Systems and methods for wafer shape classification using machine learning for wire saw process control

Abstract

A computer device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: a) receive a plurality of historical images of objects; b) train a model to classify the plurality of historical images; c) receive scan data of an object to be analyzed; d) execute the model to determine a category for the object to be analyzed; and e) determine whether or not to approve the object based on the category for the object.

Description

SYSTEMS AND METHODS FOR WAFER SHAPE CLASSIFICATION USING MACHINE LEARNING FOR WIRE SAW PROCESS CONTROLCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 676,541, filed July 29, 2024, which application is hereby incorporated by reference in its entirety.FIELD

[0002] This disclosure relates to system and methods for shape analysis of semiconductor wafers and, more particularly, to systems and methods implementing a classification model for wafer shape detection during a wire saw process step.BACKGROUND

[0003] Semiconductor wafers, such as silicon wafers, are commonly used as substrates in the production of integrated circuit (IC) chips. Chip manufacturers require wafers that have extremely flat and parallel surfaces to ensure that a maximum number of chips can be fabricated from each wafer. After being sliced from an ingot, wafers typically undergo grinding and polishing processes designed to improve certain surface features, such as flatness and parallelism.

[0004] The shape of a silicon wafer plays a crucial role in fabrication processes and product yield. Ideally, the wafer should be a perfectly round and flat disk of uniform thickness with the edges rounded to the desired profile. However, due to deformations and thickness variations, the wafer deviates from this ideal shape. Wafer flatness, which refers to the variation of wafer thickness relative to a reference plane, is an important characteristic to consider.

[0005] Accordingly, a system to enable shape analysis of semiconductor wafers is needed.

[0006] This Background section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and / or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.BRIEF DESCRIPTION

[0007] In one aspect, a system includes a computing device that may include at least one processor in communication with at least one memory device. The at least one processor may be configured to: a) receive a plurality of historical images of objects; b) train a model to classify the plurality of historical images; c) receive scan data of an object to be analyzed; d) execute the model to determine a category for the object to be analyzed; and e) determine whether or not to approve the object based on the category for the object. The system may have additional, less, or alternate functionalities, including those discussed elsewhere herein.

[0008] In another aspect, a computer-implemented method may be performed by a computer device including at least one processor in communication with at least one memory device. The method may include a) receiving a plurality of historical images of objects; b) training a model to classify the plurality of historical images; c) receiving scan data of an object to be analyzed; d) executing the model to determining a category for the object to be analyzed; and e) determining whether or not to approve the object based on the category for the object. The method may have additional, less, or alternate functionalities, including those discussed elsewhere herein.

[0009] In a further aspect, a computer device includes at least one processor in communication with at least one memory device. The at least one processor may be configured to: a) receive a plurality of historical images of objects;b) train a model to classify the plurality of historical images; c) receive scan data of an object to be analyzed; d) execute the model to determine a category for the object to be analyzed; and e) determine whether or not to approve the object based on the category for the object. The computer device may have additional, less, or alternate functionalities, including those discussed elsewhere herein.

[0010] In another aspect, at least one non-transitory computer- readable media having computer-executable instructions embodied thereon, when executed by a computing device including at least one processor in communication with at least one memory device, the computer-executable instructions may cause the at least one processor to: a) receive a plurality of historical images of objects; b) train a model to classify the plurality of historical images; c) receive scan data of an object to be analyzed; d) execute the model to determine a category for the object to be analyzed; and e) determine whether or not to approve the object based on the category for the object. The non-transitory computer-readable media may have additional, less, or alternate functionalities, including those discussed elsewhere herein.

[0011] Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The Figures described below depict various aspects of the systems and methods disclosed. Each Figure depicts an embodiment of a particular aspect of the disclosed systems and methods, and that each of the Figures is intended to accord with a possible embodiment. Further, wherever possible, the following description refers to the reference numerals included in the following Figures, in which features depicted in multiple Figures are designated with consistent reference numerals.

[0013] Figure 1 illustrates a series of wafer shape categorizations in accordance with at least one embodiment.

[0014] Figure 2 is a flowchart illustrating an example process of evaluating a wafer using the system shown in Figure 3 with the categories shown in Figure 1.

[0015] Figure 3 is a simplified block diagram of an example system for evaluating a wafer using the process shown in Figure 2 in accordance with the categories shown in Figure 1.

[0016] Figure 4 illustrates an example configuration of the client systems shown in Figure 3.

[0017] Figure 5 illustrates an example configuration of the server systems shown in Figure 3.

