at least one process performed by the semiconductor processing tool is modified. The system according to any one of claims 1 to 7, further configured as follows.

11. The system according to any one of claims 1 to 7, wherein the access zone is at least partially defined by the walls of the vacuum transfer module (VTM) and the equipment front-end module (EFEM) of the semiconductor processing tool.

12. The aforementioned controller Decoding the plurality of sensor measurements to determine wireless connection parameters associated with the semiconductor processing tool, wherein the decoding includes one or more of the following: wireless frequency, wireless channel, wireless signal strength, wireless coverage area, and wireless signal dead zone. The system according to any one of claims 1 to 7, further configured to perform the following:

13. The aforementioned controller To generate a correlation between the wireless connection parameters and the detected presence or movement of the interfering object within the access zone, To generate instructions associated with at least one process executed by the semiconductor processing tool, wherein the instructions are generated based on the correlation. The system according to claim 12, further configured to perform the following:

14. The aforementioned controller Based on the aforementioned instruction, the wafer calibration routine or placement routine of the semiconductor processing tool is fine-tuned. The system according to claim 13, further configured as follows.

15. The aforementioned controller To generate a correlation between the wireless connection parameters and the configuration of the semiconductor processing tool components, To generate instructions associated with at least one process executed by the semiconductor processing tool, wherein the instructions are generated based on the correlation. The system according to claim 12, further configured to perform the following:

16. The system according to claim 15, wherein the components of the semiconductor processing tool include one or more of a group of components, including a vacuum transfer module (VTM), an equipment front-end module (EFEM), a load lock, a process module, a transfer robot, a loading station, an EFEM rear wall, a VTM top wall, a wafer handling robot, a vacuum transfer robot, an external storage buffer, an internal storage buffer, an automatic calibration controller, a wireless connectivity controller, and an end effector for the wafer handling robot.

17. The system according to claim 16, wherein the embodiments of the components of the semiconductor processing tool include one or more of a group of embodiments, each comprising the position of the components, the dimensions or size of the components, the number of components, the configuration of the components, and the tool density affected by the embodiments of the components.

18. A method for calibrating sensor-assisted signals of a semiconductor processing tool, wherein the method is The steps include decoding a plurality of sensor measurements received from at least one distance measuring sensor of a sensor array associated with the semiconductor processing tool, The steps include detecting the presence of an interfering object within the access zone of the semiconductor processing tool based on the multiple sensor measurements, A step of detecting a deviation of at least one signal characteristic of a wireless signal from a pre-configured value, A step of generating a correlation based on the deviation of the at least one signal characteristic of the wireless signal and the presence of the interfering object, A step of performing mitigation measures associated with the wireless signal, at least in part based on the correlation, Methods that include...

19. The step of implementing the aforementioned mitigation measures is, Steps to adjust the at least one signal characteristic of the wireless signal to reach the pre-configured value. The method according to claim 18, further comprising:

20. The step of implementing the aforementioned mitigation measures is, The steps include generating a notification of the presence of the interfering object detected within the access zone, The steps of causing the notification communication within the access zone and The method according to claim 18, further comprising:

21. The method according to claim 18, wherein the at least one distance measuring sensor includes at least a first two-dimensional (2D) light detection and distance measuring (LIDAR) sensor, a second 2D LIDAR sensor, and a third 2D LIDAR sensor, all located within the access zone.

22. The step of decoding the multiple sensor measurements in order to determine the measurements from the first 2D LiDAR sensor, the second 2D LiDAR sensor, and the third 2D LiDAR sensor. The method according to claim 21, further comprising:

23. The aforementioned measurement values ​​are The first distance from the first 2D LIDAR sensor to the interfering object, The second distance from the second 2D LiDAR sensor to the interfering object, and The third distance from the third 2D LiDAR sensor to the interfering object. The method according to claim 22, including the method described in claim 22.

24. A step of determining the spatial coordinates of the obstructing object within the access zone based on the first distance, the second distance, and the third distance, A step of causing the wireless signal to be rerouted based on the spatial coordinates of the interfering object. The method according to claim 23, further comprising:

25. The at least one distance measuring sensor includes a three-dimensional (3D) light detection and distance measuring (LIDAR) sensor, and the method is The step of decoding the multiple sensor measurements in order to determine the measurement values ​​from the 3D LiDAR sensor. The method according to any one of claims 18 to 24, further comprising:

26. The steps include determining the spatial coordinates of the obstructing object within the access zone based on the measured values ​​from the 3D LiDAR sensor, A step of causing the wireless signal to be rerouted based on the spatial coordinates of the interfering object. The method according to claim 25, further comprising:

27. A step of detecting that the deviation of at least one signal characteristic of the wireless signal from the pre-configured value is greater than a threshold, A step of correcting at least one process performed by the semiconductor processing tool based on the detection that the deviation is greater than the threshold, The method according to any one of claims 18 to 24, further comprising:

28. A method for calibrating sensor-assisted signals of a semiconductor processing tool, wherein the method is A step of providing an automatically calibrated wafer, the automatically calibrated wafer comprising: a substrate having a size that can be transported by a wafer handling robot and having a first surface configured to contact the end effector of the wafer handling robot when the substrate is transported by the wafer handling robot; and a plurality of imaging sensors supported by the substrate, each imaging sensor having a downward field of view when the substrate is oriented such that the first surface faces downward; The steps include: connecting an automatic calibration controller to each of the plurality of imaging sensors in a manner that enables communication; The steps include determining the access zone within the semiconductor processing tool, The steps include: positioning a time-of-flight ranging sensor to detect the presence of a person or object located within the access zone, or to track its movement; The steps include providing a wireless connection controller and connecting the wireless connection controller to the automatic calibration controller and the time-of-flight ranging sensor in a communicative manner, The steps include: transmitting a command from the wireless connection controller to the automatic calibration wafer or the components of the semiconductor processing tool; Methods that include...

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