TW201825860A - Optical measurement of bump height - Google Patents

Abstract

A method of generating 3D information including: varying the distance between the sample and an objective lens of the optical microscope at pre-determined steps; capturing an image at each pre-determined step; determining a characteristic value of each pixel in each captured image; determining, for each captured image, the greatest characteristic value across a first portion of pixels in the captured image; comparing the greatest characteristic value for each captured image to determine if a surface of the sample is present at each pre-determined step; determining a first captured image that is focused on an apex of a bump of the sample; determining a second captured image that is focused on a first surface of the sample based on the characteristic value of each pixel in each captured image; and determining a first distance between the apex of the bump and the first surface.

Description

Optical measurement of bump height

The described embodiments are generally directed to measuring the same three-dimensional information and, more particularly, about automatically measuring three-dimensional information in a fast and reliable manner.

Three-dimensional (3-D) measurements of various objects or samples are useful in many different applications. One such application is during wafer level packaging processing. The three-dimensional measurement information of one of the wafers during the different steps of wafer level fabrication provides insight into the presence of wafer processing defects that may be present on the wafer. The 3D measurement information of the wafer during wafer level fabrication provides insight into the absence of defects before additional funds are available to continue processing the wafer. Currently, a three-dimensional measurement information is collected by human manipulation of a microscope. The human user uses his eye to focus the microscope to determine when the microscope is focused on one of the surfaces of the sample. An improved method for collecting 3D measurement information.

In a first novel aspect, an optical microscope is used to generate the same three-dimensional (3-D) information by changing the distance between the sample and an objective lens of the optical microscope in a predetermined step; Determining an image at a predetermined step; determining a characteristic value of each pixel in each captured image; determining, for each captured image, a maximum characteristic value across all pixels in the captured image; comparing each of the selected images Taking the maximum characteristic value of the image to determine whether a surface of the sample exists at each predetermined step; determining, based on the characteristic value of each pixel in each captured image, focusing on one of the first surfaces of the sample Capturing an image; determining, based on the characteristic value of each pixel in each captured image, a second captured image focused on a second surface of the sample; and determining the first surface and the second A first distance between the surfaces. In a second novel aspect, a three-dimensional (3-D) measurement system includes: determining a thickness of one of the translucent layers of the sample; and determining a thickness of one of the metal layers of the sample, wherein the thickness of the metal layer And a difference between the thickness of the translucent layer and the first distance, wherein the first surface is a top surface of a photoresist layer, and wherein the second surface is a top surface of a metal layer. In a third novel aspect, an optical microscope is used to generate the same three-dimensional (3-D) information by changing the distance between the sample and one of the objective lenses of the optical microscope in a predetermined step; Determining an image at a predetermined step; determining a characteristic value of each pixel in each captured image; determining, for each captured image, a maximum characteristic value of the first portion of one of the pixels in the captured image; comparing Determining, by each of the maximum characteristic values of the image, whether one of the surfaces of the sample exists at each predetermined step; determining that the first captured image is focused on one of the vertices of one of the bumps of the sample; Determining, by the characteristic value of each pixel in the image, a second captured image focused on one of the first surfaces of the sample; and determining a first between the vertex of the bump and the first surface distance. In a fourth novel aspect, determining a maximum characteristic value of each of the xy pixel positions in a second portion of one of the xy pixel positions across the captured image, wherein the second portion of the xy pixel position is included in each At least some of the xy pixel locations included in the image are captured; determining a subset of the captured images, wherein the captured image including only the maximum characteristic value of the xy pixel location is included in the subset; and determining Among all the captured images in the captured image subset, the first captured image is focused at a highest z position compared to all other captured images in the captured image subset. Further details and embodiments and techniques are described in the following embodiments. This summary does not define the invention. The invention is defined by the scope of the invention patent application.

