Surface scanning tool

Abstract

A surface scanning tool. In an embodiment, the surface scanning tool includes a laser, a detector, and a signal analysis module. The laser emits a beam of light that is reflected off of a bottom surface of the tool. The detector receives the reflected beam of light and transmits a reflected beam signal to the signal analysis module. The signal analysis module determines a surface shape of the bottom surface and triggers a mitigation action. In an alternative embodiment, the surface scanning tool includes a camera and a signal analysis module. The camera takes a photograph of the bottom surface of the tool and transmits an image to the signal analysis module. The signal analysis module determines a surface shape of the bottom surface and triggers a mitigation action.

Description

Technical Field

[0001] This utility model relates to a surface scanning tool. Background Technology

[0002] The semiconductor industry continues to grow due to the increasing integration density of various electronic components (e.g., transistors, diodes, resistors, capacitors, etc.). These increases in integration density mainly come from the gradual shrinking of the smallest feature size, which allows more components to be integrated into a given area (i.e., footprint).

[0003] In addition to smaller electronic components, improvements in component packaging aim to provide smaller packages that occupy less area than previous packages. Examples of semiconductor package types include quad flat packs (QFP), pin grid arrays (PGA), ball grid arrays (BGA), flip chips (FC), three-dimensional integrated circuits (3DICs), wafer-level packages (WLPs), package-on-package (PoP), system-on-chip (SoC), or system-on-integrated circuit (SoIC) devices. Some of these 3D devices (e.g., 3D integrated circuits, SoCs, and SoICs) are fabricated by placing wafers on wafers at the semiconductor wafer level.

[0004] Semiconductor devices can be assembled using pick-and-place machines (PnP machines), which include sophisticated robots with dispensing heads or tips that move along one or more axes to assemble unfinished products. The dispensing heads are capable of picking up parts and / or placing parts from one surface to another.

[0005] However, in a pick-and-place machine, the ends may be defective. For example, the ends may include protrusions or be contaminated. Defective and / or contaminated ends may damage the original surface and / or the target transfer surface. Due to the rapid nature of the pick-and-place process, it can occur before defective ends are identified. In other words, contamination or defects may not be detected until damage has already occurred to the transfer surface and / or the target surface. Utility Model Content

[0006] The purpose of this invention is to provide a surface scanning tool to solve at least one of the above-mentioned problems.

[0007] This utility model provides a surface scanning tool, including a laser, a detector, and a signal analysis module. The laser is configured to irradiate the bottom surface of the tool with a light beam, the detector is configured to receive a set of reflected light from the bottom surface of the tool, and the signal analysis module is configured to receive the set of reflected light and determine the surface shape of the bottom surface of the tool.

[0008] According to one embodiment of the present invention, it further includes a display device configured to display a visual representation of the bottom surface of the tool, wherein the visual representation is generated by the signal analysis module.

[0009] According to one embodiment of the present invention, the light beam has a wavelength between 300 nm and 950 nm.

[0010] According to one embodiment of the present invention, the signal analysis module determines that the surface shape is normal in response to determining that the set of reflected light reflected from the tool includes several main reflected light signals.

[0011] According to one embodiment of the present invention, the signal analysis module determines the surface shape abnormality in response to determining that the set of reflected light reflected from the tool includes several scattered reflected light signals.

[0012] According to one embodiment of the present invention, the signal analysis module is further configured to trigger an action.

[0013] This utility model provides a surface scanning tool, including a camera and an image analysis module. The camera is configured to capture images of the bottom surface of the tool, and the image analysis module is configured to receive the images of the bottom surface of the tool and determine the surface shape of the bottom surface of the tool.

[0014] According to one embodiment of the present invention, it further includes a display device configured to display the image of the bottom surface of the tool. Attached Figure Description

[0015] The complete disclosure is based on the following detailed description and the accompanying drawings. It should be noted that, in accordance with the general practice of the industry, the illustrations are not necessarily drawn to scale. In fact, the dimensions of components may be arbitrarily enlarged or reduced for clarity.

[0016] Figure 1A This is an example schematic diagram of a common pick-and-place tool according to various embodiments of the present invention.

[0017] Figure 1B This is an example schematic diagram of a pick-and-place tool with end defects according to various embodiments of the present invention, and a bottom view of the pick-and-place tool.

[0018] Figure 1C This is an exemplary cross-sectional view of a pick-and-place tool with end defects according to various embodiments of the present invention, and a cross-sectional view of the pick-and-place tool itself.

[0019] Figure 2A This is an example schematic diagram of a common probe card according to various embodiments of the present invention.

[0020] Figure 2B This is an example schematic diagram of a probe card with end defects according to various embodiments of the present invention, and a bottom view of the probe card.

[0021] Figure 2C This is an example cross-sectional view of a probe card with end defects according to various embodiments of the present invention, and a cross-sectional view of the probe card.

[0022] Figure 3A These are illustrative diagrams of surface scanning tools and common pick-and-place tools according to various embodiments of the present invention.

[0023] Figure 3B These are example schematic diagrams of surface scanning tools and defective pick-and-place tools according to various embodiments of the present invention.

[0024] Figure 4A This is a schematic diagram illustrating alternative examples of surface scanning tools and common pick-and-place tools according to various embodiments of the present invention.

[0025] Figure 4B This is a schematic diagram illustrating alternative examples of surface scanning tools and defective pick-and-place tools according to various embodiments of the present invention.

[0026] Figure 5 This is a block diagram of components of an example mobile computing device suitable for providing a signal analysis module or an image analysis module according to various embodiments.

[0027] Figure 6 This is a flowchart illustrating a method for detecting a pick-and-place tool using a surface inspection tool according to an embodiment of the present invention.

[0028] Figure 7 This is a flowchart illustrating a method for detecting a pick-and-place tool using an alternative surface inspection tool according to an embodiment of the present invention.

[0029] The attached figures are labeled as follows:

[0030] 102: Picking and placing tools / picking and placing machine

[0031] 106,310,412:End

[0032] 108, 208: Electrical components

[0033] 110: Target wafer / Wafer surface / Wafer

[0034] 112,212,318: Defects

[0035] 114: End Retainer

[0036] 116,214: Bottom surface

[0037] 118: Target Location

[0038] 202: Probe Card

[0039] 204: Contact element

[0040] 210: Wafer / Target Wafer

[0041] 216: Components

[0042] 300: Surface scanning inspection tool / Surface scanning tool

[0043] 302: Laser

[0044] 304: Detector

[0045] 306, 406: Bottom surface

[0046] 308: Pick-up / Place / Transfer Tool

[0047] 312a: Beam / Narrow Beam / Light

[0048] 312b: Reflected light signal / Reflection / Main reflected light / Reflected light

[0049] 312c: Scattered light signal / Scattered reflected light signal / Scattered light

[0050] 314: Signal Analysis Module

[0051] 316,424: Display devices

[0052] 400: Surface scanning tool / Surface scanning inspection tool / Scanning tool

[0053] 402: Camera

[0054] 404: Image Analysis Module / Signal Analysis Module

[0055] 408: Tools for picking up and putting down

[0056] 416: Image

[0057] 418: Defect

[0058] 418': Defect / Image Defect

[0059] 500: Laptops / Computers

[0060] 502: Processor

[0061] 508: Antenna

[0062] 512: Volatile Memory

[0063] 513: Disk drive

[0064] 514: Floppy disk drive

[0065] 515: CD / DVD drive

[0066] 516: Honeycomb Telephone Transceiver

[0067] 517: Touchpad touch surface / Touchpad

[0068] 518: Keyboard

[0069] 519: Monitor

[0070] 600, 700: Methods 602, 604, 606, 608, 610, 612, 614, 702, 704, 706, 708, 710, 712: Steps Detailed Implementation

[0071] The following disclosure provides numerous different embodiments or examples to implement various features of this invention. The following disclosure describes specific examples of the various components and their arrangements for simplification. Of course, these specific examples are not intended to be limiting. For example, if this utility model describes a first feature formed on or above a second feature, it indicates that it may include embodiments where the first and second features are in direct contact, or embodiments where an additional feature is formed between the first and second features, so that the first and second features may not be in direct contact. Furthermore, the same reference numerals and / or designations may be repeated in different examples in the following disclosure. These repetitions are for simplification and clarity and are not intended to limit any specific relationship between the different embodiments and / or structures discussed.

[0072] Furthermore, spatially related terms, such as "below," "below," "lower," "above," "higher," and similar terms, are used to facilitate the description of the relationship between one element or feature and another element(s) in the figures. In addition to the orientations shown in the figures, these spatially related terms are intended to encompass different orientations of the device in use or operation. The device may be rotated to different orientations (rotated 90 degrees or other orientations), and the spatially related terms used herein may be interpreted accordingly. Unless otherwise explicitly stated, each element having the same reference numeral is assumed to have the same material composition and thickness within the same thickness range. Various embodiments will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used wherever possible to refer to the same or similar parts. References to specific examples and embodiments are for illustrative purposes and are not intended to limit the scope of the claims.

[0073] As used in this article, “computing device” refers to a fixed computing device, including personal computers, desktop computers, all-in-one computers, workstations, supercomputers, general-purpose GPUs, mainframe computers, embedded computers (e.g., in vehicles and other large systems), computing systems within or configured for servers, cloud computing systems, and enterprise computing systems.

[0074] Pick-and-place machines play a crucial role in automatically moving electronic components from one location to another. For example, they can be used to pick up electronic components (e.g., semiconductor dies, resistors, capacitors, etc.) from reels, wafers, trays, or frames and place them onto printed circuit boards (PCBs), wafers, or frames. Pick-and-place machines can be equipped with various types of nozzles or tips designed to pick up different electronic components.

[0075] Automation of the pick-and-place machine increases productivity, enabling the rapid assembly of large quantities of printed circuit boards. Automation requires virtually no user intervention during the pick-and-place process.