[0018] Figure 6 is a diagram illustrating an example line scanning process executed by a measurement device.

[0019] Figures 7A and 7B are diagrams further illustrating the example line scanning process shown in Figure 6.

[0020] Figure 8A and 8B are side diagrams of a wafer depicting example wafer parameters associated with wafer flatness.

[0021] Figures 9A and 9B are top views of a wafer illustrating the scan lines obtained for the wafer.

[0022] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION

[0023] The field of the disclosure relates to shape analysis of semiconductor wafers and, more particularly, to systems and methods implementing a classification model for wafer shape detection during a wire saw process step.

[0024] The most common measurements of wafer flatness are TTV (Total Thickness Variation), BOW and WARP. These parameters are defined in the SEMI standards, which also give detailed instructions on measurements. However, these existing summary indicators, in combination to other local ones, are not effective for accurately depicting the wafer shape. Traditionally, wafer shape in the semiconductor industry has been assessed using three parameters: TTV, WARP, and BOW. However, as slicing technology has improved, these parameters are no longer sufficient to accurately describe the shape of the wafer. This is because secondary shape components have become more noticeable, and capturing these components requires more complex analysis.

[0025] Semiconductor Wafer Gaging Stations measure wafers for Thickness, Total Thickness Variation (TTV), Flatness, Bow, Warp and Resistivity. These Wafer Gaging stations use non-contact measurement techniques to assure accurate, precise, and repeatable data. The measurements are made by placing the wafer on a positioning ring and moving it between the sensors. The operator selects the desired measurement. In some embodiments, thickness is given as a real time measurement, where the display is updated 5 times per second, for example.

[0026] Total Thickness Variation (TTV) is determined by scanning the wafer between one or more probes. The TTV is the difference between the largest and smallest thickness measurements made during the scan. TTVs are displayed directly on a digital readout. This measurement conforms to ASTM standard F657.

[0027] Bow, or the centerpoint deflection of the wafer, is measured by placing the wafer onto a wafer ring and positioning it over the lower probe. The reading is taken. The wafer is turned over and the same center-point position is measured. Thedifference between the readings is divided by two to get the centerpoint Bow. It is also possible to determine whether the centerpoint Bow is due to a concave or convex condition.

[0028] Warp is the numeric representation of the shape of the wafer. It is measured with respect to the reference plane created by the pins of the wafer ring. Warp is given for the centerline (the general shape of the wafer) or for the top or bottom surface. The measurement is made in the same manner as TTV, per ASTM 657.

[0029] Wafer rings are required for Flatness, Warp, and Bow measurements. Three contact points on the wafer ring define a reference plane for determining surface variation. The base surface of the ring is ground flat and parallel to the reference plane. Three locator pads keep the wafer centered and prevent the wafer edge from mechanical damage. The vacuum chucks, made of hard-coated aluminum, are used with compatible wafer positioning rings for Flatness measurements.

[0030] In an example embodiment, the silicon ingot is sliced into wafers using a wire saw machine. The wire saw machine consists of a thin wire, often coated with diamond abrasives, which is wound around pulleys and moves at high speed. In some cases, a slurry containing abrasive particles (like silicon carbide) is used in conjunction with the wire to enhance the cutting action. The abrasive particles are carried by the wire as it cuts through the silicon. The wire, with or without the abrasive slurry, moves against the silicon ingot, and the abrasive particles grind or cut away the silicon, slicing it into thin wafers. The silicon ingot is sliced into multiple wafers of precise thickness, which can be as thin as 180 pm for solar wafers.

[0031] Wire sawing is advantageous because it produces thin wafers with minimal kerf loss, meaning more usable material is obtained from the ingot. It also allows for efficient processing of multiple ingots simultaneously. The wire saw process, especially when cutting thinner wafers, can be more challenging due to the increased risk of wire breakage and the need for precise control of the cutting process, especially with thinner wires and wafers. Research efforts focus on optimizing theprocess parameters (wire speed, feed rate, abrasive particle size and density) to minimize subsurface damage and wafer breakage. Wire sawing is widely used in the production of silicon wafers for both the semiconductor and photovoltaic (solar cell) industries.

[0032] Those having ordinary skill in the art would understand that although the present systems and methods are described in view of semiconductor wafers, the present systems and methods may also be applied to other surfaces in need of analysis, such as, but not limited to, mechanically machined surfaces.

[0033] Figure 1 illustrates a series of wafer shape categorizations 100 in accordance with at least one embodiment.