CROSS-REFERENCE TO RELATED APPLICATIONS This application is hereby incorporated by reference in its entirety in its entire entire entire entire entire entire entire entire entire entire entire entire entire content . S. C.  § 120 stipulates the priority of the case. The entire disclosure of this disclosure is incorporated herein by reference. Application 15/338,838 is part of the non-provisional U.S. Patent Application Serial No. 15/233,812, filed on August 10, 2016, entitled "AUTOMATED 3-D MEASUREMENT", and is based on 35 U. S. C.  § 120 stipulates the priority of the case. The entire disclosure of this disclosure is incorporated herein by reference.  Reference will now be made in detail to Examples of such are shown in the accompanying drawings. In the scope of the following description and invention patent application, Such as "top", "below", "on", "under", "top", "bottom", The term "left" and "right" may be used to describe the relative orientation between different parts of the described structure. And should understand that The overall structure described can be oriented in virtually any way in three dimensions.  Figure 1 is a diagram of one of the automated three-dimensional metering systems 1. The semi-automated three-dimensional metering system 1 comprises an optical microscope (not shown), An on/off button 5, A computer 4 and a stage 2. In operation, A wafer 3 is placed on the stage 2. The function of the semi-automated 3D metrology system 1 captures multiple images of an object and automatically generates three-dimensional information describing the various surfaces of the object. This is also known as "scanning" one of the objects. Wafer 3 is an example of one of the objects analyzed by the semi-automated three-dimensional metering system 1. An object can also be called the same book. In operation, The wafer 3 is placed on the stage 2 and the semi-automated three-dimensional metering system 1 begins to automatically generate a program describing the three-dimensional information of the surface of the wafer 3. In one example, The semi-automated three-dimensional metering system 1 begins by pressing a designated button that is connected to a keyboard (not shown) of the computer 4. In another example, The semi-automated three-dimensional metering system 1 begins by sending a start command to the computer 4 across a network (not shown). The semi-automated 3D metering system 1 can also be configured to mate with half of an automated wafer handling system (not shown). The automated wafer handling system removes the wafer after scanning one of the wafers and inserts a new wafer for scanning.  A fully automated three-dimensional metering system (not shown) similar to the semi-automated three-dimensional metering system of Figure 1; however, A fully automated 3D metering system also includes a robotic handler, It automatically picks up a wafer without human intervention and places the wafer on the stage. In a similar way, A fully automated 3D metering system can also automatically pick up a wafer from the stage using a robotic handler and remove the wafer from the stage. A fully automated 3D metrology system can be expected during the production of many wafers. Because it avoids the possibility of contamination by a human operator and improves time efficiency and total cost. Alternatively, When only a small amount of wafer needs to be measured, A semi-automated three-dimensional metering system 1 can be desired during research and development activities.  2 is a diagram of a three-dimensional imaging microscope 10 including a plurality of objective lenses 11 and an adjustable stage 12. The three-dimensional imaging microscope can be a confocal microscope, a structured illumination microscope, An interferometer microscope or any other type of microscope well known in the art. A confocal microscope will measure the intensity. A structured illumination microscope will measure the contrast of a projected structure. An interferometer microscope will measure the interference fringe contrast.  In operation, A wafer is placed on the adjustable stage 12 and an objective lens is selected. The three-dimensional imaging microscope 10 captures multiple images of the wafer while adjusting the height of the stage on which the wafer rests. This results in multiple images of the wafer being taken while the wafer is being positioned at various distances away from the selected lens. In an alternative example, Place the wafer on a fixed stage and adjust the position of the objective lens, Thereby, the distance between the objective lens and the sample is changed without moving the stage. In another example, The stage can be adjusted in the x-y direction and the objective lens can be adjusted in the z direction.  The captured image can be stored locally in one of the memories included in the three-dimensional imaging microscope 10. Alternatively, The captured image can be stored in a data storage device included in a computer system. The three-dimensional microscope 10 transmits the captured image to the computer system across a data communication link. An example of a data communication link includes: A universal serial bus (USB) interface, An Ethernet connection, a firewire bus interface, A wireless network (such as WiFi).  Figure 3 includes a three-dimensional microscope 21, a sample handler 22, a computer 23, A display 27 (optional) and a map of one of the three-dimensional metering systems 20 of the input device 28. The three-dimensional metering system 20 is an example of one of the systems included in the semi-automated three-dimensional metering system 1. The computer 23 includes a processor 24, A storage device 25 and a network device 26 (optional). The computer outputs the information to a user via the display 27. If the display 27 is a touch screen device, The display can also be used as an input device. Input device 28 can include a keyboard and a mouse. The computer 23 controls the operation of the three-dimensional microscope 21 and the sample handler/stage 22. When a start scan command is received by the computer 23, The computer sends one or more commands to configure a 3D microscope for image capture ("Microscope Control Data"). E.g, Need to choose the correct objective lens, Need to select the resolution of the image to be captured, And you need to choose the mode for storing captured images. When a start scan command is received by the computer 23, The computer sends one or more commands to configure the sample handler/stage 22 ("Processor Control Data"). E.g, You need to select the correct height (z-direction) adjustment and choose the correct horizontal (x-y direction) alignment.  During operation, Computer 23 causes sample handler/stage 22 to be adjusted to the appropriate position. Once the sample handler/stage 22 is properly positioned, The computer 23 will cause the three-dimensional microscope to focus on a focal plane and capture at least one image. then, Computer 23 will cause the stage to move in the z direction, This changes the distance between the sample and the objective lens of the optical microscope. Once the stage moves to the new location, The computer 23 will cause the optical microscope to capture a second image. This procedure continues until an image is taken at each desired distance between the objective lens of the optical microscope and the sample. The images captured at the respective distances are transmitted from the three-dimensional microscope 21 to the computer 23 ("image data"). The captured image is stored in a storage device 25 included in the computer 23. In one example, The computer 23 analyzes the captured image and outputs the three-dimensional information to the display 27. In another example, The computer 23 analyzes the captured image and outputs the three-dimensional information to a remote device via the network 29. In yet another example, The computer 23 does not analyze the captured image, Instead, the captured image is sent to another device for processing via network 29. The three-dimensional information can include rendering a three-dimensional image based on the captured image. 3D information can contain no images. Rather, it contains information based on various characteristics of each captured image.  4 is a view showing one of the methods of capturing an image when changing the distance between the objective lens of the optical microscope and the sample. In the embodiment illustrated in Figure 4, Each image contains 1000 by 1000 pixels. In other embodiments, Images can contain a variety of pixel configurations. In one example, The interval between successive distances is fixed to a predetermined amount. In another example, The interval between consecutive distances may not be fixed. If only one part of the z-direction scan of the sample requires additional z-direction resolution, This unfixed spacing between images in the z direction can be advantageous. The z-direction resolution is based on the number of images captured per unit length in the z-direction. Therefore, extracting additional images per unit length in the z direction will increase the measured z-direction resolution. Conversely, Placing fewer images per unit length in the z direction will reduce the measured z-direction resolution.  As discussed above, The optical microscope is first adjusted to focus on one of the focal planes located at a distance of one from the objective lens of the optical microscope. then, Taking an image with an optical microscope, The image is stored in a storage device (ie, In "memory"). then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of two. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of three. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of four. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of 5. then, Taking an image with an optical microscope, The image is stored in a storage device. The procedure continues for N different distances between the objective lens of the optical microscope and the sample. Information indicating which image is associated with each distance is also stored in the storage device for processing.  In an alternate embodiment, The distance between the objective lens of the optical microscope and the sample is fixed. Real facts, The optical microscope includes a zoom lens, It allows an optical microscope to change the focal plane of the optical microscope. In this way, When the stage and the sample supported by the stage are fixed, The focal plane of the optical microscope varies across N different focal planes. An image is captured for each focal plane and the image is stored in a storage device. then, The captured image is processed across all of the focal planes to determine the three-dimensional information of the sample. This embodiment requires a zoom lens, It provides sufficient resolution across all focal planes and introduces minimal image distortion. In addition, The calibration between the zoom positions and the resulting focal length of the zoom lens are required.  Figure 5 is a graph showing the distance between the objective lens of the optical microscope and the sample, Each of the x-y coordinates has a maximum characteristic value. Once images are captured and stored for each distance, The characteristics of each pixel of each image can be analyzed. E.g, The light intensity of each pixel of each image can be analyzed. In another example, The contrast of each pixel of each image can be analyzed. In yet another example, The stripe contrast of each pixel of each image can be analyzed. The contrast of a pixel can be determined by comparing the intensity of a pixel with the intensity of a predetermined number of surrounding pixels. For additional descriptions on how to generate contrast information, See U.S. Patent Application Serial No. 12/699, entitled "3-D Optical Microscope", filed on February 3, 2010, by No. 824 (the subject matter of which is incorporated herein by reference).  Figure 6 is a three-dimensional representation of one of the three dimensional images using the maximum characteristic values of the x-y coordinates shown in Figure 5. All pixels having an X position between 1 and 19 have a maximum characteristic value at a distance 7 in the z direction. All pixels having an X position between 20 and 29 have a maximum characteristic value at a distance 2 in the z direction. All pixels having an X position between 30 and 49 have a maximum characteristic value at a distance 7 in the z direction. All pixels having an X position between 50 and 59 have a maximum characteristic value at a distance 2 in the z direction. All pixels having an X position between 60 and 79 have a maximum characteristic value at a distance 7 in the z direction. In this way, The three-dimensional image depicted in Figure 6 can be generated using the maximum characteristic value for each x-y pixel of all captured images. In addition, In the case where the distance 2 is known and the distance 7 is known, The depth of the well depicted in Figure 6 can be calculated by subtracting the distance 7 from distance 2.  Peak Mode Operation Figure 7 is a graph showing peak mode operation using images captured at various distances. As discussed above with respect to Figure 4, The optical microscope is first adjusted to focus on a plane that is positioned at a distance of one from one of the objective lenses of the optical microscope. then, Taking an image with an optical microscope, The image is stored in a storage device (ie, In "memory"). then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of two. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of three. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of four. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of 5. then, Taking an image with an optical microscope, The image is stored in a storage device. The procedure continues for N different distances between the objective lens of the optical microscope and the stage. Information indicating which image is associated with each distance is also stored in the storage device for processing.  Determining the maximum characteristic value of all x-y positions in a single captured image at one z-distance in peak mode operation, Rather than determining the maximum characteristic value for each x-y position of all captured images across various z-distances. In other words, For each captured image, Select the maximum characteristic value across all pixels included in the captured image. As shown in Figure 7, The pixel location with the largest characteristic value will likely vary between different captured images. Characteristics can be strength, Contrast or stripe contrast.  