[0076] While automation can generally improve efficiency, problems arise when the pick-and-place terminals used to pick up different electronic components are defective or contaminated. For example, the terminals may have protrusions, which can cause defects when they come into contact with the surface of the electronic component. Alternatively, the terminals may be contaminated with surface particles, excess die material, or fragments of broken wafers, die frames, or die frames. While automation of pick-and-place machines offers advantages, defects or contamination are not easily identified. Therefore, defects and / or contaminants can have negative consequences, leading to defective products, reduced efficiency, and potential damage to the pick-and-place machine itself.

[0077] For example, the bottom surface of the pick-and-place machine where the end piece is located is typically larger than the target transfer surface. Therefore, during pick-and-place, the end piece comes into close contact with the target transfer surface when transferring the die or component. In the event of defects at the end piece, it may damage the target transfer surface. Additionally, if the end piece is contaminated, contaminants may be transferred to the target transfer surface.

[0078] This invention relates to a surface inspection tool capable of identifying defects or contamination in a pick-and-place end tool. In some embodiments, the surface module inspection tool may include a laser, a detector, and a signal analysis module. The laser scans the surface of the pick-and-place end tool by sending a narrow beam of light towards the bottom surface of the tool. In the presence of defects or contamination, the narrow beam of light is reflected and scattered. The detector receives the reflected and / or scattered light signals and sends the signals to the signal analysis module. The signal analysis module analyzes the reflected and / or scattered light to determine the surface shape of the bottom surface of the pick-and-place tool. In the presence of defects and / or contamination, the signal analysis module determines that the surface shape is abnormal. Based on the determined surface shape, the signal analysis module can then determine appropriate mitigation actions, such as continuing the pick-and-place process, issuing an alarm, shutting down the pick-and-place machine, initiating a cleaning process, and / or rescanning the surface.

[0079] In an alternative embodiment, the surface inspection tool includes a camera and a signal analysis module. The camera captures images of the surface of the pick-and-place tool and transmits the images to the signal analysis module. Based on the images, the signal analysis module can determine the surface shape of the pick-and-place tool. In the presence of defects and / or contamination, the signal analysis module can determine that the pick-and-place end has an abnormal surface shape. The signal analysis module can then determine appropriate mitigation actions, such as continuing the pick-and-place process, issuing an alarm, shutting down the pick-and-place machine, initiating a cleaning process, capturing subsequent images of the surface, and / or retraining the signal analysis module using the images.

[0080] The various embodiments disclosed herein offer various advantages and improvements. For example, the various embodiments disclosed herein can identify defects and / or contamination on the pick-and-place tool before moving electrical components from the transfer surface to the target surface. Therefore, the various embodiments disclosed herein can reduce damage to the pick-and-place tool and the target surface. The various embodiments disclosed herein can provide immediate analysis of the pick-and-place end surface before further contamination or damage to other surfaces. Additionally, the various embodiments disclosed herein can immediately notify the user of detected defects and / or contamination. Furthermore, the various embodiments disclosed herein can take appropriate mitigation actions before damage and / or contamination of the target surface, and in some cases, automatically without user intervention.

[0081] Now refer to the attached diagram, Figures 1A to 1C An example of a pick-and-place tool is shown. See Figure 1A The pick-and-place tool 102 includes an end 106, also referred to as a head or nozzle. The end 106 picks up electrical components from a feeder system. The feeder system can be a belt feeder, pallet feeder, bulk feeder, pneumatic feeder, die frame, transfer wafer, or other suitable feeder. The end 106 can transfer the electrical components picked up from the feeder system to a target location, such as a printed circuit board, a target wafer, a target die frame, or other target location.

[0082] The pick-and-place tool 102 may additionally include a vision system, a transport system, and a control system (not shown). The vision system may include a camera and / or sensors to identify the position of electrical components. The vision system further ensures that the electrical components are accurately placed in the target location. The transport system delivers the target location (e.g., the target wafer) to the pick-and-place machine. The control system coordinates the functions of the pick-and-place machine, such as the movement of electrical components.

[0083] like Figure 1A As shown, the pick-and-place device 102 may include electrical components 108 adhered to or held by an end 106 for intended transfer to a target wafer 110 (left). The electrical components 108 may originate from a die frame, wafer, or other feeder system. Once the electrical components 108 are adhered to or held by the end 106, the pick-and-place device moves such that the end 106 contacts the target wafer 110 (right). In some embodiments, the target wafer 110 may be moved to contact the end 106 holding the electrical components 108. The electrical components 108 may be transferred to the target wafer 110 at a target location 118. This process may be repeated until each electrical component 108 has been transferred to the target wafer 110. Due to the contact between the end 106 and various surfaces, contaminants may quickly and easily accumulate and build up.

[0084] Figure 1B A pick-and-place machine 102 including defect 112 is shown. In some embodiments, defect 112 may be a protrusion formed during or after manufacturing. Alternatively, defect 112 may be an accumulated contaminant, such as surface particles, residue from broken wafers, or other contaminants. In some embodiments, more than one defect 112 may be present on the end 106 and / or the bottom surface 116 of the pick-and-place machine 102.

[0085] Figure 1BA bottom view of the pick-and-place machine 102 is also shown. The pick-and-place machine 102 includes an end 106 on a bottom surface 116 and an end holder 114. As shown, the bottom surface 116 is larger than the end 106 and the target location 118 of the electrical components 108 on the target wafer 110. As shown, the bottom surface of the pick-and-place machine 102 includes a defect 112. The defect 112 may be located on the bottom surface 116, the end 106, and / or the end holder 114.

[0086] Figure 1C A pick-and-place mechanism 102 is shown for placing an electrical component 108 onto a target wafer 110. As described above, the bottom surface 116 of the pick-and-place mechanism 102 may be larger than the target position 118 of the electrical component 108. When the electrical component 108 is moved from the end 106 to the target position 118, the pick-and-place mechanism 102 is in close contact with the target wafer 110. Due to the size of the bottom surface 116 and the close proximity of the pick-and-place mechanism 102 to the target wafer 110, in the presence of a defect 112, the defect 112 is likely to come into contact with the target wafer 110, such as... Figure 1C As shown.

[0087] Figure 1C A cross-sectional view of a defect 112 in contact with the target wafer 110 is also shown. If the defect 112 includes a protrusion or is a protrusion, the defect 112 may physically collide with the target wafer 110 and cause physical damage to the target wafer 110 and / or the pick-and-place machine 102. If the defect 112 is a contaminant, the contaminant may transfer from the bottom surface 116 or end 106 of the pick-and-place machine 102 to the target wafer 110. Regardless of whether the defect is a protrusion or contamination, the defect 112 may damage the target wafer 110. Therefore, the target wafer 110 may become unusable, resulting in reduced yield for the apparatus. The damaged wafer 110 or other transfer locations must then be discarded or recycled. This leads to delays in the manufacturing process, unnecessary waste, and overall cost and time inefficiency.

[0088] Figure 2A Alternative testing tools for probe card 202 are shown. Probe card 202 can be a pin-type, vertical-type, MEMS-type, or other suitable type of probe card. Once pick-and-place machine 102 has moved all electrical components 208 to their respective target locations, probe card 202 can be used to test the system and circuitry at the target locations. Additionally, probe card 202 can be used to test and verify circuitry at the wafer level. Generally, probe card 202 is used to test wafers before dicing and packaging into individual wafers, but it can also be used to perform other tests.

[0089] The probe card 202 includes a contact element 204 that can contact an electrical component 208 being tested on a wafer 210. The contact element 204 can be formed of a metallic material such as W, ReW, BeCu, Pd, or Al2O3 to allow proper electrical contact between the contact element 204 and the electrical component 208, although other suitable metallic materials are also within the scope of this invention. Due to the contact between the contact element 204 and various surfaces, contaminants can quickly and easily accumulate on the surfaces of the contact element 204 and / or the probe card 202. The contact element 204 can be a printed circuit board, a wafer, or other suitable surface. Additionally, the probe card 202 can be connected to testing equipment, such as a computer or sensor, to analyze and display results.

[0090] Figure 2B An example of a probe card 202 with defect 212 is shown. Similar to... Figure 1B Defect 212 can be a protrusion or contaminant, such as surface particles. Figure 2B A bottom view of the probe card 202 is also shown. The probe card has a bottom surface 214 and two contact elements 204, with the defect 212 located between the contact elements. In some cases, the defect 212 may be located anywhere on the bottom surface 214 and / or the contact elements 204.

[0091] Figure 2C An example is shown where probe card 202 tests wafer 210 by moving probe card 202 downwards until contact element 204 contacts electrical component 208. Similar to... Figure 1C As shown, during testing, probe card 202 can be very close to wafer 210. Therefore, defect 212 can come into contact with wafer 210 and damage it. As shown, defect 212 can come into contact with component 216 that is not currently being tested by contact component 204. Alternatively, defect 212 can come into contact with the surface of wafer 210 without electrical component 208.

[0092] In various embodiments, such as Figure 2C As shown in the cross-sectional view, defect 212 has a high probability of contacting wafer 210. If defect 212 is a protrusion, it may cause physical damage to the wafer. If defect 212 is a contaminant, it may transfer to wafer 210.

[0093] Embodiments of surface scanning inspection tools will now be discussed. While pick-and-place tool 102 and probe card 202 have been discussed above, surface scanning inspection tools are not limited to using pick-and-place tool 102 and probe card 202. Various embodiments of surface scanning inspection tools can be used with a variety of tools, such as transfer tools, testing tools, or other suitable tools for surfaces that may have defects. Although the following discussion focuses on pick-and-place machine 102 that receives electrical components 108, 208 from the die frame and transfers them to target wafers 110, 210, those skilled in the art will understand that the disclosed embodiments are applicable to a variety of tools, surfaces, and locations. For example, in alternative embodiments, electrical components 108, 208 may be received from other feeder systems and transferred to other target locations.

[0094] Figure 3A An example of a surface scanning inspection tool 300 is shown. In various embodiments, the surface scanning inspection tool 300 includes a laser 302, a detector 304, and a signal analysis module 314. In embodiments, the laser 302 may be a UV laser, a visible light laser, an IR laser, a helium-neon laser, or any other suitable laser. Other suitable laser sources are also within the scope of this invention. In embodiments, the laser 302 emits (irradiates) a narrow beam 312a with a wavelength range between approximately 300 nm and approximately 950 nm, approximately 350 nm and approximately 900 nm, or approximately 400 nm and approximately 800 nm.