[0034] Traditionally, wafer shape in the semiconductor industry has been assessed using three parameters: TTV, WARP, and BOW. However, as slicing technology has improved, these parameters are no longer sufficient to accurately describe the shape of the wafer. This is because secondary shape components have become more noticeable, and capturing these components requires more complex analysis.

[0035] The categories described include ‘U’ 105, ‘S’ 110, ‘L’ 115, ‘W’ 120, and T 125. For the ‘U’ 105 category, this include a standard wafer shape where the warp value ranges from 5 to 50 pm. BOW can be positive or negative, depending on whether it is convex or concave general shape.

[0036] For ‘S’ 110, this category describes a shape with flex. This is usually where a process issues occurred. For this category, the bow measurement is meaningless since convexity is useless for bow flipping. Bow flipping is a procedure which aims to optimize total warp, by means of choosing how to flip the wafers after slicing, according to their convexity. The contribution of each layer to total warp is taken in consideration to decide how to flip.

[0037] For ‘L’ 115, this category describes a shape with a high pick of warp on one side (cut beginning to end). For this category, the wafer has a bow value very close to zero.

[0038] For ‘W’ 120, this category describes a “U-like” shape, with an opposite bow.

[0039] For ‘I’ 125, this category describes every shape with a warp value lower than 5 pm, which can be considered as flat.

[0040] The classification of the wafer shapes provides insights into machine performance and on process variability. In some embodiments, the classification information may be used to determine a condition of a device, such as a wire saw or cutter. This may be used to determine when to adjust the machine to ensure proper operation.

[0041] Figure 2 is a flowchart illustrating an example process 200 of evaluating a wafer using the system 300 (shown in Figure 3) with the categories 100 (shown in Figure 1). In the example embodiment, steps of process 200 are performed by the WSA computer device 310 (shown in Figure 3).

[0042] In the example embodiment, the WSA computer device 310 receives 205 a plurality of historical images of objects. In some embodiments, the plurality of historical images of objects includes a plurality of scans of silicon wafers post-slicing, such as via a wire saw.

[0043] In the example embodiment, the WSA computer device 310 trains 210 a model to classify the plurality of historical images. In some embodiments, the model is a neural network trained to classify the images into the categories 100 shown in Figure 1. In some further embodiments, the model is trained using supervised learning, by also receiving categories 100 for the plurality of historical images.

[0044] In the example embodiment, the WSA computer device 310 receives 215 scan data of an object to be analyzed. In some embodiments, the objectto be analyzed is a circular, semiconductor wafer. In other embodiments, other surfaces may be analyzed instead. In some embodiments, the scan data is post-slicing nanotopography. In further embodiments, the scan data is one of four line scan data or eight line scan data of the object

[0045] In the example embodiment, the WSA computer device 310 executes 220 the model to determine a category 100 for the object to be analyzed. In some embodiments, the WSA computer device 310 determines multiple categories for the object being analyzed. In these embodiments, the WSA computer device 310 includes locations on the object where the conditions for the different categories occur.

[0046] In the example embodiment, the WSA computer device 310 determine 225 whether or not to approve the object based on the category 100 for the object. In these embodiments, the WSA computer device 310 determines whether or not the silicon wafer may be used after being categorized.

[0047] In some further embodiments, the WSA computer device 310 determines a condition of a device, such as, but not limited to, a wire saw, based on the assigned category of the object. The WSA computer device 310 adjusts the device based on the determined condition. For example, the category of the object may indicate a condition of the device, such as the wire being damaged or a failure with the liquid abrasive. The condition of the device may be determine based on reviewing a plurality of categories of a plurality of objects, where the plurality of categories may indicate a trend.

[0048] In some further embodiments, the WSA computer device 310 adjust operation of a subsequent device to correct the category of the object. For example, the WSA computer device 310 may adjust operation of a grinder and / or polisher to remove any imperfection that may have caused the categorization.

[0049] In the example embodiment, the categories include, but are not limited to, a shape with a flex; a shape with a high pick of warp on one side; a shapewith a bow value close to zero; a ‘U-like’ shape with an opposite bow; and a shape with a warp value less than 5 pm.

[0050] Figure 3 is a simplified block diagram of an example system 300 for evaluating a wafer using the process 200 (shown in Figure 2) in accordance with the categories 100 (shown in Figure 1). In the example embodiment, system 300 is used for analyzing wafers post-slicing. In addition, system 300 is a real-time data analyzing and classifying computer system that includes a wafer surface analysis (WSA) computer device 310 (also known as a WSA server) configured to analyze wafers and predict future states based on the analysis.