Figure 8 is a graph showing the peak mode operation of images taken at various distances when a photoresist (PR) opening is in the field of view of an optical microscope. The top view of the object shows the cross-sectional area of the PR opening in the x-y plane. The PR opening also has a depth of a particular depth in the z direction. The top view in Figure 8 below shows images captured at various distances. At distance 1, The optical microscope is not focused on the top surface of the wafer or the bottom surface of the PR opening. At distance 2, The optical microscope is focused on the bottom surface of the PR opening, But not focused on the top surface of the wafer. This results in a pixel that is received from light that is reflected from other surfaces of the defocus (the top surface of the wafer) One of the pixels received from the light reflected from the bottom surface of the PR opening increases the characteristic value (intensity/contrast/streak contrast). At a distance of 3, The optical microscope is not focused on the top surface of the wafer or the bottom surface of the PR opening. therefore, At a distance of 3, The maximum characteristic value will be substantially lower than the characteristic value measured at distance 2. At distance 4, The optical microscope is not focused on any surface of the sample; however, Due to the difference between the refractive index of air and the refractive index of the photoresist layer, One of the measured maximum characteristic values (intensity/contrast/streak contrast) is increased. Figure 11 and the accompanying text describe this phenomenon in more detail. At a distance of 6, The optical microscope is focused on the top surface of the wafer, However, it is not focused on the bottom surface of the PR opening. This results in a pixel that is received from light that is reflected from other surfaces of the defocus (the bottom surface of the PR opening) One of the pixels received from the light reflected from the top surface of the wafer increases the characteristic value (intensity/contrast/streak contrast). Once the maximum characteristic value from each captured image is determined, The results can be used to determine at which distances one of the wafer surfaces is located.  Figure 9 is a diagram showing one of three-dimensional information derived from peak mode operation. As discussed with respect to Figure 8, At distance 1, The maximum characteristic value of the images captured at 3 and 5 has less than the distance 2 One of the maximum characteristic values of the image captured at 4 and 6 and the maximum characteristic value. The curve of the maximum characteristic value at various z distances can be attributed to environmental effects such as vibrations and contain noise. To minimize this noise, A standard smoothing method can be applied before further data analysis, Such as Gaussian filtering with a certain core size.  A method of comparing the maximum characteristic values by a peak finding algorithm. In one example, The zero crossing point is located along the z-axis using a derivative method to determine the distance between each "peak". then, The maximum characteristic value at each distance at which a peak is found is compared to determine the distance at which the maximum characteristic value is measured. In the case of Figure 9, A peak will be found at distance 2, This is used as an indication that one of the surfaces of the wafer is positioned at a distance of two.  Another method of comparing the maximum characteristic values is performed by comparing each of the maximum characteristic values with a predetermined threshold. Based on wafer material, Distance and optical microscope specifications are used to calculate the threshold. Alternatively, The threshold can be determined by empirical testing prior to automated processing. In either case, Compare the maximum characteristic value and threshold value of each captured image. If the maximum characteristic value is greater than the threshold, It is then determined that the maximum characteristic value indicates the presence of one of the surfaces of the wafer. If the maximum characteristic value is not greater than the threshold, It is then determined that the maximum characteristic value does not indicate one of the surfaces of the wafer.  Summation Mode Operation FIG. 10 is a diagram showing the sum mode operation using images captured at various distances. As discussed above with respect to Figure 4, The optical microscope is first adjusted to focus on a plane that is positioned at a distance of one from one of the objective lenses of the optical microscope. then, Taking an image with an optical microscope, The image is stored in a storage device (ie, In "memory"). then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of two. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of three. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of four. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of 5. then, Taking an image with an optical microscope, The image is stored in a storage device. The procedure continues for N different distances between the objective lens of the optical microscope and the sample. Information indicating which image is associated with each distance is also stored in the storage device for processing.  Adding the characteristic values of all x-y positions of each captured image, Rather than determining the maximum characteristic value of all x-y positions in a single captured image spanning one z distance. In other words, For each captured image, The characteristic values of all the pixels included in the captured image are added together. Characteristics can be strength, Contrast or stripe contrast. One of the average summed characteristic values that is substantially greater than the adjacent z-distance, via the summed characteristic value, indicates that one of the wafer surfaces exists at that distance. however, This method can also result in a false positive as described in FIG.  Figure 11 is a diagram showing an error surface detection when operating in a summation mode. The wafer shown in FIG. 11 includes a germanium substrate 30 and a photoresist layer 31 deposited on top of the germanium substrate 30. The top surface of the crucible substrate 30 is positioned at a distance of two. The top surface of the photoresist layer 31 is positioned at a distance of 6. An image captured at distance 2 will result in a sum of characteristic values that are substantially greater than one of the other images captured at a distance from one of the surfaces of the wafer. The image captured at distance 6 will result in a sum that is substantially greater than the characteristic value of one of the other images captured at a distance from one of the surfaces of the wafer. at this time, The sum mode operation appears to be a valid indicator of one of the surfaces of the wafer. however, The image captured at distance 4 will result in a sum of characteristic values that are substantially greater than one of the other images captured at a distance from one of the wafer surfaces. This is a problem, Because as clearly shown in Figure 11, One of the wafer surfaces is not positioned at a distance of four. Real facts, The increase in the sum of the characteristic values at 4 distances is one of the artifacts located on the surface at distances 2 and 6. The main part of the light that irradiates the photoresist layer is not reflected. Instead, it travels into the photoresist layer. The angle at which this light travels is due to the difference in refractive index between air and photoresist. The new angle is closer to the normal than the light angle of the top surface of the irradiated photoresist. Light travels to the top surface of the germanium substrate below the photoresist layer. then, The light is reflected by the highly reflective 矽 substrate layer. When the reflected light leaves the photoresist layer and enters the air, The angle of the reflected light changes again due to the difference in refractive index between the air and the photoresist layer. This first re-directing of the irradiation light, The reflection and the second re-directing caused an increase in the characteristic value (intensity/contrast/streak contrast) at the distance 4 observed by the optical microscope. This example shows that whenever a transparent material is included, The sum mode operation will detect surfaces that are not present on the sample.  Figure 12 is a diagram showing one of three-dimensional information from the summation mode operation. This chart shows the results of the phenomenon depicted in Figure 11. The large value of the total characteristic value at the distance of 4 incorrectly indicates that there is a surface at the distance 4. One method of false positive indication that does not result in the presence of the surface of the wafer is required.  Range Mode Operation Figure 13 is a diagram showing a range mode operation using images captured at various distances. As discussed above with respect to Figure 4, The optical microscope is first adjusted to focus on a plane that is positioned at a distance of one from one of the objective lenses of the optical microscope. then, Taking an image with an optical microscope, The image is stored in a storage device (ie, In "memory"). then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of two. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of three. then, Taking an image with an optical microscope, The image is stored in a storage device. then, Adjust the stage, The distance between the objective lens of the optical microscope and the sample is such that the distance is 4. then, Taking an image with an optical microscope, The image is stored in a storage device. then, The stage is adjusted such that the distance between the objective lens of the optical microscope and the sample is a distance of 5. then, Taking an image with an optical microscope, The image is stored in a storage device. The procedure continues for N different distances between the objective lens of the optical microscope and the sample. Information indicating which image is associated with each distance is also stored in the storage device for processing.  Determining, by one of a plurality of pixels having a characteristic value within a particular range, in a single captured image at a z-distance, Rather than determining the sum of all characteristic values across all x-y locations in the single captured image. In other words, For each captured image, A count of one of the pixels having one of the characteristic values within a particular range is determined. Characteristics can be strength, Contrast or stripe contrast. One of the pixel counts at a particular z-distance that is substantially greater than the average pixel count at the adjacent z-distance indicates that one of the wafers exists at that distance. This method reduces the false positives described in Figure 11.  Figure 14 is a diagram showing one of three-dimensional information derived from range mode operation. Knowing the different material types and optical microscope configurations present on the wafer, One expected range of characteristic values can be determined for each material type. E.g, The photoresist layer will reflect a relatively small amount of light on the top surface of the irradiated photoresist layer (ie, 4%). The layer of germanium will reflect the light of the top surface of the irradiated layer (ie, 37%). Redirected reflection from the top surface of the photoresist layer observed at distance 4 (ie, 21%) will be substantially larger than the reflection observed at distance 6; however, Redirected reflection from the top surface of the germanium substrate observed at distance 4 (ie, 21%) will be substantially less than the reflection observed at distance 2. therefore, When looking for the top surface of the photoresist layer, The first range, centered on the expected characteristic value of the photoresist, can be used to filter out pixels having characteristic values outside the first range, Thereby, pixels having characteristic values that are not derived from the reflection of the top surface of the photoresist layer are filtered out. A pixel count across all distances generated by applying a first range of characteristic values is depicted in FIG. As shown in Figure 15, Some but not necessarily all pixels from other distances (surfaces) are filtered out by applying the first range. This occurs when the characteristic values measured at a plurality of distances fall within the first range. however, Applying the first range before counting the pixels is still used to make the pixel count at the desired surface more prominent than other pixel counts at other distances. This is illustrated in Figure 15. After applying the first range, The pixel count at 6 is greater than the pixel count at distances 2 and 4. Before applying the first range, The pixel count at distance 6 is less than the pixel count at distances 2 and 4 (as shown in Figure 14).  In a similar way, When looking for the top surface of the ruthenium substrate layer, A pixel having a characteristic value outside the second range may be filtered using a second range that is a center of a desired property value of the germanium substrate layer. Thereby, pixels having characteristic values that are not derived from the reflection of the top surface of the ruthenium substrate layer are filtered out. A pixel count across all distances generated by applying a second range of characteristic values is depicted in FIG. This range application reduces the erroneous indication that a wafer surface is positioned at a distance 4 by knowing the expected characteristic values of all materials present on the scanned wafer. As discussed with respect to Figure 15, Some but not necessarily all pixels from other distances (surfaces) are filtered out by applying a range. however, When the characteristic values measured at a plurality of distances do not fall within the same range, The result of the application range will eliminate all pixel counts from other distances (surfaces). Figure 16 shows this case. In Figure 16, The second range is applied before the pixel count at each distance is generated. The result of applying the second range is to count only pixels at distance 2. This is a very clear indication that the surface of the germanium substrate is positioned at a distance of two.  It should be noted that To reduce the effects of potential noise, such as environmental vibrations, A standard smoothing operation, such as Gaussian filtering, can be applied to the total pixel count along the z-distance before any peak seek operation is performed.  