[0095] In some embodiments, the light beam 312a from laser 302 has a specific wavelength or wavelength range. The light beam 312a from laser 302 can be transmitted toward the bottom surface 306 of the pick-and-place tool 308, and in some embodiments, it can be reflected. In some embodiments, laser 302 further illuminates the bottom surface 306 of the pick-and-place tool 308. The light beam 312a from laser 302 can illuminate the bottom surface 306 of the pick-and-place tool 308. The light beam 312a from laser 302 can be reflected 312b by the bottom surface 306 of the pick-and-place tool 308 and incident on detector 304. Detector 304 can transmit the reflected light signal 312b to signal analysis module 314.

[0096] In some embodiments, the signal analysis module 314 receives and analyzes the reflected light signal 312b to determine the surface shape of the bottom surface 306 of the pick-and-place tool 308. In some embodiments, the signal analysis module 314 may include software executed by a processor in a computing system. In some embodiments, the detector 304 transmits the raw data of the reflected light signal 312b to the signal analysis module 314. In an alternative embodiment, the detector 304 may preprocess the reflected light signal 312b and transmit the processed reflected light signal data to the signal analysis module 314.

[0097] In some embodiments, the signal analysis module 314 receives raw or processed reflected light signal data as input. The signal analysis module 314 outputs the determined surface shape of the bottom surface 306 of the pick-and-place tool 308. In some embodiments, this output can reconstruct the surface shape of the bottom surface 306 of the pick-and-place tool 308 and display a visualization of the bottom surface 306 to the user on the display device 316. In other embodiments, the signal analysis module 314 outputs a file containing information about the bottom surface 306, such as a topology file.

[0098] Once the signal analysis module 314 determines the surface shape of the bottom surface 306, it can trigger various implementation actions. For example, in some embodiments, the pick-up and drop-off process can proceed normally. In other embodiments, the implementation action may include sending a signal to the user. However, in other embodiments, the implementation action may stop the pick-up and drop-off process. In other embodiments, the implementation action may initiate a cleaning process. In other embodiments, the implementation action may perform subsequent analysis of the bottom surface 306, or other appropriate actions.

[0099] like Figure 3A As shown, the pick-and-place tool 308 has a defect-free end 310. In this embodiment, the light beam 312a can illuminate the bottom surface 306 of the pick-and-place tool 308, and the main reflected light 312b is detected by the detector 304. When the reflected light 312b is the main reflected light, the signal analysis module 314 can determine that the bottom surface 306 is normal and free from defects and / or contamination. In some embodiments, the signal analysis module 314 can continue the pick-and-place process. In other embodiments, the signal analysis module 314 can send a notification to the user confirming the normal bottom surface 306. In other embodiments, the signal analysis module 314 can create a visual representation on the display device 316 and allow the user to manually continue the pick-and-place process.

[0100] Display device 316 (optional) may be a useful component in some embodiments of this surface scanning inspection tool 300, providing the user with a visual representation of the topography of the bottom surfaces 306, 406 and the results of defect detection for easy interpretation. In one embodiment, display device 316 may be any suitable LCD screen or OLED panel with sufficient resolution (e.g., 1024x768 pixels), color gamut, and brightness level to accurately represent the image data generated from the signal analysis module (SAM) 314.

[0101] In another embodiment, a high-resolution touchscreen interface for user input is integrated into the display device 316. This allows the user to zoom in on specific areas of interest, adjust parameters such as illumination wavelength or camera resolution, and access additional information about detected defects via an interactive menu. The touchscreen can also be used to initiate actions based on the analysis results of the signal analysis module 314.

[0102] In another embodiment, a high-definition display device with 4K (3840x2160 pixels) or higher resolution can be used to enhance image quality and visualize detailed surface topography. This allows users to detect minute details, such as scratches, corrosion, or contamination, that might not be visible on a low-resolution display.

[0103] In addition, the display device 316 may incorporate features such as gesture recognition technology, allowing users to manipulate images through hand gestures rather than relying solely on touch input.

[0104] In some embodiments where multiple cameras are used in conjunction with a signal analysis module (e.g., stereo vision) 314, the 3D visualization module is integrated into the display device. This allows for the instant rendering of surface topography and defect detection results, as if viewed from different angles, providing the user with an immersive experience and thus improving detection accuracy.

[0105] Additionally, some embodiments may include augmented reality or virtual reality capabilities within the display device 316 to overlay digital information about detected defects onto the actual image captured by camera 402. Finally, the display device 316 may also include audio output capabilities, such as speakers or headphones that provide auditory cues when a defect is detected during the detection process.

[0106] The laser 302 used in the surface scanning inspection tool 300 of various embodiments can be a useful component for accurately irradiating and detecting defects on the bottom surface of the transfer tool. In various embodiments, the laser 302 can be configured to operate within a specific wavelength range, including UV (300 nm to 400 nm), visible light (350 nm to 900 nm), or IR (700 nm to 950 nm). The choice of wavelength may depend on the type of target defect; shorter wavelengths are generally more effective for detecting surface contamination and minute defects. In one embodiment, a diode-pumped solid-state laser can be used to provide high-power irradiation without generating excessive heat.

[0107] In another embodiment, an ultraviolet (UV) laser can be used because it can excite fluorescence in certain materials, thereby allowing enhanced detection of subtle changes on the bottom surface. The UV wavelength range can also more accurately identify defects that might be invisible under other irradiation conditions. In another embodiment, a helium-neon gas discharge lamp or an LED-based laser source can be used as an alternative option.

[0108] In some embodiments, multiple lasers 302 with different wavelengths are used in combination to provide enhanced defect detection capabilities. For example, an ultraviolet wavelength and an infrared wavelength can be combined to simultaneously detect surface contamination and corrosion on the bottom surface. In other embodiments, a single high-power visible light laser 302 may be sufficient to detect larger defects or wear patterns.

[0109] Regarding beam selection, various embodiments allow for adjustable focus settings to optimize illumination intensity for specific regions of interest. This may include adjusting the spot size, divergence angle, and wavelength tuning based on the type of target defect. In some embodiments, combinations of these parameters can be used in conjunction with adaptive optics or wavefront correction techniques to ensure optimal beam quality.

[0110] The light beam 312a used in the surface scanning inspection tool 300 of the embodiment is a useful component capable of accurately detecting and analyzing defects on the bottom surface of the tool. In various embodiments, the laser 302 used can be configured to emit a light beam with a wavelength range from about 300 nanometers (nm) to about 950 nm, thereby allowing effective irradiation without damaging or contaminating these delicate surfaces.

[0111] In one embodiment, a UV diode-pumped solid-state (DPSS) laser is used as the light source. This type of laser emits ultraviolet radiation in the 350 nm to 400 nm range, making it particularly suitable for detecting surface contaminants such as dust particles, oil stains, and other substances that may affect tool performance or quality. Furthermore, because ultraviolet laser diodes can penetrate thin layers or coatings, they can be used to irradiate underlying surfaces with high intensity and resolution. This wavelength range is particularly effective for detecting small defects, such as contaminant particles on precision surfaces found in medical devices, aerospace components, automotive parts, semiconductor manufacturing equipment, etc.

[0112] In another embodiment, a visible diode-pumped solid-state (DPSS) laser can have an emission wavelength range of approximately 450 nm to 650 nm. This type of beam has been found to effectively illuminate large areas while providing sufficient penetration depth to detect defects on the bottom surface.

[0113] For applications requiring high-resolution imaging and precise defect detection, helium-neon gas discharge lamps can be used as the light source. These lamps emit radiation with wavelengths between 630 nm and 670 nm, making them particularly suitable for inspecting surface morphology at the microscopic level. Helium-neon lasers allow for precise control of beam intensity, thus they are used for high-precision inspection of delicate optical components. This wavelength range is especially effective for detecting defects such as scratches or contamination on the surfaces of optical and photonic components.

[0114] In some embodiments, multiple light beams of different colors or polarizations can be used simultaneously to enhance defect detection capabilities and improve the accuracy of identifying anomalies on the bottom surface. For example, a combination of UV-A (365 nm) and visible light (550 nm to 650 nm) can enhance the contrast between the defect and the surrounding area, thereby enabling more accurate analysis.

[0115] In addition to selecting the type of laser or lamp used as the beam source, various embodiments also involve adjusting parameters such as power level, spot size, and scanning speed. For example, when detecting small features on a bottom surface, a higher-power UV-A beam with a smaller spot size can be used while maintaining high sensitivity for detecting defects.

[0116] In other embodiments requiring the inspection of larger areas or more detailed topographical information, the laser can operate at a lower power level but maintain a longer exposure time. This embodiment can provide images capturing surface roughness and texture at greater resolution without compromising the accuracy of defect detection.

[0117] Furthermore, some embodiments relate to beamforming techniques, such as Gaussian beams, Bessel beams, or other custom-designed profiles, to optimize illumination patterns for specific applications. These customized methods can enhance the ability to detect defects on bottom surfaces while minimizing interference from ambient light or background noise.

[0118] The selection of the wavelength used to irradiate the bottom surface of the tool is a useful aspect in determining its effectiveness and accuracy in detecting defects or anomalies. In various embodiments, this can be achieved by using lasers with different wavelengths between 300 nm and approximately 950 nm, such as UV laser diodes emitting at approximately 350 nm to 400 nm, visible light lasers operating in the range of approximately 450 nm to 650 nm, infrared (IR) lasers emitting wavelengths in the range of approximately 700 nm to 900 nm, or even helium-neon lasers emitting spectra in the range of 630 nm to 670 nm. These different wavelengths can be selected based on the specific requirements for detecting various types of defects (e.g., contamination, scratches, corrosion, wear, misalignment, etc.).