[0051] A measurement device 305 is configured to scan the surface of a wafer to generate a profile of that wafer. More specifically, the measurement device 305 scans the nanotopography of the wafer and is in communication with the WSA computer device 310. In some embodiments, the measurement device 305 is a semiconductor wafer gaging station to determine the thickness of the wafer at a plurality of points. The measurement device 305 connects to the WSA computer device 310 through various wired or wireless interfaces including without limitation a network, such as a local area network (LAN) or a wide area network (WAN), dial-in-connections, cable modems, Internet connection, wireless, and special high-speed Integrated Services Digital Network (ISDN) lines. The measurement device 305 receives data about the surface of a wafer and reports that data to the WSA computer device 310. In other embodiments, the measurement device 305 is in communication with one or more client systems 325 and the client systems 325 route the measurement data to the WSA computer device 310 in real-time or near real-time. In some embodiments, a first measurement device 305 measures one side of the wafer and a second measurement device 305 measures the other side of the wafer. In some other embodiments, a single measurement device 305 measures both sides of the wafer.

[0052] As described above in more detail, the WSA server 310 is programmed to analyze wafers to determine a category of the wafer surface post-slicing to allow the system 300 to respond to changes that would cause the wafer to be out oftolerance quickly. The WSA server 310 is programmed to (1) determine current conditions of a wafer; (2) determine one or more categories based on the current conditions of the wafer; and (3) determine if adjustments need to be made to one or more wafer processing devices based on the post-slicing category of the wafer.

[0053] Client systems 325 are computers that include a web browser or a software application, which enables client systems 325 to communicate with the WSA server 310 using the Internet, a local area network (LAN), or a wide area network (WAN). In some embodiments, client systems 325 are communicatively coupled to the Internet through many interfaces including, but not limited to, at least one of a network, such as the Internet, a LAN, a WAN, or an integrated services digital network (ISDN), a dial-up-connection, a digital subscriber line (DSL), a cellular phone connection, a satellite connection, and a cable modem. Client systems 325 can be any device capable of accessing a network, such as the Internet, including, but not limited to, a desktop computer, a laptop computer, a personal digital assistant (PDA), a cellular phone, a smartphone, a tablet, a phablet, or other web-based connectable equipment.

[0054] A database server 315 is communicatively coupled to a database 320 that stores data. In one embodiment, database 320 is a database that includes historical data and the model. In some embodiments, database 320 is stored remotely from WSA server 310. In some embodiments, database 320 is decentralized. In the example embodiment, a person can access database 320 via client systems 325 by logging onto WSA server 310.

[0055] Figure 4 illustrates an example configuration of client systems shown in Figure 3. User computer device 402 is operated by a user 401. User computer device 402 may include, but is not limited to, measurement device 305, WSA computer device 310, and client systems 325 (all shown in Figure 3). User computer device 402 includes a processor 405 for executing instructions. In some embodiments, executable instructions are stored in a memory area 410. Processor 405 may include one or more processing units (e.g., in a multi-core configuration). Memory area 410 is any device allowing information such as executable instructions and / or transaction data to bestored and retrieved. Memory area 410 may include one or more computer-readable media.

[0056] User computer device 402 also includes at least one media output component 415 for presenting information to user 401. Media output component 415 is any component capable of conveying information to user 401. In some embodiments, media output component 415 includes an output adapter (not shown) such as a video adapter and / or an audio adapter. An output adapter is operatively coupled to processor 405 and operatively coupleable to an output device such as a display device (e.g., a cathode ray tube (CRT), liquid crystal display (LCD), light emitting diode (LED) display, or “electronic ink” display) or an audio output device (e.g., a speaker or headphones). In some embodiments, media output component 415 is configured to present a graphical user interface (e.g., a web browser and / or a client application) to user 401. A graphical user interface may include, for example, an interface for viewing the results of the analysis of one or more wafers. In some embodiments, user computer device 402 includes an input device 420 for receiving input from user 401 . User 401 may use input device 420 to, without limitation, select a wafer to view the analysis of. Input device 420 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector, a biometric input device, and / or an audio input device. A single component such as a touch screen may function as both an output device of media output component 415 and input device 420.

[0057] User computer device 402 may also include a communication interface 425, communicatively coupled to a remote device such as WSA server 310 (shown in Figure 3). Communication interface 425 may include, for example, a wired or wireless network adapter and / or a wireless data transceiver for use with a mobile telecommunications network.