Figure 17 is a flow chart diagram 200 of various steps involved in peak mode operation. In step 201, The distance between the sample and the objective lens of an optical microscope is changed in a predetermined step. In step 202, An image is captured at each predetermined step. In step 203, A characteristic of each pixel in each captured image is determined. In step 204, For each captured image, A determination is made as to the maximum characteristics of all pixels in the captured image. In step 205, The maximum characteristics of each captured image are compared to determine if one of the surfaces of the sample is present at each predetermined step.  Figure 18 is a flow diagram 300 showing one of the various steps involved in range mode operation. In step 301, The distance between the sample and the objective lens of an optical microscope is changed in a predetermined step. In step 302, An image is captured at each predetermined step. In step 303, A characteristic of each pixel in each captured image is determined. In step 304, For each captured image, A count of one of the pixels having a characteristic value within a first range is determined. In step 305, A surface of one of the samples is determined at each predetermined step based on the pixel count of each captured image.  Figure 19 is a diagram of a captured image containing one of a single feature. An example of a feature is an opening in a circular shape in the photoresist layer. Another example of a feature is one of the openings in the photoresist layer in the shape of a trench (such as an unplated wiring (RDL) structure). During the wafer process, It is advantageous to measure various features of one of the photoresist openings in a wafer layer. The measurement of a photoresist opening provides detection of defects in the structure before the metal is plated into the holes. E.g, If a photoresist opening does not have the correct size, The plated RDL width will be wrong. Detecting this type of defect prevents further fabrication of a defective wafer. Preventing further manufacturing of a defective wafer saves material and processing costs. FIG. 19 illustrates that when the captured image is focused on the top surface of the photoresist layer, The measured intensity of the light reflected from the top surface of the photoresist layer is greater than the measured intensity of the light reflected from the opening in the photoresist layer. As discussed in more detail below, The information associated with each pixel in the captured image can be used to generate an intensity value for each of the pixels in the captured image. then, The intensity value of each pixel and an intensity threshold can be compared to determine whether each pixel is associated with a first region of the captured image (such as the top surface of the photoresist layer) or with a second region of the captured image (such as a photoresist open area) is associated. This can be done by the following steps: (i) first applying an intensity threshold to the measured intensity of each pixel in the captured image; (ii) classifying all pixels having an intensity value below one of the intensity thresholds as being associated with the first region of the captured image; (iii) classifying all pixels having an intensity value above one of the intensity thresholds as being associated with a second region of the captured image; And (iv) defining a feature as a group of pixels adjacent to other pixels associated with the same region within the same region.  The captured image shown in Figure 19 can be a color image. Each pixel of the color image contains red, Blue and green (RBG) channel values. Each of these color values can be combined to produce a single intensity value for each pixel. Various methods for converting the RBG values of each pixel into a single intensity value are described below.  A first method uses three weighting values to convert three color channels into one intensity value. In other words, Each color channel has its own weighting or conversion factor. We can modify one of the three conversion factors defined in one of the system recipes or modify the three conversion factors based on their sample measurement requirements. A second method subtracts the color channel of each pixel from a preset color channel value of each color channel. This result is then converted to an intensity value using the conversion factor discussed in the first method. A third method uses a "color difference" scheme to convert color to intensity values. In a color difference scheme, By using a pixel of color compared to a predefined fixed red, The proximity of the green and blue color values defines the resulting pixel intensity. An example of chromatic aberration is the weighted vector distance between a color value of a pixel and a fixed color value. Yet another method of "color difference" is to have one of the fixed color values automatically derived from the image. In one example, The boundary area of one of the images is known to have a background color. The weighted average of the colors of the pixels in the boundary region can be used as a fixed color value for the color difference scheme.  Once the color image has been converted to an intensity image, The intensity threshold and the intensity of each pixel can be compared to determine the image region to which the pixel belongs. In other words, A pixel having one of the intensity values above the intensity threshold indicates that the pixel receives light reflected from the first surface of one of the samples, And having one of the intensity values below one of the intensity thresholds indicates that the pixel has not received light reflected from the first surface of the sample. Once the pixels in the image are mapped to an area, The approximate shape of the feature focused on the image can be determined.  Figure 20, 21 and 22 illustrate three different methods of generating an intensity threshold. The intensity threshold can be used to measure the pixels of the light reflected from the top surface of the photoresist layer and the pixels that measure the light that is not reflected from the top surface of the photoresist layer.  Figure 20 illustrates a first method of generating one of the intensity thresholds for analyzing a captured image. In this first method, A pixel count is generated for each measured intensity value. This type of graph is also known as a histogram. Once the pixel count for each intensity value is generated, It is then possible to determine the range of intensities between the peak counts of the pixels originating from the measured light reflected from the photoresist layer and the peak counts of the pixels originating from the measured light not reflected from the photoresist layer. One of the intensity values within the intensity range is selected as the intensity threshold. In one example, The midpoint between the two peak counts is chosen as the threshold strength. In other examples falling within the disclosure of the present invention, Other intensity values between the two peak counts can be used.  Figure 21 is a second method of generating one of the intensity thresholds for analyzing a captured image. In step 311, A determination is made as to a first percentage of the captured image representing the photoresist region. Can be measured by physical means, This determination is made by optical inspection or based on production specifications. In step 312, A determination is made as to a second percentage of the captured image representing the open area of the photoresist. Can be measured by physical means, This determination is made by optical inspection or based on production specifications. In step 313, All pixels in the captured image are classified according to the intensity measured by each pixel. In step 314, Select all pixels with one intensity within the penultimate percentage of all pixel intensities. In step 315, Analyze all selected pixels.  Figure 22 illustrates a third method of determining a strength threshold. In step 321, A predetermined intensity threshold is stored in the memory. In step 322, The intensity of each pixel and the stored intensity threshold are compared. In step 323, Select all pixels that have an intensity value that is less than one of the intensity thresholds. In step 324, Analyze selected pixels.  Nothing about how to generate intensity thresholds, The threshold intensity value is used to roughly determine the location of the boundary of the feature in the captured image. then, The approximate boundary of the feature will be used to more accurately measure one of the boundaries of the feature, As discussed below.  Figure 23 is a three-dimensional view of one of the photoresist openings shown in Figure 19. Pay attention to various photoresist opening measurements during the process, Such as the area of the top opening and the bottom opening, The diameter of the top opening and the bottom opening, The top opening and the circumference of the bottom opening, The cross-sectional width of the top opening and the bottom opening and the depth of the opening. A first measurement system has a top surface opening area. Figure 8 (and accompanying text) describes how a plurality of images obtained at different distances from the sample are selected to focus on one of the images on the top surface of the photoresist opening and one of the images on the bottom surface of the photoresist opening. Once you select an image that is focused on the top surface, The top opening measurement can be determined using an image that is focused on the top surface of the photoresist opening. Similarly, Once the image is focused on the bottom surface of the photoresist opening, The bottom opening measurement can be determined using an image focused on the bottom surface of the photoresist opening. U.S. Patent Application Serial No. 12/699, entitled "3-D Optical Microscope" by James Jianguo Xu et al. No. 824 (the subject matter of which is incorporated herein by reference), A pattern or grid can be projected onto the surface of the sample while capturing multiple images. In one example, An image containing a projected pattern or grid is used to determine the photoresist opening measurement. In another example, A new image that is not included in the pattern or grid captured at the same z-distance is used to determine the photoresist opening measurement. In the latter instance, A new image that does not have a projected pattern or grid on the sample provides a "clearer" image. It provides easier detection of the boundaries of the photoresist opening.  Figure 24 is a two dimensional view of the top surface opening shown in Figure 23. The two-dimensional map clearly shows the boundary of the top surface opening (solid line) 40. The boundary is tracked using a best fit line (dashed line 41). Once the best fit line tracking is produced, The diameter of the best fit line 41 can be produced, Area and circumference.  Figure 25 is a two-dimensional view of the bottom surface opening shown in Figure 23. The two-dimensional map clearly shows the boundary of the bottom surface opening (solid line 42). The boundary is tracked using a best fit line (dashed line 43). Once the best fit line tracking is produced, Calculate the bottom surface opening diameter of the best fit line, Area and circumference.  In this example, A best fit line is automatically generated by a computer system that communicates with the optical microscope. A best fit line can be generated by analyzing the transition between the dark and bright portions of the selected image. As discussed in more detail below.  Figure 26 is a two dimensional image of one of the openings in a photoresist layer. Focus the image on the top surface of the photoresist layer. In this example, The light reflected from the top surface of the photoresist layer is bright, Because the microscope is focused on the top surface of the photoresist layer. The light intensity measured from the photoresist opening is dark, Because there is no reflective surface in the photoresist opening. The intensity of each pixel is used to determine if the pixel belongs to the top surface of the photoresist or the opening in the photoresist. The intensity change from the transition between the top surface of the photoresist and the opening in the photoresist can span multiple pixels and multiple intensity levels. The background intensity of the image may not be uniform. therefore, Further analysis is needed to determine the exact pixel location of the boundary of the photoresist. To determine the pixel location of a single surface transition point, Obtaining an average value of intensity in one of the adjacent bright areas outside the transition zone, And an average of the intensity is obtained in the adjacent dark areas outside the transition zone. The intermediate intensity value between the average of the adjacent bright areas and the average of the adjacent dark areas is used as the intensity threshold for distinguishing whether a pixel belongs to the top surface of the photoresist or the opening in the photoresist. This intensity threshold can be different from the previously discussed intensity threshold for selecting features within a single captured image. Once the intermediate strength threshold is determined, The intermediate intensity threshold is compared to all pixels to distinguish pixels belonging to the top surface of the photoresist or the opening in the photoresist. If the pixel intensity is higher than the intensity threshold, The pixel is then determined to be a photoresist pixel. If the pixel intensity is below the intensity threshold, The pixel is then determined to be an open area pixel. Multiple boundary points can be determined in this manner and used to fit a shape. then, The fitted shape is used to calculate all the desired dimensions of the top opening of the photoresist. In one example, The fitted shape can be selected from the following groups: Round, Square, rectangle, triangle, Oval, hexagon, Pentagon and so on.  Figure 27 illustrates the variation in measured intensity across adjacent regions around the luminance transition of Figure 26. In the leftmost part of the adjacent area, The measured intensity is high, Because the microscope is focused on the top surface of the photoresist layer. The measured light intensity is reduced by the brightness transition of the adjacent zone. The measured light intensity drops to a minimum range at the rightmost portion of the adjacent zone, Because there is no top surface of the photoresist layer in the rightmost portion of the adjacent region. Figure 27 plots this variation in measured intensity across adjacent regions. then, A boundary point indicating where the top surface of the photoresist layer ends can be determined by applying a threshold intensity. The boundary point at the end of the top surface of the photoresist is located at the intersection of the measured intensity and the threshold intensity. This procedure is repeated at different adjacent locations located along the brightness transition. A boundary point is determined for each adjacent zone. then, The boundary points of the adjacent regions are used to determine the size and shape of the top surface boundary.  