[0119] In another embodiment, a visible laser operating in the 450 nm to 650 nm wavelength range is used to detect larger areas with lower sensitivity but greater penetration depth. This wavelength range may be suitable for detecting more useful defects, such as scratches or corrosion on surfaces found in industrial machinery parts, building materials, and other heavy equipment.

[0120] In another embodiment, IR lasers propagating in the 700nm to 900nm wavelength range are used to detect thicker material layers with high accuracy due to their ability to penetrate multiple coatings. This wavelength range is particularly effective for detecting defects such as misalignment or wear on surfaces of mechanical parts, gears, and bearings.

[0121] In addition to laser selection, various embodiments may also include optical design considerations for efficient light transmission and detection. This includes using high-quality lenses, mirrors, or prisms to minimize aberrations and maximize signal-to-noise ratios. In some embodiments, polarization filters may be used to enhance defect contrast by selectively filtering out unwanted reflections from the bottom surface.

[0122] In terms of data processing, various embodiments relate to algorithms for analyzing reflected light signals received from detectors such as photodiodes or CCDs. These algorithms may include edge detection techniques, machine learning models trained on large datasets of benchmark images and defects, or even deep neural networks that learn to recognize patterns in surface morphology over time.

[0123] Detector 304 is a useful component in some embodiments of surface scanning tools, helping to capture and process reflected light signals from the bottom surface of the transfer tool 308 or the electrical component 108 being inspected. In one embodiment, detector 304 can be implemented using a photodetector sensitive to a specific wavelength between 300 nm and approximately 950 nm (e.g., a silicon-based detector or an InGaAs-based detector). These detectors 304 convert incident photons into a charge proportional to their intensity and wavelength.

[0124] In another embodiment, a high-speed CMOS detector 304 with a pixel array can be used to detect reflected light signals from the bottom surface. This type of detector 304 may be particularly suitable when a camera is used instead of a laser detector combination in an alternative embodiment. See the discussion below for more details. Figure 4A as well as Figure 4B Examples of this implementation include the camera's frame rate and resolution, which can be adjusted to meet specific requirements, such as 10fps to 1000fps, or a resolution range from VGA (640x480 pixels) to high-definition video formats (such as HD720p).

[0125] In another embodiment, a photomultiplier tube (PMT) can be used to detect weak signals under low-light conditions. This type of detector 304 is particularly useful when the surface scanning inspection tool 300 is detecting surfaces with minimal reflectivity (e.g., surfaces coated with anti-reflective materials or with very rough textures). In some embodiments, the gain and sensitivity of the photomultiplier tube can be adjusted to optimize the signal-to-noise ratio.

[0126] In another embodiment, a hybrid photodetector combining the advantages of silicon-based and InGaAs-based detectors can be used to simultaneously detect signals across multiple wavelength ranges. This allows for a more comprehensive analysis of the surface morphology by capturing visible light reflection as well as infrared or ultraviolet radiation scattered from defects on the bottom surface.

[0127] In another embodiment, an array of detectors 304, comprising a arrangement of photodetectors with different spectral sensitivities, can be used to simultaneously capture signals spanning multiple wavelength ranges. By analyzing the reflectance patterns of different portions of the electromagnetic spectrum, surface anomalies can be detected and classified more accurately.

[0128] The selection of detector 304 can depend on specific requirements, such as signal strength, noise level, and desired resolution or frame rate. Generally, detectors with higher sensitivity can be used to detect weak signals from distant surfaces, while detectors with lower sensitivity are better suited for detecting strong reflections from nearby light sources. In combination-based embodiments, the selection of a particular detector 304 can also influence the selection of the laser wavelength range.

[0129] Figure 3B A surface scanning inspection tool 300 is shown. (Compared to...) Figure 3A Compared to the surface scanning inspection tool 300 shown, the end 310 of the pick-and-place tool 308 has a defect 318. The defect 318 may be a protrusion and / or contaminant accumulated on the bottom surface 306 of the pick-and-place tool 308. In some embodiments, a laser 302 may emit a narrow beam 312a at a specific wavelength or wavelength range toward the bottom surface 306 of the pick-and-place tool 308. The beam 312a emitted by the laser 302 illuminates the bottom surface 306 and may be reflected as a reflected light signal 312b from various surfaces and objects of the pick-and-place tool 308 to the detector 304. Additionally, the defect 318 may cause the illuminating beam 312a to generate a scattered light signal 312c when it impacts the bottom surface 306. The detector 304 may transmit any reflected light signal 312b and scattered light signal 312c to the signal analysis module 314. In some embodiments, the signal analysis module 314 may further receive reflected light signals 312b reflected from various surfaces and objects on the pick-and-place tool 308 to the detector 304 to detect features (e.g., electrical components 108) and their positions to be placed on the wafer surface 110. If a feature (component) is misplaced or misaligned, PnP can be used to take corrective measures to move the feature.

[0130] The signal analysis module (SAM) 314 takes the scattered light signal 312c as input. The signal analysis module 314 can generate and output a determined surface shape of the bottom surface 306. For example... Figure 3BAs shown, the signal analysis module 314 determines the presence of defect 318 based on any reflected light signal 312b and scattered light signal 312c. In some embodiments, the signal analysis module 314 can generate a visual representation of the bottom surface 306 of the pick-and-place tool 308, and can display the generated visual representation on the display device 316. The generated visual representation may include representations such as colors, emphasis points, or symbols to identify the location of defect 318. In other embodiments, the signal analysis module 314 can create a file containing information about the bottom surface 306, such as a topology file.

[0131] In some embodiments, if the signal analysis module 314 determines that there is a defect 318 on the bottom surface 306 of the pick-and-place tool 308, the signal analysis module 314 may trigger the surface scanning detection tool 300 to perform an action. In some embodiments, this action may stop the pick-and-place tool 308 from further handling electrical components. In other embodiments, this action may send a warning to the user on the display device 316, prompting the user to input information. In other embodiments, this action may prompt a cleaning cycle for the pick-and-place tool 308. In other embodiments, this action may initiate a rescan of the bottom surface 306.

[0132] The signal analysis module (SAM) 314 is a useful component of the surface scanning inspection tool 300 in various embodiments, capable of analyzing and detecting defects or anomalies on the bottom surfaces 306, 406 of the transfer tool in real time with unprecedented accuracy and speed. In one embodiment, the signal analysis module 314 may use a trained machine learning model trained using a set of reference images representing typical shapes of each type of bottom surface to determine whether the detected shape meets a predetermined threshold for being considered normal or abnormal.

[0133] In another embodiment, the signal analysis module 314 can be combined with photodetectors such as silicon-based detectors or InGaAs-based detectors sensitive to specific wavelengths between 300 nm and 950 nm to utilize edge detection algorithms. This combination improves accuracy by identifying defects on the bottom surface and then evaluating machine learning models, thereby enabling accurate analysis of complex surface shapes.

[0134] In another embodiment, the signal analysis module 314 can combine multiple signal processing algorithms in a hierarchical manner, allowing it to analyze and process complex patterns with increased accuracy. For example, defect identification based on edge detection algorithms can be combined with wavelet analysis or Fourier transform techniques to extract subtle features representing surface anomalies.

[0135] The signal analysis module 314's ability to adapt over time can be further enhanced by incorporating new images into incremental updates of its training set without requiring retraining from scratch. This allows the module to continuously learn and improve itself when encountering useful defects, wear patterns, or other types of irregularities on the bottom surface.

[0136] In some embodiments, the signal analysis module 314 may be configured with multiple cameras 402 that use CMOS sensors or CCDs to capture high-speed video sequences at a resolution of up to 10 megapixels per frame. This allows for real-time analysis and processing within milliseconds or microseconds, depending on specific application requirements.

[0137] Furthermore, in embodiments where the bottom surfaces 306, 406 of the transfer tool are damaged beyond repair due to excessive wear, the signal analysis module 314 can recommend replacement rather than cleaning and reuse based on user input regarding production limitations and quality standards for each component type.

[0138] In this embodiment, an image analysis module (IAM) can be used to store multiple reference images and compare them simultaneously with a single input image. This allows the module to analyze complex patterns of multiple defects or anomalies in different regions of interest within seconds.

[0139] The output of the signal analysis module 314 can be presented in real time through intuitive graphical user interfaces (GUIs) to visualize surface morphology and defect detection results, allowing users to adjust parameters such as illumination wavelength, camera resolution, and analysis algorithm according to their specific needs.

[0140] In another embodiment of the signal analysis module 314 used in a production environment, multiple displays can be networked and synchronized via wireless communication protocols (e.g., Wi-Fi) to monitor surface quality across the entire production line in real time. This allows operators to quickly identify areas requiring maintenance or replacement and minimizes downtime.

[0141] In one embodiment, the signal analysis module 314 can use a machine learning model trained with a set of benchmark images including normal surfaces and surfaces with various defects or anomalies. Training data can be generated by manual annotations of each image, labeled by experts in the art according to its defect type and severity. New images are added to this database as they become available to continuously retrain the model.

[0142] In another embodiment, the signal analysis module 314 may use a combination of edge detection algorithms, followed by a machine learning model trained on a labeled dataset, to identify defects or anomalies on the bottom surface with high accuracy. This approach can be particularly effective in detecting subtle changes in surface topography that may indicate potential problems with tool performance over time. In yet another embodiment, instead of using a single high-speed camera, multiple cameras are used simultaneously to capture 3D images of the bottom surface at different angles and resolutions.

[0143] The signal analysis module 314 determines whether anomalies on surfaces 306 and 406 indicate contamination, wear, or other types of defects by analyzing patterns in the reflected light signals and comparing them with a reference image. For example, the detection of multiple small scratches in a specific area may indicate normal wear; however, large-scale irregularities may represent more significant problems, such as corrosion.

[0144] In embodiments where the bottom surfaces 306, 406 of the transfer tool are damaged beyond repair due to excessive wear or other factors, the signal analysis module 314 can recommend replacement rather than cleaning and reuse based on user input regarding production limitations and quality standards for each type of electrical component. This system is configured to be easily integrated into existing production lines without disrupting workflows or requiring effective restructuring.