[0058] Stored in memory area 410 are, for example, computer- readable instructions for providing a user interface to user 401 via media output component 415 and, optionally, receiving and processing input from input device 420.A user interface may include, among other possibilities, a web browser and / or a client application. Web browsers enable users, such as user 401, to display and interact with media and other information typically embedded on a web page or a website from WSA server 310. A client application allows user 401 to interact with, for example, WSA server 310. For example, instructions may be stored by a cloud service, and the output of the execution of the instructions sent to the media output component 415.

[0059] Processor 405 executes computer-executable instructions for implementing aspects of the disclosure. In some embodiments, the processor 405 is transformed into a special purpose microprocessor by executing computer-executable instructions or by otherwise being programmed.

[0060] Figure 5 illustrates an example configuration of the server systems shown in Figure 3. Server computer device 501 may include, but is not limited to, database server 315 and WSA server 310 (both shown in Figure 3). Server computer device 501 also includes a processor 505 for executing instructions. Instructions may be stored in a memory area 510. Processor 505 may include one or more processing units (e.g., in a multi-core configuration).

[0061] Processor 505 is operatively coupled to a communication interface 515 such that server computer device 501 is capable of communicating with a remote device such as another server computer device 501, another WSA server 310, or client system 325 (shown in Figure 3). For example, communication interface 515 may receive requests from client system 325 via the Internet, as illustrated in Figure 3.

[0062] Processor 505 may also be operatively coupled to a storage device 534. Storage device 534 is any computer-operated hardware suitable for storing and / or retrieving data, such as, but not limited to, data associated with database 320 (shown in Figure 3). In some embodiments, storage device 534 is integrated in server computer device 501. For example, server computer device 501 may include one or more hard disk drives as storage device 534. In other embodiments, storage device 534 is external to server computer device 501 and may be accessed by a plurality of server computer devices 501. For example, storage device 534 may include a storage areanetwork (SAN), a network attached storage (NAS) system, and / or multiple storage units such as hard disks and / or solid state disks in a redundant array of inexpensive disks (RAID) configuration.

[0063] Processor 505 may be operatively coupled to storage device 534 via a storage interface 520. Storage interface 520 is any component capable of providing processor 505 with access to storage device 534. Storage interface 520 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component providing processor 505 with access to storage device 534.

[0064] Processor 505 executes computer-executable instructions for implementing aspects of the disclosure. In some embodiments, the processor 505 is transformed into a special purpose microprocessor by executing computer-executable instructions or by otherwise being programmed. For example, the processor 505 is programmed with instructions such as illustrated in Figure 2.

[0065] Figure 6 is a diagram illustrating an example line scanning process 600 executed by a measurement device 305 (shown in Figure 3). In the example embodiment, process 600 is performed by measurement device 305 as a part of system 300 (shown in Figure 1) and process 200 (shown in Figure 2).

[0066] According to the line scanning process 600, the wafer W is supported by one or more support pins 603 in contact with a first surface 605A of the wafer. As illustrated by a comparison between a shape of the wafer in a gravity-free state (indicated with reference number 607) to a shape of the wafer in the supported state (indicated with reference number 609), the shape of the supported wafer 609 is deflected as a function of gravity and a mass of the wafer W. The measurement device 305 includes a first electrostatic capacitive sensor 621 A for measuring a plurality of distances (e.g., “Distance-B”) between the first sensor 621 A and a first surface 605A (e.g., front surface) along a diameter of the supported wafer 609. Similarly, the measurement device 305 includes a second electrostatic capacitive sensor 62 IB formeasuring a plurality of distances (e.g., “Distance-F”) between the second sensor 621B and a second surface 605B (e.g., back surface) along a diameter of the supported wafer 609. The obtained data includes a line scan data set corresponding to the diameter. The line scan data set comprises the plurality of distances measured by the first sensor 621 A along the diameter of the supported wafer 609 and the plurality of distances measured by the second sensor 621 B along the diameter of the supported wafer 609. The line scan data set is indicative of the wafer profile along the diameter.