Figure 28 is a two dimensional image of one of the openings in a photoresist layer. Focus the image on the bottom surface of the photoresist opening. In this example, The light reflected from the bottom surface of the open area of the photoresist is bright, Because the microscope is focused on the bottom surface of the photoresist opening. The light reflected from the photoresist area is also relatively bright. Because the substrate has a high reflectivity or a metal seed layer. Due to light scattering caused by the photoresist boundary, Light reflected from the boundary of the photoresist layer is dark. The measured intensity of each pixel is used to determine if the pixel belongs to the bottom surface of the photoresist opening. The intensity change from the transition between the bottom surface of the photoresist and the open area of the photoresist can span multiple pixels and multiple intensity levels. The background intensity of the image may not be uniform. therefore, Further analysis is needed to determine the exact pixel location of the photoresist opening. To determine the pixel position of a boundary point, A position having one of the smallest intensity pixels is determined within the adjacent pixel. Multiple boundary points can be determined in this manner and used to fit a shape. then, The fitted shape is used to calculate the desired size of the bottom opening.  Figure 29 illustrates the variation in measured intensity across adjacent regions around the luminance transition of Figure 28. At the far right part of the adjacent area, The measured intensity is high, Because the microscope is focused on the bottom surface of the photoresist opening. The measured light intensity is reduced to a minimum intensity and then reduced by the brightness transition of the adjacent zone. Due to light reflection from the surface of the substrate, The measured light intensity rises to a relatively high intensity range at the rightmost portion of the adjacent zone. Figure 29 plots this variation in measured intensity across adjacent regions. then, The boundary point indicating the location at which the boundary of the photoresist opening is located can be determined by finding the position of the minimum measured intensity. The boundary points are located at the location where the minimum measured intensity is at. The procedure is repeated at different adjacent zones located along the brightness transition. A boundary point is determined for each adjacent zone. then, The boundary points of the adjacent regions are used to determine the size and shape of the bottom surface boundary.  Figure 30 is a two dimensional image of a trench structure, such as an unplated wiring (RDL) structure, in a photoresist layer. Focus the image on the top surface of the photoresist layer. In this example, The light reflected from the top surface of the photoresist layer is bright, Because the microscope is focused on the top surface of the photoresist layer. The light reflected from the opening in the photoresist layer is dark, Because less light is reflected from the open trench region. The intensity of each pixel is used to determine whether the pixel belongs to the top surface of the photoresist or the open area in the photoresist. The intensity change from the transition between the top surface of the photoresist and the open region in the photoresist can span multiple pixels and multiple intensity levels. The background intensity of the image may not be uniform. therefore, Further analysis is needed to determine the exact pixel location of the boundary of the photoresist. To determine the pixel location of a single surface transition point, Obtaining an average value of intensity in one of the adjacent bright areas outside the transition zone, And an average of the intensity is obtained in the adjacent dark areas outside the transition zone. The intermediate intensity value between the average of the adjacent bright areas and the average of the adjacent dark areas is used as the intensity threshold for distinguishing the top surface resist reflection from the non-top surface resist reflection. Once the intermediate strength threshold is determined, The intermediate intensity threshold is compared to all adjacent pixels to determine a boundary between the top surface pixel and the photoresist open area. If the pixel intensity is higher than the intensity threshold, The pixel is then determined as a top surface photoresist pixel. If the pixel intensity is below the intensity threshold, The pixel is then determined to be a photoresist open area pixel. Multiple boundary points can be determined in this manner and used to fit a shape. then, Use the fitted shape to calculate all the desired dimensions of the photoresist opening of the trench, Such as the groove width.  Figure 31 illustrates the variation in measured intensity across adjacent regions around the luminance transition of Figure 30. In the leftmost part of the adjacent area, The measured intensity is high, Because the microscope is focused on the top surface of the photoresist layer. The measured light intensity is reduced by the brightness transition of the adjacent zone. The measured light intensity drops to a minimum range at the rightmost portion of the adjacent zone, Because there is no top surface of the photoresist layer in the rightmost portion of the adjacent region. Figure 31 plots this variation in measured intensity across adjacent regions. then, A boundary point indicating the end of the top surface of the photoresist layer can be determined by applying a threshold intensity. The boundary point at the end of the top surface of the photoresist is located at the intersection of the measured intensity and the threshold intensity. This procedure is repeated at different adjacent locations located along the brightness transition. A boundary point is determined for each adjacent zone. then, The boundary points of the adjacent regions are used to determine the size and shape of the top surface boundary.  Regarding Figures 26 to 31, Pixel intensity is only one example of pixel characteristics that can be used to distinguish pixels of different regions in an image. E.g, Pixels from different regions of an image can also be distinguished in a similar manner using the wavelength or color of each pixel. Once the boundaries between the regions are precisely defined, then, Using the boundary to determine the critical dimension (CD) of a PR opening, Such as its diameter or width. usually, The measured CD values are then compared to values measured on other types of tools, such as a critical dimension scanning electron microscope (CD-SEM). To ensure measurement accuracy in production monitoring tools, This type of cross calibration is necessary.  Figure 32 is a three dimensional view of one of the photoresist openings partially filled with metallization. The opening in the photoresist layer has a groove shape. Such as a plated heavy wiring (RDL) structure. During the wafer process, It is advantageous to measure the various features of the metallization deposited into the photoresist opening while the photoresist is still intact. E.g, If the thickness of the metal is not thick enough, Then we can always plate extra metal, As long as the photoresist has not been stripped. The ability to spot potential problems while the wafer is still in a working phase prevents further fabrication of a defective wafer and saves material and processing costs.  Figure 33 is a cross-sectional view of one of the photoresist openings partially filled with a metallization. Figure 33 clearly shows that the height of the top surface of the photoresist ("PR") region is greater than the height of the top surface of the metallization. The width of the top surface of the metallization is also shown in FIG. Using the various methods described above, The z position of the top surface of the photoresist region and the z position of the top surface of the metallization can be determined. The distance between the top surface of the photoresist region and the top surface of the metallization (also referred to as "step height") is equal to the difference between the height of the top surface of the photoresist region and the height of the top surface of the metallization. To determine the thickness of the metallization, Another measurement of the thickness of the photoresist area is required. As discussed above with respect to Figure 11, The photoresist region is translucent and has a refractive index that is different from the refractive index of the open air. therefore, The focal plane of the captured image that is focused on the light reflected from the bottom surface of the photoresist region is not actually positioned at the bottom surface of the photoresist region. however, at this time, Our goals are different. I don't want to filter out the wrong surface measurement, Instead, the thickness of the photoresist area is now required. Figure 40 illustrates how a portion of the incident light that is not reflected from the top surface of the photoresist region travels through the photoresist region at an angle different from the incident light due to the refractive index of the photoresist material. If this error is not resolved, The measured thickness of the photoresist region D' (focusing on the measured z-position of the captured image on the light reflected from the top surface of the photoresist region minus the focus on the bottom surface of the self-resisting region The position of the image on the light is measured by the z position), Figure 40 clearly shows that the measured thickness D' is not close to the actual thickness D of the photoresist region. however, The error introduced by the refractive index of the photoresist region can be removed by applying a correction calculation to the measured thickness of the photoresist region. A first correction calculation is shown in FIG. The actual thickness (D) of the photoresist region is equal to the measured thickness (D') of the photoresist region multiplied by the refractive index of the photoresist region. A second correction calculation is shown in FIG. The actual thickness (D) of the photoresist region is equal to the measured thickness (D') of the photoresist region multiplied by the refractive index of the photoresist region plus an offset value. The second correction calculation is more general and considers the following facts: The refractive index of the photoresist varies depending on the wavelength and when imaged through a transparent medium, The spherical aberration of an objective lens can affect the z position measurement. therefore, As long as the appropriate calibration procedure is followed, The actual thickness of the photoresist region can be calculated using one of the focal planes of the focal plane of the captured image focused on the light reflected from the bottom surface of the photoresist region.  Once the correction equation is applied to the measured thickness of the photoresist region, The true thickness of the photoresist area is obtained. Referring again to Figure 33, The thickness of the metallization can now be calculated. The thickness of the metallization is equal to the difference between the thickness of the photoresist region minus the z-position of the top surface of the photoresist region and the z-position of the top surface of the metallization.  Figure 34 is a three dimensional view of one of the circular photoresist openings with metallization. Figure 35 is a cross-sectional view of one of the circular photoresist openings having the metallization shown in Figure 34. Figure 35 is a cross-sectional view similar to the cross-sectional view of Figure 33. Figure 35 clearly shows that the height of the top surface of the photoresist ("PR") region is greater than the height of the top surface of the metallization. Using the various methods described above, The z position of the top surface of the photoresist region and the z position of the top surface of the metallization can be determined. The distance between the top surface of the photoresist region and the top surface of the metallization (also referred to as "step height") is equal to the difference between the height of the top surface of the photoresist region and the height of the top surface of the metallization. To determine the thickness of the metallization, Another measurement of the thickness of the photoresist area is required. As discussed above with respect to Figure 11, The photoresist region is translucent and has a refractive index that is different from the refractive index of the open air. therefore, The focal plane of the captured image that is focused on the light reflected from the bottom surface of the photoresist region is not actually positioned at the bottom surface of the photoresist region. however, at this time, Our goals are different. The thickness of the photoresist area is now required. Figure 40 illustrates how a portion of the incident light that is not reflected from the top surface of the photoresist region travels through the photoresist region at an angle different from the incident light due to the refractive index of the photoresist material. If this error is not resolved, The measured thickness of the photoresist region D' (focusing on the measured z-position of the captured image on the light reflected from the top surface of the photoresist region minus the focus on the bottom surface of the self-resisting region The position of the image on the light is measured by the z position), Figure 40 clearly shows that the measured thickness D' is not close to the actual thickness D of the photoresist region. however, The error introduced by the refractive index of the photoresist region can be removed by applying a correction calculation to the measured thickness of the photoresist region. A first correction calculation is shown in FIG. The actual thickness (D) of the photoresist region is equal to the measured thickness (D') of the photoresist region multiplied by the refractive index of the photoresist region. A second correction calculation is shown in FIG. The actual thickness (D) of the photoresist region is equal to the measured thickness (D') of the photoresist region multiplied by the refractive index of the photoresist region plus an offset value. The second correction calculation is more general and considers the following facts: The refractive index of the photoresist varies depending on the wavelength and the spherical aberration of an objective lens can affect the z-position measurement when imaged through a transparent medium. therefore, As long as the appropriate calibration procedure is followed, The actual thickness of the photoresist region can be calculated using one of the focal planes of the focal plane of the captured image focused on the light reflected from the bottom surface of the photoresist region.  