[0145] In one embodiment, the signal analysis module 314 can determine whether a surface shape is normal or abnormal by comparing an image from a camera with a reference image representing the typical shape of each type of bottom surface (e.g., clean, contaminated). The threshold can be adjusted by user input to fine-tune the sensitivity and specificity for detecting defects or anomalies.

[0146] In another embodiment, the signal analysis module 314 determines whether the surface shape is abnormal based on a machine learning model trained with a set of benchmark images (e.g., clean, contaminated) representing typical shapes of the bottom surface. The thresholds used in these comparisons may vary depending on specific production requirements and constraints.

[0147] Abnormal Surface Detection (ASD) technology in signal analysis modules is a useful method for detecting and analyzing the surface morphology of tools used in various industries, such as medical devices, aerospace components, automotive manufacturing, semiconductor manufacturing, and other high-tech applications that require precise quality control measures to ensure optimal performance.

[0148] In a further embodiment, the Image Analysis Module (IAM) 404 can combine multiple signal processing algorithms to analyze complex surface shapes more accurately. For example, edge detection algorithms for identifying defects, combined with subsequent machine learning model analysis, can improve accuracy.

[0149] This system can also be configured to detect and identify specific types of defects on the bottom surface, such as protrusions, contamination, scratches, corrosion, or other irregularities, based on patterns in the reflected light signals compared to a reference image database. The image analysis module 404 determines whether the anomaly is minor (e.g., a dispersed but relatively uniform reflection pattern) or severe by analyzing the intensity distribution pattern.

[0150] In embodiments where multiple anomalies occur simultaneously, the image analysis module 404 / signal analysis module 314 can use a machine learning model trained on a dataset including various defect types and severity levels to prioritize them based on severity and location. For specific scenarios based on production requirements and constraints, user input can override these preset values.

[0151] This system is configured to be easily integrated into existing production lines without disrupting workflows or requiring effective reorganization by adapting the image analysis module, configuring a user interface tailored to the needs of each industry, and ensuring seamless communication with other equipment on the production line.

[0152] In embodiments where defects cannot be cleaned or removed (e.g., due to material properties), the signal analysis module 314 can recommend alternative solutions, such as reconfiguration or complete replacement of components based on production requirements and limitations. Users interact with an intuitive graphical user interface to visualize surface morphology and defect detection results, and adjust parameters such as illumination wavelength, camera resolution, and analysis algorithms as needed.

[0153] Furthermore, the machine learning model within the image analysis module can learn from new images added to its training set without requiring separate retraining each time a new image is introduced, allowing it to adapt rapidly over time while maintaining high accuracy in defect detection. Multiple benchmark images can be stored using the image analysis module and compared simultaneously with a single input image. However, the number of benchmark images may depend on available computing resources and storage capacity limitations.

[0154] In another embodiment, the system includes multiple cameras at different angles or resolutions to capture 3D surface topography, rather than just a single high-speed camera, and combines data from each camera using techniques such as stereo vision or structured light scanning.

[0155] The output from the signal analysis module 314 can trigger specific actions to address defects or anomalies detected on the bottom surface of the tool based on its determination that the shape is normal or abnormal. In embodiments where the anomaly is identified as minor and does not impair performance, the system can continue normal operation without interruption while monitoring for further changes in real time. Conversely, in the event of a useful irregularity, the signal analysis module 314 can immediately trigger a shutdown to prevent damage to or contamination of other components on the production line.

[0156] In some embodiments, when a defect exceeds a predetermined threshold, multiple actions can be triggered simultaneously based on user input and system configuration settings. For example, when an abnormal surface shape is detected, the tool handling process stops, and a cleaning cycle is initiated for maintenance to prevent further damage or contamination of other components in the production line.

[0157] Figure 4A as well as Figure 4B An alternative embodiment of the surface scanning tool 400 is shown. As illustrated, the surface scanning tool 400 may include a camera 402 and a signal analysis module 404. In embodiments, the camera 402 may be a high-speed camera, a high-resolution camera, or other suitable camera. In embodiments with a high-speed camera, the camera 402 can take pictures in less than about 0.001 seconds, less than about 0.002 seconds, less than about 0.005 seconds, or less than about 0.01 seconds. In embodiments with a high-resolution camera, the camera may have a resolution of about 0.04 mm, about 0.05 mm, about 0.07 mm, about 0.08 mm, or about 0.1 mm.

[0158] In some embodiments, camera 402 can capture photographs of the bottom surface 406 of the pick-and-place tool 408. In some embodiments, camera 402 can capture a single image of the bottom surface 406. In other embodiments, camera 402 can capture multiple photographs of the bottom surface 406. In still other embodiments, camera 402 can capture multiple images of the bottom surface 406 at different angles. In other embodiments, camera 402 can capture multiple images of the bottom surface 406 at different angles, shutter speeds, and resolutions. Camera 402 can transmit the images of the bottom surface 406 to signal analysis module 404.

[0159] In some embodiments, the signal analysis module 404 may receive and analyze images to determine the surface shape of the bottom surface 406. In some embodiments, the signal analysis module 404 (also referred to as the image analysis module (IAM) 404) may include software executed by a processor on a computing system. In some embodiments, the camera 402 transmits raw image data to the image analysis module 404. In an alternative embodiment, the camera 402 preprocesses the image and transmits the processed image data to the image analysis module 404.

[0160] In some embodiments, the image analysis module 404 may receive a raw image or a processed image as input and output the surface shape of the bottom surface 406. In some embodiments, the image analysis module 404 may be executed as a machine learning model. In embodiments, the machine learning model may be trained using image training data including images of normal and abnormal bottom surfaces. Additionally, the training data may include a baseline image.

[0161] A machine learning model can determine and generate the surface shape of the bottom surface 406. In some embodiments, the image analysis module 404 outputs a binary determination. For example, the output includes normal or abnormal; yes or no; or clean or defective. In other embodiments, the output may display a raw or pre-processed image of the bottom surface 406 captured by the camera 402. The raw or pre-processed image is displayed to the user via a display device 424.

[0162] In this embodiment, the output of the image analysis module 404 can trigger an action. This action can be to continue the pick-up and drop-off process normally, send a signal to the user, stop the pick-up and drop-off process, start a cleaning process, perform subsequent analysis of the surface, or other suitable actions. In some embodiments, a combination of these actions can be performed.

[0163] like Figure 4A As shown, the pick-and-place tool 408 has a flawless end 412. In this embodiment, a camera captures a photograph of the bottom surface 406 of the pick-and-place tool 408. Figure 4A In the example shown, the signal analysis module 404 will determine whether the bottom surface 406 is normal.

[0164] In some embodiments, the image analysis module 404 can determine whether the bottom surface 406 is normal based on a reference image. For example, the image analysis module 404 can compare an image from a camera with a reference image. If the similarity between the image of the bottom surface 406 and the reference image meets or exceeds a threshold, the image analysis module 404 determines that the bottom surface 406 is normal. In some embodiments, the threshold can be predetermined by the image analysis module 404 or set by the user. In other embodiments, the threshold can be a dynamic threshold based on the signal analysis module 404 or user input. In embodiments, the threshold can be approximately 70%, approximately 80%, approximately 90%, or approximately 95%. In still other embodiments, the image analysis module 404 makes a determination based on identifying certain features in the image without needing to compare with a reference image.

[0165] In some embodiments, where the image analysis module 404 determines that the bottom surface 406 is normal, the image analysis module 404 will continue the pick-and-place process. In other embodiments, the image analysis module 404 may send a notification to the user confirming that the surface 406 is normal. The user can then provide input to the image analysis module 404 to continue the pick-and-place process or take another action. In still other embodiments, the signal analysis module 404 may display image 416 on display device 424. The user can manually continue the pick-and-place process, save the image for later reference, and / or take subsequent images of the bottom surface 406.

[0166] In some embodiments, the image analysis module 404 may automatically store images in internal memory and / or a cloud system. In other embodiments, the image analysis module 404 may delete or remove images once analysis is complete or based on user input. In embodiments with multiple images, the image analysis module 404 may create an image collage for user comparison. Additionally, the image analysis module 404 may add images to training data to retrain and / or validate machine learning models.

[0167] In alternative embodiments using camera 402 instead of the laser-detector-signal analysis module combination, a high-speed CMOS sensor and a CCD can be used as suitable options to provide sufficient resolution (e.g., 10x768 pixels) and frame rate for accurate analysis by the image analysis module. In these cases, the output of camera 402 can be analyzed using a machine learning model trained on datasets including normal bottom surfaces and bottom surfaces with various defects or anomalies.

[0168] In another embodiment, storing multiple benchmark images and comparing them simultaneously with a single input image using image analysis, this allows the image analysis module 404 to rapidly adapt to changes in surface shape over time while maintaining high accuracy for detection purposes. This approach enables the system to learn from new data without requiring separate retraining each time an updated dataset is introduced into its training set.

[0169] In another embodiment where multiple actions are triggered simultaneously based on the output of the image analysis module 404, the production line can be stopped immediately and a cleaning cycle can be started immediately through seamless communication between different components of the production line. This ensures that defects or anomalies detected by the image analysis module 404 do not affect the overall quality control standards in the real-time monitoring scenario.

[0170] Furthermore, when defects cannot be cleaned or removed (e.g., due to material properties), alternative solutions can be recommended based on user input and production limitations for each part type, such as reassembling or replacing specific parts. This approach allows the system to flexibly adapt to changing needs while maintaining high accuracy in defect detection.

[0171] Figure 4B A surface scanning inspection tool 400 is shown. (Compared to...) Figure 4A Compared to the surface scanning inspection tool 400 shown, Figure 4B The surface scanning inspection tool 400 displays the end 412 of the pick-and-place tool 408 as having a defect 418. The defect 418 can be a protrusion or a contaminant. In some embodiments, the camera 402 can capture a photograph of the bottom surface 406 of the pick-and-place tool 408. In some embodiments, the camera 402 captures more than one image. Multiple images can have the same angle or different angles. In some embodiments, the camera 402 can capture multiple images from different angles, with different shutter speeds, and / or resolutions. The defect 418 is shown as defect 418' in the image. In some embodiments, the camera 402 can preprocess the image to display the image defect 418' with a prominent color, mark, or other appropriate identifier. The camera 402 can transmit the image 416 as a raw image or a preprocessed image to the image analysis module 404.