[0067] Figures 7A and 7B are diagrams further illustrating the example line scanning process 600 (shown in Figure 6). Figures 7A and 7B illustrate the line scanning process 600 executed by a measurement device 305 for obtaining a plurality of line scan data sets, each indicative of a wafer profile along a particular diameter. As illustrated by Figure 7A, a first line scan (indicated by arrow 701) is executed along a first diameter of the wafer. In particular, the first sensor 621 A is moved in a plane above the first surface 605A in a first direction along the first diameter of the wafer. The first sensor 621 A measures the distance between the first sensor 621 A and the first surface 605A of the wafer at pre-defined intervals (i.e., pitch R, measurement frequency). The pre-defined intervals are illustrated as has marks on the surface of wafer W in Figure 7A. For instance, the first sensor 621 A may measure the distance at 1 or 2 mm intervals along the first diameter of the wafer. The second sensor 62 IB is similarly moved in a plane below the second surface 605B in the first direction to measure the distance between the second sensor 62 IB and the second surface 605B along the first diameter of the wafer. The first diameter of the wafer may be defined as a function of a reference point. For example, in the illustrated process, the first diameter passes through the notch N located on the perimeter of the wafer.

[0068] As illustrated by Figure 7B, after completing the first line scan 701, the wafer W is rotated (indicated by arrow 709). In particular, a rotation stage 705, positioned below the support pins 603, is raised to lift the wafer W to a position (indicated by reference number 707) above the support pins 603. While supporting the wafer in the lifted position 707, the rotation stage rotates. As a result, the wafer is rotated a number of degrees (0). The rotation stage 705 is lowered and the rotated waferis re-positioned on the support pins 603. The positions of the support pins 603 with respect to the second surface of the wafer are indicated with hidden lines in Figures 7A and 7B. In turn, a line scan (indicated by arrow 715) along a second diameter of the wafer is executed. According to the illustrated process, the first and second sensors 621 A and 62 IB are moved in planes respectively corresponding to the first and second surfaces 605A and 605B in a second direction (e.g., opposite to the first direction) along the second diameter of the wafer. As explained above in connection with the first line scan 701, the first and second sensors 621 A and 62 IB respectively measure the distances between the sensors 621A and 621B and the first and second surfaces 605A and 605B of the wafer at the pre-defined intervals along the second diameter of the wafer. The rotation 709 and line scanning operations 701 and 715 are repeated in order to obtain each of the plurality of line scan data sets.

[0069] The measurement device 305 suitably uses a self-mass compensation algorithm to determine the wafer shape for a gravity free state 607. The self-mass compensation determines the shape of the wafer as a function of the line scan data sets, wafer density, an elastic constant, the diameter of the wafer, and the positions of the support pins 603. In one embodiment, measurement device 305 measures one or more wafer parameters based on the wafer shape. The wafer parameters may include one or more of the following: warp, bow, TTV (total thickness variation), and / or GBIR (global back surface ideal range).

[0070] Referring to Figure 8A, warp and bow are generally determined with respect to a reference plane. The reference plane is defined as a function the contact points between the support pins 603 and the surface 605A of the wafer . Specifically, warp is defined as the absolute value of the difference between maximum deviation and minimum deviation of the median area from the reference plane. The median area is a locus of points which are equidistant from the front surface 605B of the wafer and the back surface 605A of the wafer. Bow is defined as the amount of deviation from the reference plane at the wafer center. Referring to Figure 8B, GBIR and TTV reflect the linear thickness variation of the wafer and can be computed based on a difference between a maximum and a minimum distance from theback surface of the wafer to the reference plane. For example, the measurement device 305 may obtain four line scan data sets as illustrated by Figure 9 A or eight line scan data sets as illustrated in Figure 9B. Each line scan data set is indicative of a diametric profile of the wafer.

[0071] Referring again to the system 300 illustrated in Figure 3, the data obtained by the measurement device 305 for measuring the nanotopography of the wafer as sliced by the wire saw is transmitted to the WSA computer device 310. For example, the line scan data sets and / or the determined wafer shape may be transmitted to the WSA computer device 310. The WSA computer device 310 receives the scan data and executes computer-executable instructions for performing a plurality of operations for processing the received scan data as described herein. In particular, the WSA computer device 310 categorizes the wafer based on the received scan data. In some embodiments, the WSA computer device 310 determines a grinding parameter based on the predicted nanotopography of the wafer. The operation of the grinder is adjusted accordingly.

[0072] The WSA computer device 310 may access a feedback program for processing the received scan data. The received scan data may include the line scan data sets and / or the determined wafer shape for the sliced wafer. The WSA computer device 310 determines one or more grinding parameters based on the category of the wafer to correct for the category.

[0073] The operation of the wire saw or other stations is adjusted based on the determined parameters. In one embodiment, the wire saw, grinder, and / or polisher are configured to receive the determined operating parameters and adjust one or more components of device. In another embodiment, the determined operating parameters are provided to an operator and the operator configures the device to adjust one or more components of the device as a function of the determined operating parameters.