Once the correction equation is applied to the measured thickness of the photoresist region, The true thickness of the photoresist area is obtained. Referring again to Figure 35, The thickness of the metallization can now be calculated. The thickness of the metallization is equal to the difference between the thickness of the photoresist region minus the z-position of the top surface of the photoresist region and the z-position of the top surface of the metallization.  Figure 36 is a three dimensional view of one of the metal pillars above the passivation layer. Figure 37 is a cross-sectional view of one of the metal posts above the passivation layer shown in Figure 36. Figure 37 clearly shows that the height of the top surface of the passivation layer is less than the height of the top surface of the metal layer. The diameter of the top surface of the metallization is also shown in FIG. Using the various methods described above, The z position of the top surface of the passivation layer and the z position of the top surface of the metal layer can be determined. The distance between the top surface of the passivation layer and the top surface of the metal layer (also referred to as "step height") is equal to the difference between the height of the top surface of the metal layer and the height of the top surface of the passivation layer. To determine the thickness of the metal layer, Another measurement of the thickness of the passivation layer is required. As discussed above with respect to Figure 11, A translucent material, such as a photoresist region or a passivation layer, has a refractive index that is different from the refractive index of the open air. therefore, The focal plane of the captured image that is focused on the light reflected from the bottom surface of the passivation layer is not actually positioned at the bottom surface of the passivation layer. however, at this time, Our goals are different. The thickness of the passivation layer is now required. Figure 47 illustrates how a portion of the incident light that is not reflected from the top surface of the passivation layer travels through the passivation layer at an angle different from the incident light due to the refractive index of the passivation material. If this error is not resolved, The measured thickness of the passivation layer D' (focusing on the measured z position of the captured image on the light reflected from the top surface of the passivation region minus the light reflected on the bottom surface of the self-passivation region) After measuring the z position of the image, Figure 47 clearly shows that the measured thickness D' is not close to the actual thickness D of the passivation layer. however, The error introduced by the refractive index of the passivation layer can be removed by applying a correction calculation to the measured thickness of the passivation layer. A first correction calculation is shown in FIG. 47, Wherein the actual thickness (D) of the passivation layer is equal to the measured thickness (D') of the passivation layer multiplied by the refractive index of the passivation layer. A second correction calculation is shown in FIG. Wherein the actual thickness (D) of the passivation layer is equal to the measured thickness (D') of the passivation layer multiplied by the refractive index of the passivation layer plus an offset value. The second correction calculation is more general and considers the following facts: The refractive index of the passivation layer varies depending on the wavelength and when imaged through a transparent medium, The spherical aberration of an objective lens can affect the z position measurement. therefore, As long as the appropriate calibration procedure is followed, The actual thickness of the passivation layer is calculated using one of the focal planes of the focal plane of the image captured on the light reflected from the bottom surface of the passivation layer.  Once the correction equation is applied to the measured thickness of the passivation layer, The true thickness of the passivation layer is obtained. Referring again to Figure 37, The thickness of the metal layer can now be calculated. The thickness of the metal layer is equal to the sum of the thickness of the passivation layer and the difference between the z position of the top surface of the passivation layer and the z position of the top surface of the metal layer.  Figure 38 is a three dimensional view of a metal above the passivation layer. In this particular case, The metal structure shown is a heavy wiring (RDL). Figure 39 is a cross-sectional view of one of the metals above the passivation layer shown in Figure 38. Figure 39 clearly shows that the height of the top surface of the passivation layer is less than the height of the top surface of the metal layer. Using the various methods described above, The z position of the top surface of the passivation layer and the z position of the top surface of the metal layer can be determined. The distance between the top surface of the passivation layer and the top surface of the metal layer (also referred to as "step height") is equal to the difference between the height of the top surface of the metal layer and the height of the top surface of the passivation layer. To determine the thickness of the metal layer, Another measurement of the thickness of the passivation layer is required. As discussed above with respect to Figure 11, A translucent material, such as a photoresist region or a passivation layer, has a refractive index that is different from the refractive index of the open air. therefore, The focal plane of the captured image that is focused on the light reflected from the bottom surface of the passivation layer is not actually positioned at the bottom surface of the passivation layer. however, at this time, Our goals are different. The thickness of the passivation layer is now required. Figure 40 illustrates how a portion of the incident light that is not reflected from the top surface of the passivation layer travels through the passivation layer at an angle different from the incident light due to the refractive index of the passivation material. If this error is not resolved, The measured thickness of the passivation layer D' (focusing on the measured z position of the captured image on the light reflected from the top surface of the passivation region minus the light reflected on the bottom surface of the self-passivation region) After measuring the z position of the image, Figure 40 clearly shows that the measured thickness D' is not close to the actual thickness D of the passivation layer. however, The error introduced by the refractive index of the passivation layer can be removed by applying a correction calculation to the measured thickness of the passivation layer. A first correction calculation is shown in FIG. Wherein the actual thickness (D) of the passivation layer is equal to the measured thickness (D') of the passivation layer multiplied by the refractive index of the passivation layer. A second correction calculation is shown in FIG. Wherein the actual thickness (D) of the passivation layer is equal to the measured thickness (D') of the passivation layer multiplied by the refractive index of the passivation layer plus an offset value. The second correction calculation is more general and considers the following facts: The refractive index of the passivation layer varies depending on the wavelength and when imaged through a transparent medium, The spherical aberration of an objective lens can affect the z position measurement. therefore, As long as the appropriate calibration procedure is followed, The actual thickness of the passivation layer can be calculated using one of the focal planes of the focal plane of the captured image focused on the light reflected from the bottom surface of the passivation layer.  Once the correction equation is applied to the measured thickness of the passivation layer, The true thickness of the passivation layer can be obtained. Referring again to Figure 39, The thickness of the metal layer can now be calculated. The thickness of the metal layer is equal to the sum of the thickness of the passivation layer and the difference between the z position of the top surface of the passivation layer and the z position of the top surface of the metal layer.  Figure 41 is a graph showing the peak mode operation of images taken at various distances when a photoresist opening is in the field of view of an optical microscope. The captured image depicted in Figure 41 is obtained from a sample similar to the sample structure shown in Figure 32. This structure is a metallized trench structure. The top view of the sample shows the area of the photoresist opening (a metallization) in the x-y plane. The PR opening also has a depth (higher than metallization) at a particular depth in the z-direction. The top view in Figure 41 below shows the images captured at various distances. At distance 1, The optical microscope is not focused on the top surface of the photoresist area or the top surface of the metallization. At distance 2, The optical microscope is focused on the top surface of the metallization, However, it is not focused on the top surface of the photoresist region. This results in a pixel that is received from light that is reflected from other surfaces of the defocus (the top surface of the photoresist region) One of the pixels received from the light reflected from the top surface of the metallization increases the characteristic value (intensity/contrast/streak contrast). At a distance of 3, The optical microscope is not focused on the top surface of the photoresist area or the top surface of the metallization. therefore, At a distance of 3, The maximum characteristic value will be substantially lower than the maximum characteristic value measured at distance 2. At distance 4, The optical microscope is not focused on any surface of the sample; however, Due to the difference between the refractive index of air and the refractive index of the photoresist region, One of the measured maximum characteristic values (intensity/contrast/streak contrast) is increased. Figure 11, Figure 11, Figure 40 and accompanying text describe this phenomenon in more detail. At a distance of 6, The optical microscope is focused on the top surface of the photoresist area, However, it is not focused on the top surface of the metallization. This results in a pixel that is received from light that is reflected from other surfaces of the defocus (the top surface of the metallization) One of the pixels received from the light reflected from the top surface of the photoresist region increases the characteristic value (intensity/contrast/streak contrast). Once the maximum characteristic value from each captured image is determined, The results can be used to determine at which distances each surface of the wafer is positioned.  Figure 42 is a graph showing one of three-dimensional information derived from the peak mode operation illustrated in Figure 41. As discussed with respect to Figure 41, At distance 1, The maximum characteristic values of the images captured at 3 and 5 have a lower than at distance 2 One of the maximum characteristic values of the image captured at 4 and 6 and the maximum characteristic value. The curve of the maximum characteristic value at various z distances may contain noise due to environmental effects such as vibration. To minimize this noise, A standard smoothing method can be applied before further data analysis, Such as Gaussian filtering with a specific core size.  A method of comparing the maximum characteristic values by a peak finding algorithm. In one example, The zero crossing point is located along the z-axis using a derivative method to determine the distance between each "peak". then, The maximum characteristic value at each distance at which a peak is found is compared to determine the distance at which the maximum characteristic value is measured. In the case shown in Figure 42, A peak will be found at distance 2, This is used as an indication that one of the surfaces of the sample is positioned at a distance of two.  Another method of comparing the maximum characteristic values is performed by comparing each of the maximum characteristic values with a predetermined threshold. Based on wafer material, Distance and optical microscope specifications are used to calculate the threshold. Alternatively, The threshold can be determined by empirical testing prior to automated processing. In either case, Compare the maximum characteristic value and threshold value of each captured image. If the maximum characteristic value is greater than the threshold, It is then determined that the maximum characteristic value indicates the presence of one of the surfaces of the wafer. If the maximum characteristic value is not greater than the threshold, It is then determined that the maximum characteristic value does not indicate one of the surfaces of the wafer.  One of the peak mode methods described above is an alternative use, The range mode method and associated text depicted in Figure 13 can be used to determine the z position of different surfaces of the same.  