[0172] Image analysis module 404 can receive image 416 as input. Image analysis module 404 can generate and output the surface shape of the bottom of surface 406. For example... Figure 4BAs shown, the image analysis module 404 can determine the presence of a defect 418 on the bottom surface 406 of the pick-and-place tool 408. In some embodiments, the image analysis module 404 can generate an image 416 of the bottom surface 406 of the pick-and-place tool 408 on the display device 424. In other embodiments, the image analysis module 404 can generate a simplified schematic diagram of the bottom surface 406. This schematic diagram may include identification symbols, such as colors or markings, to identify different normal and abnormal portions on the bottom surface 406. In other embodiments, the signal analysis module 404 can display the generated image 416 and a reference image generated for comparison.

[0173] In some embodiments, where the image analysis module 404 determines that there is a defect 418 on the bottom surface 406 of the pick-and-place tool 408, the signal analysis module 404 may trigger an action. In some embodiments, this action may stop the pick-and-place tool 408. In other embodiments, this action sends a warning to the user on the display device 424, prompting the user to input information. The warning may include light, noise, a dialog box, or other suitable warning. In still other embodiments, this action prompts a cleaning cycle for the pick-and-place tool 408. In still other embodiments, this action takes a subsequent photograph of the bottom surface 406. In some embodiments, this action triggers retraining of the image analysis module 404.

[0174] This camera-based approach utilizes various types of sensors, such as complementary metal-oxide-semiconductor (CMOS) image sensor arrays or charge-coupled devices (CCDs), to capture images at frame rates ranging from 10 frames per second to over 1000 frames per second, depending on the specific application requirements and the desired level of detail. In one embodiment, a high-speed camera 402 with an exposure time as short as approximately 1 microsecond is used in conjunction with an image processing algorithm capable of real-time analysis of surface topography and defect detection.

[0175] In another embodiment, multiple cameras 402 can be used to capture images from different angles or resolutions to generate a detailed 3D model of the bottom surface. For example, a combination of high-speed CMOS sensors and structured light scanning technology can be used in conjunction with machine learning algorithms trained on datasets including normal and abnormal surface shapes to detect defects such as scratches, contamination, corrosion, wear, misalignment, or other irregularities.

[0176] In yet another embodiment, this camera-based system is designed for use in specific industries where precision and quality control are critical. For example, a high-speed CMOS sensor with an exposure time of approximately 0.1 microseconds can be used to detect medical devices such as surgical instruments, while a CCD array with higher resolution (e.g., up to 10 megapixels) can be used to detect defects in aerospace components (e.g., engine parts or fuel injectors).

[0177] In some embodiments, the camera-based system includes the ability to analyze and process features of images captured by multiple cameras 402 in real time. For example, image fusion techniques may be combined with machine learning algorithms trained on a dataset that includes normal surface shapes to detect anomalies such as scratches, contamination, corrosion, wear, misalignment, or other irregularities.

[0178] In another embodiment, a camera-based system is designed for use in high-speed manufacturing environments, where real-time analysis and processing of images captured by multiple cameras is useful. For example, a combination of CMOS sensors with exposure times ranging from 1 microsecond to over 10 milliseconds can be used in conjunction with machine learning algorithms trained on datasets including normal surface shapes to detect defects such as scratches, contamination, corrosion wear, or other irregularities.

[0179] In another embodiment, the camera-based system includes the ability to analyze and process features of images captured by multiple cameras 402 in real time. For example, image fusion techniques can be combined with machine learning algorithms trained on a dataset that includes normal surface shapes to detect anomalies such as scratches, contamination, corrosion, wear, misalignment, or other irregularities.

[0180] In some embodiments, camera-based system designs are used in high-speed manufacturing environments where real-time analysis and processing of images captured by multiple cameras is useful. For example, a combination of CMOS sensors with exposure times ranging from 1 microsecond to over 10 milliseconds can be used in conjunction with machine learning algorithms trained on datasets including normal surface shapes to detect defects such as scratches, contamination, corrosion wear, or other irregularities.

[0181] In another embodiment, the camera-based system includes the ability to analyze and process features from images captured by multiple cameras in real time. For example, image fusion techniques can be combined with machine learning algorithms trained on a dataset that includes normal surface shapes to detect anomalies such as scratches, contamination, corrosion, wear, misalignment, or other irregularities.

[0182] The Image Analysis Module (IAM) 404 is a useful component of this surface scanning tool. It can analyze and process images captured in real time by a combination of cameras or laser detectors to determine the shape and morphology of the bottom surface on the tool (e.g., pick-and-place machines, probe cards, medical devices, aerospace components, automotive parts, semiconductor manufacturing equipment, and other high-tech applications). The Image Analysis Module 404 is designed with machine learning capabilities, enabling it to learn from a training dataset that includes images of normal surface shapes as well as images exhibiting various defects or anomalies.

[0183] In one embodiment of this invention, the image analysis module 404 uses a convolutional neural network (CNN) architecture trained on a labeled dataset including clean and contaminated bottom surfaces. This enables the image analysis module 404 to recognize patterns in image features, such as texture, color, and shape, thereby distinguishing more useful problems such as typical wear and corrosion or contamination. In another embodiment, the image analysis module 404 incorporates transfer learning from a pre-trained model optimized for a specific industry or application.

[0184] The image analysis module 404 can be configured with a variety of camera settings, including high-speed CMOS sensors (e.g., 10 megapixels) that operate at frame rates of up to hundreds of frames per second; and CCDs with resolutions as low as 0.04 mm and shutter speeds of less than 1 millisecond, enabling it to capture detailed images in real time.

[0185] In addition to machine learning-based analysis, the image analysis module 404 can also use edge detection algorithms to identify defects on the bottom surface, and then use feature extraction techniques such as texture analysis or shape recognition. Compared to relying on only one method, this hybrid approach can achieve more accurate defect classification and localization.

[0186] The output of the image analysis module 404 is a visual representation of the analyzed images displayed through an intuitive interface, allowing users to easily identify anomalies, prioritize repairs based on severity, and initiate corrective actions accordingly. In embodiments where defects cannot be cleaned or removed (e.g., due to material properties), the image analysis module 404 can recommend alternative solutions, such as remodeling or complete replacement of the component.

[0187] In another embodiment, multiple benchmark images 416 can be stored for comparison with a single input image using the image analysis module 404; this allows the user to analyze and compare surface shapes in different production operations. Furthermore, incremental updates of new images 416 added to its training set enable the machine learning model of the image analysis module 404 to adapt quickly without requiring separate retraining each time an updated dataset is introduced.

[0188] In another embodiment, multiple signal processing algorithms can be combined in the image analysis module 404 to enhance accuracy and robustness; these include edge detection algorithms, followed by feature extraction techniques such as texture analysis or shape recognition.

[0189] This process involves comparing the input image 416 captured by camera 402 with a pre-stored reference image called a baseline. These baselines can be generated by various methods, such as manual annotation by experts, automated data collection from the production line, or even machine learning algorithms that learn surface morphology patterns over time. In one embodiment, multiple baseline images are stored for each type of bottom surface (e.g., clean, contaminated) to account for variations caused by manufacturing tolerances or environmental factors.

[0190] In another embodiment, the image analysis module 404 uses a combination of edge detection algorithms and machine learning model analysis before comparing the input image data from camera 402 with a benchmark. This method can more accurately identify defects by filtering out noise and irrelevant features in the surface topography pattern. Furthermore, the benchmark image can be dynamically updated when a new production line or manufacturing process is introduced into the system.

[0191] In another embodiment, the image analysis module 404 can use a hierarchical comparison strategy, where multiple levels of analysis occur simultaneously: first, the input image data is compared to coarse-grained baselines (e.g., overall shape and size), and then this evaluation is further refined by analyzing finer details (e.g., surface roughness and texture). This multi-level approach can more accurately detect defects or anomalies on the bottom surface.

[0192] In some embodiments, the image analysis module 404 may also incorporate additional information from other sensors to enhance the accuracy of benchmark image comparisons; for example, temperature data from a thermocouple or vibration readings from an accelerometer can be used as input features in a machine learning model. This integration can monitor and adapt surface topography patterns in real time according to environmental conditions.

[0193] In another embodiment, the image analysis module 404 can dynamically adjust the threshold used to determine whether a surface shape is normal or abnormal by analyzing user feedback (e.g., adjusting sensitivity or specificity). Users can also manually input specific parameters, such as illumination wavelength, camera resolution, or analysis algorithm, to fine-tune performance in a production environment.

[0194] In one embodiment, the initial dataset of baseline images comprises 500 samples with varying degrees of surface defects or anomalies on the bottom surface of the pick-and-place tool. These images are manually annotated by experts using a standardized labeling scheme that categorizes each image into normal (clean) or anomalous categories based on visual detection and defect severity assessment.

[0195] As new data emerges, the model is updated through incremental learning to incorporate new information from the production line without requiring separate retraining for each update. In another embodiment, multiple reference images are stored in a memory or cloud storage system with a capacity of at least 1 terabyte (TB) to accommodate a large-scale dataset and enable simultaneous comparisons between the input image and these reference points.

[0196] In another embodiment, a machine learning model is trained using transfer learning techniques by extracting features from images using a pre-trained convolutional neural network (CNN). This approach can quickly adapt to defect detection capabilities across different tool types or production lines without requiring extensive retraining.

[0197] Furthermore, various data augmentation strategies can be used to artificially increase the size and diversity of the dataset while maintaining image quality. These include random rotations of up to 30 degrees, flipping along the horizontal axis (mirroring), scaling factors between -20% and +10%, and adding Gaussian noise with a standard deviation of ±5%. Additionally, active learning techniques involve selecting samples from the new data stream and manually annotating them based on the indeterministic scores generated during model prediction.