[0074] At least one of the technical problems addressed by this system may include: (i) improve analysis of wafers; (ii) decreased loss of material due tomalfunction; (iii) earlier determination of wafer quality; (iv) increased accuracy in wafer analysis; and / or (v) increased accuracy in wafer analysis.ADDITIONAL CONSIDERATIONS

[0075] As will be appreciated based upon the foregoing specification, the above-described embodiments of the disclosure may be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or subset thereof. Any such resulting program, having computer-readable code means, may be embodied or provided within one or more computer-readable media, thereby making a computer program product, i.e., an article of manufacture, according to the discussed embodiments of the disclosure. The computer-readable media may be, for example, but is not limited to, a fixed (hard) drive, diskette, optical disk, magnetic tape, semiconductor memory such as read-only memory (ROM), and / or any transmitting / receiving medium such as the Internet or other communication network or link. The article of manufacture containing the computer code may be made and / or used by executing the code directly from one medium, by copying the code from one medium to another medium, or by transmitting the code over a network.

[0076] These computer programs (also known as programs, software, software applications, “apps,” or code) include machine instructions for a programmable processor and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms “machine-readable medium” “computer-readable medium” refers to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine -readable signal. The “machine-readable medium” and “computer-readable medium,” however, do not include transitory signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0077] As used herein, the terms “processor” and “computer” and related terms, e.g., “processing device”, “computing device”, and “controller” are not limited to just those integrated circuits referred to in the art as a computer, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller (PLC), a reduced instruction set circuit (RISC), an application specific integrated circuit (ASIC), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are example only and are thus not intended to limit in any way the definition and / or meaning of the term “processor.”

[0078] As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are example only, and are thus not limiting as to the types of memory usable for storage of a computer program.

[0079] As used herein, the term “database” can refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database can include any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object-oriented databases, and any other structured collection of records or data that is stored in a computer system. The above examples are example only, and thus are not intended to limit in any way the definition and / or meaning of the term database. Examples of RDBMS’ include, but are not limited to including, Oracle® Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, and PostgreSQL. However, any database can be used that enables the systems and methods described herein. (Oracle is a registered trademark of Oracle Corporation, Redwood Shores, California; IBM is a registered trademark of International Business Machines Corporation, Armonk, New York; Microsoft is a registered trademark of Microsoft Corporation, Redmond, Washington; and Sybase is a registered trademark of Sybase, Dublin, California.)

[0080] In another example, a computer program is provided, and the program is embodied on a computer-readable medium. In an example, the system is executed on a single computer system, without requiring a connection to a server computer. In a further example, the system is being run in a Windows® environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Washington). In yet another example, the system is run on a mainframe environment and a UNIX® server environment (UNIX is a registered trademark of X / Open Company Limited located in Reading, Berkshire, United Kingdom). In a further example, the system is run on an iOS® environment (iOS is a registered trademark of Cisco Systems, Inc. located in San Jose, CA). In yet a further example, the system is run on a Mac OS® environment (Mac OS is a registered trademark of Apple Inc. located in Cupertino, CA). In still yet a further example, the system is run on Android® OS (Android is a registered trademark of Google, Inc. of Mountain View, CA). In another example, the system is run on Linux® OS (Linux is a registered trademark of Linus Torvalds of Boston, MA). The application is flexible and designed to run in various different environments without compromising any major functionality.

[0081] As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural elements or steps, unless such exclusion is explicitly recited. Furthermore, references to “example” or “one example” of the present disclosure are not intended to be interpreted as excluding the existence of additional examples that also incorporate the recited features. Further, to the extent that terms “includes,” “including,” “has,” “contains,” and variants thereof are used herein, such terms are intended to be inclusive in a manner similar to the term “comprises” as an open transition word without precluding any additional or other elements.

[0082] Furthermore, as used herein, the term “real-time” refers to at least one of the time of occurrence of the associated events, the time of measurement and collection of predetermined data, the time to process the data, and the time of a system response to the events and the environment. In the examples described herein, these activities and events occur substantially instantaneously.

[0083] In some embodiments, the system includes multiple components distributed among a plurality of computer devices. One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium. The systems and processes are not limited to the specific embodiments described herein. In addition, components of each system and each process can be practiced independent and separate from other components and processes described herein. Each component and process can also be used in combination with other assembly packages and processes. The present embodiments may enhance the functionality and functioning of computers and / or computer systems.