Figure 43 is a view of one of the captured images focused on one of the top surfaces of one of the photoresist layers in a trench structure, A contour of a first analysis area A and a second analysis area B is included. As discussed above, The entire field of view of each captured image can be used to generate three-dimensional information. however, It is advantageous to have an option to generate three-dimensional information using only one selectable portion of the field of view (area A or region B). In one example, A user selects an area using a mouse or touch screen device that communicates with one of the processed images. Once selected, We can apply different thresholds to each region to more efficiently pick out a particular surface peak as shown in Figure 42. This case is illustrated in Figure 43. When it is desired to obtain three-dimensional information about the top surface of the metallization, Set a selectable portion of the field of view (Area A) to include multiple areas of metallization, This is because the characteristic value associated with a metal surface is typically greater than the characteristic value associated with the photoresist. Therefore, a high threshold can be applied to region A to filter out the characteristic values associated with the photoresist to improve the detection of metal surface peaks. Alternatively, When it is desired to obtain three-dimensional information about the top surface of a photoresist region, The selectable portion of the field of view (region B) is set to be located in one of the cells of a video center. Compared to the characteristic values associated with metal surfaces, The characteristic values associated with a photoresist surface are typically relatively weak. The quality of the original signal used to determine the property value calculation is optimal around the center of the field of view enclosed within region B. By setting an appropriate threshold for one of the areas B, We can more effectively detect the peak value of a weak characteristic value of the photoresist surface. The user can set and adjust the area A and the area B and the thresholds used in each area via the graphical interface of the top view image of the sample and store them in one of the recipes for automated measurement.  Figure 44 is a three dimensional view of one of the bumps above the passivation structure. Figure 45 is a top plan view of a bump above the passivation structure shown in Figure 44, A contour of a first analysis area A and a second analysis area B is included. Area A can be set, This allows area A to always contain the vertices of the metal bumps during an automated sequence measurement. Region B does not enclose any portion of the metal bumps and only encloses one portion of the passivation layer. Only analyze all areas of the captured image to provide pixel filtering. This allows most of the pixels analyzed to contain information about the metal bumps. Analyze all areas of the captured image to provide pixel filtering. All pixels analyzed are made to contain information about the passivation layer. The user can select an application in the analysis area to provide pixel filtering based on location rather than pixel values. E.g, When the position of the top surface of the passivation layer is required, Area B can be applied and all effects caused by the metal bumps can be eliminated immediately by analysis. In another example, When the position of the apex of the metal bump is required, Area A can be applied and all effects caused by the large passivation layer area can be eliminated immediately by analysis.  In some instances, The spatial relationship between the fixed area A and the area B is also useful. When the metal bump of one of the known sizes is equivalently measured (such as shown in FIGS. 44 and 45), The spatial relationship between fixed area A and area B is useful to provide a consistent measurement system, Because area A is always used to measure the three-dimensional information of the metal bumps and area B is always used to measure the three-dimensional information of the passivation layer. Furthermore, When area A and area B have a fixed spatial relationship, Adjustment of one area automatically causes one of the other areas to adjust. This scenario is illustrated in FIG. FIG. 46 is a plan view showing one of the analysis area A and the analysis area B when the entire bump is not positioned in the original analysis area A. This can happen for a variety of reasons, Such as a processor's inaccurate placement of one of the samples or program changes during sample manufacture. Whatever the reason, Area A needs to be adjusted to properly center the apex of the metal bump. Area B also needs to be adjusted to ensure that Area B does not contain any part of the metal bumps. When the spatial relationship between the area A and the area B is fixed, Then the adjustment of the area A automatically causes the realignment of the area B.  Figure 47 is a cross-sectional view of one of the bumps above the passivation structure illustrated in Figure 44. When the thickness of the passivation layer is substantially greater than the distance between the predetermined steps of the optical microscope during image acquisition, The z position of the top surface of the passivation layer can be easily detected as discussed above. however, When the thickness of the passivation layer is substantially no greater than the distance between the predetermined steps of the optical microscope (ie, When the passivation layer is relatively thin) It may not be easy to detect and measure the z position of the top surface of the passivation layer. The difficulty is due to a small percentage of the light reflected from the top surface of the passivation layer compared to a large percentage of the light reflected from the bottom surface of the passivation layer. In other words, Compared to the peak value of the characteristic value associated with the bottom surface of the passivation layer, The peak value of the characteristic value associated with the top surface of the passivation layer is very weak. The captured image is captured at a predetermined step on a high intensity reflection from the bottom surface of the passivation layer and at a predetermined step on a low intensity reflection from the top surface of the passivation layer When the distance is less than a few predetermined steps, It is not possible to distinguish between the reflection received from the bottom surface of the passivation layer and the reflection received from the top surface of the passivation layer. This problem can be solved by different methods of operation.  In a first method, Increase the total number of steps in the cross scan. In order to provide additional resolution across the entire scan. E.g, Doubling the number of predetermined steps across the same scan distance, This will result in a doubling of the Z resolution of the scan. This method will also result in doubling the amount of image captured during a single scan. The resolution of the scan can be increased until the characteristic peak from the top surface reflection measurement and the characteristic peak from the bottom surface reflection measurement can be distinguished. Figure 49 depicts one of the scenarios in which sufficient resolution is provided in the scan to distinguish reflections from the top and bottom surfaces of the passivation layer.  In a second method, Also increase the total number of predetermined steps, however, Only one part of the step is used to capture the image and the rest is skipped.  In a third method, The distance between the predetermined steps can be changed, The distance between the steps is made smaller near the passivation layer and the distance between the steps is larger outside the vicinity of the passivation layer. This method provides greater resolution near the passivation layer and a smaller resolution outside of the passivation layer. This method does not require adding additional predetermined steps to the scan. Rather, the predetermined steps are redistributed in a non-linear fashion to provide additional resolution where needed without sacrificing lower resolution without high resolution.  For an additional description of how to improve the scanning resolution, See U.S. Patent Application Serial No. 13/333, entitled "3D Microscope Including Insertable Components To Provide Multiple Imaging and Measurement Capabilities", filed on December 21, 2011 by James Jiang. No. 938 (the subject matter of which is incorporated herein by reference).  Using any of the methods discussed above, The z position of the top surface of the passivation layer can be determined.  The height of the apex of the metal bump relative to the top surface of the passivation layer ("bump height above the passivation layer") is also a measure of interest. The height of the bump above the passivation layer is equal to the z position of the apex of the bump minus the z position of the top surface of the passivation layer. The determination of the z position of the top surface of the passivation layer is described above. The determination of the z-position of the vertices of the bumps can be performed using different methods.  In a first method, The z-position of the apex of the bump is determined by determining the z-position of the peak characteristic value of each x-y pixel position of all captured images. In other words, For each x-y pixel location, The measured characteristic values are compared across all captured images at each z position and the z position containing the largest characteristic values is stored in an array. The result of performing this procedure across all x-y pixel locations is an array of all x-y pixel locations and an associated peak z position for each x-y pixel location. The maximum z position in the array is measured as the z position of the apex of the bump. For an additional description of how to generate 3D information, See U.S. Patent Application Serial No. 12/699, entitled "3-D Optical Microscope", filed on February 3, 2010, by 824 and US Patent No. 8, 174, No. 762 (the subject matter of which is incorporated herein by reference).  In a second method, The z-position of the apex of the bump is determined by fitting a three-dimensional model to one of the surfaces of the bump and then calculating the peak of the surface of the bump using the three-dimensional model. In one example, This can be done by generating the same array described above with respect to the first method, however, Once the array is completed, The array is used to generate a three-dimensional model. A three-dimensional model can be generated using a second-order polynomial function fitted to one of the data. Once the 3D model is generated, The derivative of the surface slope of the bump is determined. The apex of the bump is calculated to be located where the derivative of the surface slope of the bump is equal to zero.  Once the z position of the apex of the bump is determined, The height of the bump above the passivation layer can be calculated by subtracting the z position of the top surface of the passivation layer from the z position of the apex of the bump.  Figure 48 is a graph showing the peak mode operation of images taken at various distances when only one passivation layer is in the region B of the field of view of the optical microscope. By analyzing only the pixels in region B (shown in Figure 45), Exclude all pixel information about metal bumps. therefore, The three-dimensional information generated by analyzing the pixels in region B will only be affected by the passivation layer present in region B. The captured image depicted in Figure 48 is obtained from a sample similar to the sample structure shown in Figure 44. This structure is one of the metal bumps above the passivation structure. The top view of the sample shows the area of the passivation layer in the x-y plane. In the case where only pixels in the area B are selected, Metal bumps are not visible in the top view. The top view in Figure 48 below shows the images captured at various distances. At distance 1, The optical microscope is not focused on the top surface of the passivation layer or the top surface of the passivation layer. At distance 2, The optical microscope is not focused on any surface of the sample; however, Due to the difference between the refractive index of air and the refractive index of the passivation layer, One of the largest specific values (intensity/contrast/streak contrast) is measured to increase. Figure 11, Figure 11, Figure 40 and accompanying text describe this phenomenon in more detail. At a distance of 3, The optical microscope is not focused on the top surface of the passivation layer or the bottom surface of the passivation layer. therefore, At a distance of 3, The maximum characteristic value will be substantially lower than the characteristic value measured at distance 2. At distance 4, An optical microscope is focused on the top surface of the passivation layer, This results in a pixel that is received from light reflected from other surfaces of the defocusing One of the pixels received from the light reflected from the top surface of the passivation layer increases the characteristic value (intensity/contrast/streak contrast). At a distance of 5, 6 and 7 places, The optical microscope is not focused on the top surface of the passivation layer or the bottom surface of the passivation layer. therefore, At a distance of 5, 6 and 7 places, The maximum characteristic value will be substantially lower than the characteristic value measured at distances 2 and 4. Once the maximum characteristic value from each captured image is determined, The results can be used to determine at which distances each surface of the sample is located.  Figure 49 is a graph showing one of the three-dimensional information from the peak mode operation of Figure 48. Due to the pixel filtering provided by analyzing only the pixels in all regions B of the captured image, The peak mode operation only provides one of the surfaces of the passivation layer at one of the two z positions (2 and 4). The top surface of the passivation layer is positioned at the higher of the two indicated z position positions. The lowest of the two indicated z position positions is an error "artifact surface". Light reflected from the bottom surface of the passivation layer is measured due to the refractive index of the passivation layer. Measuring the z-position of the top surface of the passivation layer using only pixels positioned within region B simplifies peak mode operation and reduces the likelihood of false measurements due to light reflections from metal bumps positioned on the same sample.  One of the peak mode methods described above is an alternative use, The range mode method and associated text depicted in Figure 13 can be used to determine the z position of different surfaces of the same.  Although certain specific embodiments have been described above for instructional purposes, However, the teachings of this patent document have general applicability and are not limited to the specific embodiments described above. therefore, Various modifications of various features of the described embodiments can be practiced without departing from the scope of the invention as set forth in the appended claims. Adaptation and combination.