[0198] In some embodiments, reinforcement chemistry is achieved by fine-tuning a machine learning model in response to user input regarding correct defect detection and false alarms, incorporating feedback signals or rewards. This adaptive approach can continuously improve accuracy as the user interacts with the system.

[0199] Furthermore, ensemble methods can be used to combine multiple models trained on different subsets of data (e.g., tool-specific and general-purpose) to achieve improved overall performance through diversity-based decision-making strategies. In other embodiments, attention mechanisms or spatial pyramid pooling techniques can be used within convolutional neural network architectures to centralize processing resources and enhance feature extraction from specific regions of interest.

[0200] According to various embodiments (including but not limited to the above references) Figures 1A to 4BThe signal analysis module 314 / image analysis module 404 (described in the embodiments) can be implemented in various computing systems, including a laptop computer 500. Figure 5 An example of a laptop computer 500 is shown. Many laptop computers include a touchpad touch surface 517 that serves as an indicator for the computer, and therefore these devices may receive gesture operations such as dragging, scrolling, and swiping, similar to the gesture operations implemented by computing devices equipped with touchscreen displays described above. Laptop computer 500 typically includes a processor 502 coupled to volatile memory 512 and a disk drive 513 containing mass non-volatile memory (e.g., flash memory). Additionally, computer 500 may have one or more antennas 508 for transmitting and receiving electromagnetic radiation, which may be connected to a wireless data link and / or a cellular telephone transceiver 516 coupled to processor 502. Computer 500 may also include a floppy disk drive 514 and a compact disc (CD) drive 515 coupled to processor 502. In a notebook configuration, the computer enclosure includes a touchpad 517, a keyboard 518, and a display 519, all coupled to the processor 502. Other configurations of the computing device may include a computer mouse or trackball coupled to the processor (e.g., via USB input), which are well-known and can be used in various embodiments.

[0201] The following discussion concerns methods and method behavior. Although method steps are discussed in a specific order or shown in a flowchart as being executed in a specific order, a specific order is not required unless explicitly stated or required, because a specific order is only necessary when one step depends on the completion of another step before it can be executed.

[0202] Now refer to Figure 6 Describe the embodiments, Figure 6 A flowchart of an example method 600 for scanning a tool surface (e.g., pick-and-place tool 102 or probe card 202) is shown according to an embodiment of the present invention. In this embodiment, step 602 includes providing a tool having a bottom surface. Reference Figures 1A to 1C as well as Figures 2A to 2C In step 602 of method 600, the tool may be a pick-and-place tool 102 or a probe card 202. The bottom surface 116 of the pick-and-place tool 102 may include an end 106. The bottom surface 214 of the probe card 202 may include a contact element 204. In an embodiment, the bottom surface 116 of the pick-and-place tool 102 may include a defect 112. Alternatively, in an embodiment, the bottom surface 214 of the probe card 202 may include a defect 212.

[0203] In the embodiment method, step 604 includes scanning the bottom surfaces 116 and 214 using a surface scanning tool, wherein the surface scanning tool includes a scanning tool and a signal analysis module. (Reference) Figures 1A to 1C , Figures 2A to 2C , Figure 3A as well as Figure 3B In step 604 of method 600, the surface scanning tool 300 may include a laser 302, a detector 304, and a signal analysis module 314. The laser 302 may be an ultraviolet laser, a visible laser, an infrared laser, or a helium-neon laser. Other suitable lasers are also within the scope of this invention. In some embodiments, the surface scanning tool 300 is located between the feeder system and the target wafer 110. Steps 606 to 610 describe additional sub-steps in the scanning process of step 604.

[0204] In one embodiment, step 606 includes sending a narrow beam 312a from the laser 302 toward the bottom surface 306. (See reference...) Figure 3A as well as Figure 3B In step 606 of method 600, laser 302 illuminates beam 312a onto the bottom surface 306 of tool 308. In some embodiments, beam 312a has a wavelength between approximately 250 nm and approximately 950 nm, approximately 350 nm and approximately 900 nm, or approximately 450 nm and approximately 750 nm.

[0205] In some embodiments, step 608 includes receiving a set of reflected light from the bottom surface at the detector. (Reference) Figure 3A as well as Figure 3B In step 608 of method 600, detector 304 receives reflected light 312b from the bottom surface 306 of tool 308, which is light 312a emitted from laser 302. In some embodiments, this set of reflected light includes a primary reflected light signal 312b. This set of reflected light includes a primary reflected light signal 312b that occurs when the bottom surface 306 is normal or has no defects 318. In other embodiments, this set of reflected light includes a scattered reflected light signal 312c. This set of reflected light includes a scattered reflected light signal 312c that occurs when the bottom surface 306 includes defects 318 or anomalies.

[0206] In some embodiments, step 610 includes transmitting this set of reflected light signals 312b to the signal analysis module 314. (See reference...) Figure 3A as well as Figure 3BIn step 610 of method 600, detector 304 transmits this set of reflected light signals to signal analysis module 314. In some embodiments, detector 304 preprocesses the reflected light signal 312b and the scattered light signal 312c. In other embodiments, signal analysis module 314 processes the received signals.

[0207] In some embodiments, step 612 includes determining the surface shape of the bottom surface 306 using a signal analysis module 314. (See reference...) Figure 3A as well as Figure 3B In step 612 of method 600, signal analysis module 314 determines the surface shape of bottom surface 306. In some embodiments, the determined shape is a binary determination, such as normal or abnormal. In other embodiments, this determination includes information about the presence of defect 318. For example, this determination may generate a topological file or visual representation of bottom surface 306. In some embodiments, signal analysis module 314 may further detect features to be placed on wafer surface 110, such as electrical components 108.

[0208] In some embodiments, step 614 includes performing an action based on the determined surface shape. (See reference...) Figure 3A as well as Figure 3B In step 614 of method 600, a surface shape determination triggers an action. In some embodiments, such as when the surface shape is determined to be normal or without defect 318, the signal analysis module 314 may trigger the tool 308 to continue the pick-and-place or analysis process. In some embodiments, such as when the surface shape is determined to be abnormal or includes defect 318, the signal analysis module 314 may trigger a mitigation action. The mitigation action may include sending a warning, such as light, noise, or a pop-up on the display device 316. The action may include initiating a rescan of the bottom surface 306. In other embodiments, the mitigation action may include stopping the tool 308 process or initiating a cleaning process for the tool 308. In another embodiment, the mitigation action may include initiating a rescan of the bottom surface 306.

[0209] Now refer to Figure 7 Describe the embodiments, Figure 7 A flowchart of an example method 700 for scanning the surface of a tool (e.g., a pick-and-place tool or probe card) is shown according to embodiments of the present invention. In some embodiments, step 702 includes providing a tool having a bottom surface. Reference Figures 1A to 1C as well as Figures 2A to 2CIn step 702 of method 700, the tool may be a pick-and-place tool 102 or a probe card 202. The bottom surface 116 of the pick-and-place tool 102 may include an end 106. The bottom surface 214 of the probe card 202 may include a contact element 204. In some embodiments, the bottom surface 116 of the pick-and-place tool 102 may include a defect 112. Alternatively, in some embodiments, the bottom surface 214 of the probe card 202 may include a defect 212.

[0210] In the embodiment method, step 704 includes scanning the bottom surface using a surface scanning tool, wherein the surface scanning tool includes a scanning tool and a signal analysis module. (Reference) Figures 1A to 1C , Figures 2A to 2C , Figure 4A as well as Figure 4B In step 704 of method 700, the surface scanning tool 400 includes a camera 402 and an image analysis module 404. In some embodiments, the camera 402 may be a high-speed camera or a high-resolution camera. In some embodiments, the surface scanning tool 400 is located between the feeder system and the target wafer 110. Steps 706 and 708 describe additional sub-steps in the scanning process of step 704.

[0211] In some embodiments, step 706 includes capturing an image of the bottom surface. (Reference) Figure 4A as well as Figure 4B In step 706 of method 700, camera 402 captures an image 416 of the bottom surface 406 of tool 408. In some embodiments, camera 402 captures a single image 416. In other embodiments, camera 402 captures multiple images from one or more viewpoints. The one or more images may have different parameters, such as viewpoint, shutter speed, resolution, etc.

[0212] In some embodiments, step 708 includes transmitting the image to a signal analysis module. (Reference) Figure 4A as well as Figure 4B In step 708 of method 700, camera 402 transmits image 416 to image analysis module 404. In some embodiments, camera 402 transmits a single image 416 to signal analysis module 404. In other embodiments where camera 402 captures more than one image, camera 402 transmits all images or selects a single image or subset of images to transmit to image analysis module 404.

[0213] In some embodiments, step 710 includes determining the surface shape of the bottom surface using a signal analysis module. (Reference) Figure 4A as well as Figure 4BIn step 708 of method 700, signal analysis module 404 receives image 416 and uses image 416 as input. In some embodiments, image analysis module 404 determines the surface shape of bottom surface 406. The surface shape of bottom surface 406 may or may not include defects. In some embodiments, image analysis module 404 is a machine learning model trained using a reference image and / or other image data. Image analysis module 404 compares image 416 with the reference image to determine a similarity value. If the similarity value meets a threshold, image analysis module 404 determines that bottom surface 406 is normal. If the similarity value is below the threshold, image analysis module 404 determines that bottom surface 406 is abnormal or has a defect 418. In some embodiments, signal analysis module 314 may further detect features to be placed on wafer surface 110, such as electrical components 108.

[0214] In some embodiments, step 712 includes performing an action based on the determined surface shape. (See reference...) Figure 4A as well as Figure 4B In step 712 of method 700, the determination of surface shape triggers an action. In embodiments where the surface shape is determined to be normal or without defects 418, the image analysis module 404 may trigger the tool 408 to continue the pick-and-place or analysis process. In embodiments where the surface shape is determined to be abnormal or has defects 418, the image analysis module 404 triggers a mitigation action. The mitigation action may include sending a warning, such as light, noise, or a pop-up on the display device 424. In other embodiments, the mitigation action may include stopping the tool 408 process or initiating a cleaning process for the tool 408. In another embodiment, the mitigation action may include taking a subsequent image of the bottom surface 406. In other embodiments, the mitigation action includes adding image 416 to an updated image set and using the updated image set to train the image analysis module 404.