[0084] The computer-implemented methods discussed herein can include additional, less, or alternate actions, including those discussed elsewhere herein. The methods can be implemented via one or more local or remote processors, transceivers, servers, and / or sensors (such as processors, transceivers, servers, and / or sensors mounted on vehicles or mobile devices, or associated with smart infrastructure or remote servers), and / or via computer-executable instructions stored on non- transitory computer-readable media or medium. Additionally, the computer systems discussed herein can include additional, less, or alternate functionality, including that discussed elsewhere herein. The computer systems discussed herein can include or be implemented via computer-executable instructions stored on non-transitory computer- readable media or medium.

[0085] As used herein, the term “non-transitory computer-readable media” is intended to be representative of any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Therefore, the methods described herein can be encoded as executable instructions embodied in a tangible, non-transitory, computer readable medium, including, without limitation, a storage device and / or a memory device. Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. Moreover, as used herein, the term “non- transitory computer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices, including, without limitation, volatile and nonvolatile media, and removable and non-removable media such as a firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital source such as a network or the Internet, as well as yet to be developed digital means, with the sole exception being a transitory, propagating signal.

[0086] The patent claims at the end of this document are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being expressly recited in the claim(s).

[0087] This written description uses examples to disclose the disclosure, including the best mode, and also to enable any person skilled in the art to practice the disclosure, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the disclosure is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claims

WHAT IS CLAIMED IS:

1. A computer device comprising at least one processor in communication with at least one memory device, wherein the at least one processor programmed to: receive a plurality of historical images of objects; train a model to classify the plurality of historical images; receive scan data of an object to be analyzed; execute the model to determine a category for the object to be analyzed; and determine whether or not to approve the object based on the category for the object.

2. The computer device of Claim 1, wherein the at least one processor is further programmed to: determine a condition of a device based on the category of the object; and adjust the device based on the determined condition.

3. The computer device of Claim 2, wherein the device is a wire saw.

4. The computer device of Claim 2, wherein the at least one processor is further programmed to adjust operation of a subsequent device to correct the category of the object.

5. The computer device of Claim 1, wherein the category of the object represents a shape with a flex.

6. The computer device of Claim 1, wherein the category of the object represents a shape with a high pick of warp on one side.

7. The computer device of Claim 6, wherein the category of the object further represents a shape with a bow value of approximately zero.

8. The computer device of Claim 1, wherein the category of the object represents a ‘U-like’ shape with an opposite bow to the ‘U-like’ shape.

9. The computer device of Claim 1, wherein the category of the object represents a shape with a warp value less than 5 pm.

10. The computer device of Claim 1, wherein the object to be analyzed is a semiconductor wafer.

11. The computer device of Claim 1, wherein the scan data is postslicing nanotopography.

12. The computer device of Claim 1, wherein the scan data is one of four line scan data or eight line scan data of the object.

13. A computer-implemented method for analyzing an object, the computer-implemented method implemented by a computing device including at least one processor in communication with at least one memory device, the method comprising: receiving a plurality of historical images of objects; training a model to classify the plurality of historical images; receiving scan data of an object to be analyzed; executing the model to determine a category for the object to be analyzed; anddetermining whether or not to approve the object based on the category for the object.

14. The computer-implemented method of Claim 13, wherein the object to be analyzed is a semiconductor wafer.

15. The computer- implemented method of Claim 13 further comprising: determining a condition of a device based on the category of the object; and adjusting the device based on the determined condition.

16. The computer- implemented method of Claim 15, wherein the device is a wire saw.

17. The computer device of Claim 1, wherein the category of the object represents one of a shape with a flex, a shape with a high pick of warp on one side, a shape with a bow value of approximately zero, a ‘U-like’ shape with an opposite bow to the ‘U-like’ shape, and a shape with a warp value less than 5 pm.

18. At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, when executed by a computing device comprising at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to: receive a plurality of historical images of objects; train a model to classify the plurality of historical images; receive scan data of an object to be analyzed; execute the model to determine a category for the object to be analyzed; anddetermine whether or not to approve the object based on the category for the object.

19. The non-transitory computer-readable media of Claim 18, wherein the computer-executable instructions further cause the at least one processor to: determine a condition of a device based on the category of the object, wherein the device is a wire saw; and adjust the device based on the determined condition.

20. The non-transitory computer-readable media of Claim 18, wherein the category of the object represents one of a shape with a flex, shape with a high pick of warp on one side, a shape with a bow value of approximately zero, a ‘U- like’ shape with an opposite bow to the ‘U-like’ shape, and a warp value less than 5 pm.

Images