[0215] Referring to all the accompanying drawings and various embodiments of the present invention, the surface scanning tool 300 may include a laser 302, a detector 304, and a signal analysis module 314. The laser 302 is configured to irradiate a beam 312a onto the bottom surface 306 of the tool 308. The detector 304 is configured to receive a set of reflected light 312b from the bottom surface 306 of the tool 308. The signal analysis module 314 is configured to receive the set of reflected light 312b and determine the surface shape of the bottom surface 306 of the tool 308.

[0216] In an embodiment, the surface scanning tool 300 may further include a display device 316 configured to display a visual representation of the bottom surface 306 of the tool 308, wherein the visual representation is generated by a signal analysis module 314.

[0217] In some embodiments, the light beam 312a may have a wavelength between approximately 300 nm and approximately 950 nm, or approximately 350 nm and approximately 900 nm, or approximately 400 nm and approximately 800 nm. In some embodiments, if the set of light reflected from the tool 308 includes a primary reflected light signal 312b, the signal analysis module 314 may determine that the surface shape is normal. In some embodiments, if the set of light reflected from the tool 308 includes a scattered reflected light signal 312c, the signal analysis module 314 may determine that the surface shape is abnormal. In some embodiments, the signal analysis module 314 is further configured to trigger an action. In embodiments where the surface shape is normal, this action includes: continuing to use the tool 308; rescanning the bottom surface 306. In embodiments where the surface shape is abnormal, this action includes at least one of: sending a warning signal, stopping the use of the tool 308, initiating a cleaning cycle for the tool 308, or rescanning the bottom surface 306. In some embodiments, a combination of these actions may be performed. In some embodiments, the laser 302 is further configured to illuminate the bottom surface 306 of the tool 308. In some embodiments, tool 308 is pick-and-place tool 102 or probe card 202. In some embodiments, laser 302 may be a UV laser, visible light laser, IR laser, or helium-neon laser. In some embodiments, the bottom surface 306 of tool 308 is anomalous due to defect 318, wherein defect 318 is a protrusion or contaminant. In some embodiments, the bottom surface 306 may be scanned before tool 308 contacts target location 118.

[0218] In another embodiment, the surface scanning tool 400 includes a camera 402 configured to capture an image 416 of the bottom surface 406 of the tool 408, and an image analysis module 404 configured to receive the image 416 and determine the surface shape of the bottom surface 406 of the tool 408.

[0219] In some embodiments, the surface scanning tool 400 further includes a display device 424 configured to display an image 416 of the bottom surface 406 of the tool 408. In some embodiments, the image analysis module 404 may determine that the bottom surface 406 is normal if the similarity value between the image 416 and the reference image at least meets a threshold. In some embodiments, the signal analysis module 404 may determine that the bottom surface 406 is abnormal if the similarity value is below a threshold. In some embodiments, the signal analysis module 404 is further configured to trigger an action. In embodiments where the surface shape is determined to be normal, this action may include continuing to use the tool. In embodiments where the surface shape is determined to be abnormal, this action may include at least one of the following: sending a warning signal, stopping the use of the tool 408, initiating a cleaning cycle of the tool 408, or taking a subsequent image of the bottom surface 406. In some embodiments, a combination of these actions may be performed in response to determining that the surface shape is abnormal. In some embodiments, the image analysis module 404 is a machine learning model trained using a set of images including the reference image. In some embodiments, the tool 308 is a pick-and-place tool 102 or a probe card 202. In some embodiments, camera 402 is a high-speed camera that captures images in less than approximately 0.0001 seconds, less than approximately 0.002 seconds, less than approximately 0.005 seconds, or less than approximately 0.01 seconds. In some embodiments, camera 402 is a high-resolution camera with a resolution of approximately 0.04 mm, approximately 0.05 mm, approximately 0.07 mm, approximately 0.08 mm, or approximately 0.1 mm. In some embodiments, image analysis module 404 is a machine learning model trained using a set of images including a reference image. In some embodiments, image 416 is added to a set of updated images for retraining the machine learning model. In some embodiments, image analysis module 404 adds identifiers to image 416. In some embodiments, the bottom surface 406 is scanned before tool 408 contacts target location 118.

[0220] In another embodiment, a method 600 for scanning the bottom surface 306 of a tool 308 includes providing a tool 308 having a bottom surface 306; scanning the bottom surface using a surface scanning tool 300, wherein the surface scanning tool 300 includes a scanning tool and a signal analysis module 314; determining the surface shape of the bottom surface 306 by the signal analysis module 314; and performing an action based on the determined surface shape.

[0221] In some embodiments, the surface scanning tool 300 includes a laser 302 and a detector 304. Scanning the bottom surface 306 further includes sending a narrow beam 312a from the laser 302 to the bottom surface 306; receiving a set of reflected light 312b from the bottom surface 306 at the detector 304; and sending this set of reflected light 312b to a signal analysis module 314. In some embodiments, the surface shape is determined to be normal when the set of reflected light includes a primary reflected light 312b, and abnormal when the set of reflected light includes scattered light 312c. In some embodiments where the surface shape is determined to be normal, this action includes continuing to use the tool 308. In some embodiments where the surface shape is determined to be abnormal, this action may include at least one of the following: sending a warning signal, stopping the use of the tool 308, initiating a cleaning cycle for the tool 308, or rescanning the bottom surface 306 using the surface scanning tool 300. In some embodiments, a combination of these actions may be performed. In some embodiments, the bottom surface 306 may be scanned before the tool 308 contacts the target location 118. In some embodiments, this method also includes detecting features to be placed on the wafer surface.

[0222] In another embodiment, a method 700 for scanning the bottom surface 406 of a scanning tool 408 includes providing a tool 408 having a bottom surface 406; scanning the bottom surface 406 using a surface scanning tool 400, wherein the surface scanning tool 400 includes a scanning tool and an image analysis module 404; determining the surface shape of the bottom surface 406 by the signal analysis module 404; and performing an action based on the determined surface shape.

[0223] In some embodiments, the scanning tool 400 may include a camera 402, and scanning the bottom surface 406 may include capturing an image 416 of the bottom surface 406 and transmitting the image 416 to a signal analysis module 404. In some embodiments, the surface shape may be determined to be normal if the similarity value between the image 416 and the reference image meets a threshold, and may be determined to be abnormal if the similarity value between the image 416 and the reference image is below the threshold. In some embodiments where the surface shape is determined to be normal, various actions may be performed. These actions may include continuing to use the tool 408. In some embodiments where the surface shape is determined to be abnormal, this action may include at least one of the following: sending a warning signal, stopping the use of the tool, initiating a cleaning cycle for the tool, or rescanning the bottom surface 406 using the scanning tool 400. In some embodiments, a combination of these actions may be performed. In some embodiments, rescanning the bottom surface 406 includes capturing a subsequent image of the bottom surface 406 using the camera 402. In some embodiments, the bottom surface 406 is scanned before the tool 408 contacts the target location 118.

[0224] The various embodiments disclosed herein offer various advantages and improvements. For example, these embodiments can quickly identify defects and / or contaminants on the pick-and-place tool before moving electrical components from the transfer surface to the target surface. This reduces the likelihood of damage to the electrical components and / or the target surface. The various embodiments disclosed herein can provide immediate analysis of the pick-and-place end surface before further contamination or damage to other surfaces. Additionally, the various embodiments disclosed herein can immediately notify the user of detected defects and / or contamination. The various embodiments disclosed herein can further take appropriate mitigation actions before damage and / or contamination of the target surface, and in some cases, this is done automatically without user intervention.

[0225] The foregoing outlines the features of numerous embodiments, enabling those skilled in the art to better understand the present invention from various aspects. Those skilled in the art will understand that other processes and structures can be easily designed or modified based on the present invention to achieve the same purpose and / or the same advantages as the embodiments described herein. Those skilled in the art will also understand that these equivalent structures do not depart from the spirit and scope of the present invention. Various changes, substitutions, or modifications can be made to the present invention without departing from its spirit and scope.

Claims

1. A surface scanning tool, characterized in that, include: A laser, configured to direct a beam of light onto a bottom surface of a tool; A detector configured to receive a set of reflected light from the bottom surface of the tool; as well as A signal analysis module is configured to receive the set of reflected light and determine a surface shape of the bottom surface of the tool.

2. The surface scanning tool as described in claim 1, characterized in that, It also includes a display device configured to display a visual representation of the bottom surface of the tool, wherein the visual representation is generated by the signal analysis module.

3. The surface scanning tool as described in claim 1, characterized in that, The beam has a wavelength between 300 nm and 950 nm.

4. The surface scanning tool as described in claim 1, characterized in that, The signal analysis module determines whether the surface shape is normal by analyzing the set of reflected light from the tool, which includes several main reflected light signals.

5. The surface scanning tool as described in claim 1, characterized in that, The signal analysis module determines the surface shape abnormality by judging that the set of reflected light reflected from the tool includes several scattered reflected light signals.

6. The surface scanning tool as described in claim 1, characterized in that, The signal analysis module is further configured to trigger an action.

7. The surface scanning tool as described in claim 6, characterized in that, In response to determining that the surface shape is normal, the action includes: continuing to use the tool; In response to determining that the surface shape is abnormal, the action includes at least one of the following: Send a warning signal; Stop using this tool; Initiate a cleaning cycle for this tool; or Rescan the bottom surface.

8. The surface scanning tool as described in claim 1, characterized in that, The laser is further configured to irradiate the bottom surface of the tool.

9. A surface scanning tool, characterized in that, include: A camera, configured to capture an image of a bottom surface of a tool; as well as An image analysis module, configured with: Receive the image of the bottom surface of the tool; and Determine the surface shape of the bottom surface of the tool.

10. The surface scanning tool as described in claim 9, characterized in that, It also includes a display device configured to display the image of the bottom surface of the tool.

Images