1‧‧‧Semi-automated 3D metering system

2‧‧‧stage

3‧‧‧ wafer

4‧‧‧ computer

5‧‧‧ On/Off button

10‧‧‧3D Imaging Microscope

11‧‧‧Adjustable objective

12‧‧‧Adjustable stage

20‧‧‧3D metering system

21‧‧‧3D microscope

22‧‧‧sample handler/stage

23‧‧‧ computer

24‧‧‧ Processor

25‧‧‧Storage device

26‧‧‧Network devices

27‧‧‧ display

28‧‧‧ Input device

29‧‧‧Network

30‧‧‧矽 substrate

31‧‧‧ photoresist layer

40‧‧‧Boundary of the top surface opening

41‧‧‧Best fit line

42‧‧‧Boundary of the bottom surface opening

43‧‧‧Best fit line

200‧‧‧flow chart

201‧‧‧Steps

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300‧‧‧ Flowchart

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Embodiments of the present invention are illustrated with the accompanying drawings in which like reference numerals Figure 1 is a diagram of one of the semi-automated three-dimensional metering systems 1 performing the same automated three-dimensional measurement. 2 is a diagram of a three-dimensional imaging microscope 10 including an adjustable objective 11 and an adjustable stage 12. 3 is a diagram of a three-dimensional metering system 20 including a three-dimensional microscope, a processor, a computer, a display, and an input device. FIG. 4 is a view showing one of the methods of capturing an image when changing the distance between the objective lens of the optical microscope and the stage. Figure 5 is a graph showing the distance between the objective lens of the optical microscope and the surface of the sample, wherein each x-y coordinate has a maximum characteristic value. Figure 6 is a three-dimensional representation of one of the images using the maximum characteristic values of the x-y coordinates shown in Figure 5. Figure 7 is a graph showing peak mode operation using images captured at various distances. Figure 8 is a diagram showing the peak mode operation of using images taken at various distances when a photoresist opening is in the field of view of an optical microscope. Figure 9 is a diagram showing one of three-dimensional information derived from peak mode operation. Figure 10 is a diagram showing the sum mode operation using images captured at various distances. Figure 11 is a diagram showing an error surface detection when operating in a summation mode. Figure 12 is a diagram showing one of three-dimensional information from the summation mode operation. Figure 13 is a diagram showing a range mode operation using images captured at various distances. Figure 14 is a diagram showing one of three-dimensional information derived from range mode operation. Figure 15 is a graph showing only one of the pixel counts having one of the first range of characteristic values. Figure 16 is a graph showing only one of the pixel counts having one of the characteristic values in the second range. Figure 17 is a flow chart showing one of the various steps involved in peak mode operation. Figure 18 is a flow chart showing one of the various steps involved in the range mode operation. Figure 19 is a diagram of one of the captured images (including a single feature) focused on a top surface of a photoresist layer. Figure 20 is a diagram showing one of the first methods of generating an intensity threshold. Figure 21 is a diagram showing one of the second methods of generating an intensity threshold. Figure 22 is a diagram showing one of the third methods of generating an intensity threshold. Figure 23 is a three-dimensional view of one of the photoresist openings in the same. Figure 24 is a two dimensional view of the top surface opening of the photoresist shown in Figure 23. Figure 25 is a two-dimensional view of the bottom surface opening of the photoresist shown in Figure 23. Figure 26 is an image captured by focusing on one of the top surfaces of a photoresist layer. FIG. 27 is a view showing one of the boundaries of one of the photoresist layers shown in FIG. Figure 28 is an image captured by focusing on one of the bottom surfaces of a photoresist layer. FIG. 29 is a view showing one of the boundaries of one of the photoresist layers shown in FIG. Figure 30 is a view of one of the top surfaces of one of the photoresist layers in a trench structure. FIG. 31 is a diagram showing one of the boundaries of the photoresist layer detected in FIG. Figure 32 is a three dimensional view of one of the photoresist openings partially filled with metallization. Figure 33 is a cross-sectional view of one of the photoresist openings partially filled with a metallization. Figure 34 is a three dimensional view of one of the photoresist openings with metallization. Figure 35 is a cross-sectional view of one of the photoresist openings with metallization. Figure 36 is a three dimensional view of one of the metal pillars above the passivation layer. Figure 37 is a cross-sectional view of one of the metal pillars above the passivation layer. Figure 38 is a three dimensional view of a metal above the passivation layer. Figure 39 is a cross-sectional view of one of the metals above the passivation layer. Figure 40 is a cross-sectional view showing the measurement of a translucent material that is close to a metallized surface. Figure 41 is a graph showing the peak mode operation of images taken at various distances when a photoresist opening is in the field of view of an optical microscope. Figure 42 is a graph showing one of three-dimensional information derived from the peak mode operation illustrated in Figure 41. Figure 43 is a view of a captured image of one of the top surfaces of one of the photoresist layers in a trench structure, including a contour of a first analysis area A and a second analysis area B. Figure 44 is a three dimensional view of one of the bumps above the passivation structure. 45 is a top plan view of a bump above the passivation structure, including a contour of a first analysis area A and a second analysis area B. FIG. 46 is a plan view showing one of the analysis area A and the analysis area B when the entire bump is not positioned in the original analysis area A. Figure 47 is a cross-sectional view of one of the bumps above the passivation structure. Figure 48 is a diagram showing the peak mode operation of images taken at various distances when only one photoresist layer is in the region B of the field of view of the optical microscope. Figure 49 is a graph showing one of the three-dimensional information from the peak mode operation of Figure 48.

Claims (22)

A method of producing an identical three-dimensional (3-D) information using an optical microscope, the method comprising: changing a distance between the sample and an objective lens of the optical microscope in a predetermined step; capturing at each predetermined step An image, wherein a first surface of the sample and a second surface of the sample are in a field of view of each of the captured images; determining a characteristic value of each of the pixels in each captured image; Determining a maximum characteristic value of a first portion of one of the pixels in the captured image for each captured image; comparing the maximum characteristic value of each captured image to determine whether a surface of the sample exists at each predetermined step Determining a first captured image focused on one of the vertices of one of the bumps of the sample; determining, based on the characteristic value of each pixel in each of the captured images, focusing on one of the first surfaces of the sample The second captured image; and determines a first distance between the vertex of the bump and the first surface. The method of claim 1, wherein the optical microscope comprises a stage, wherein the sample is supported by the stage, wherein the optical microscope is adapted to communicate with a computer system, wherein the computer system includes adapted to store each A memory device is captured, wherein the optical microscope is selected from the group consisting of a confocal microscope, a structured illumination microscope, and an interferometer. The method of claim 1, wherein the determining of the first captured image further comprises: determining a maximum characteristic value of one of xy pixel positions in a second portion of one of xy pixel positions across all captured images, Wherein the second portion of the xy pixel location includes at least some of the xy pixel locations included in each captured image; determining a subset of the captured images, wherein only one xy pixel location maximum characteristic value is included The captured image is included in the subset; and determining that all of the captured images in the captured image subset are compared to all of the captured image subsets Other captured images are focused at a maximum z position. The method of claim 1, wherein the first portion of the pixel comprises all of the pixels included in the captured image, and wherein the characteristic value of each pixel is selected from the group consisting of intensity, contrast, and fringe contrast. The method of claim 1, wherein the first portion of pixels comprises less than all of the pixels included in the captured image. The method of claim 3, wherein the second portion of the pixels comprises all of the pixels included in the captured image. The method of claim 3, wherein the second portion of pixels comprises less than all of the pixels included in the captured image. The method of claim 1, wherein the first portion of the pixel does not receive light reflected from the metal bump. The method of claim 3, wherein the second portion of the pixel receives light reflected from the vertex of the metal bump. The method of claim 3, wherein the spatial relationship between the first portion of the pixel and the second portion of the pixel is fixed. The method of claim 3, wherein the second portion of the pixel is continuous and centered at the vertex of the bump. The method of claim 1, wherein the bump is a metal bump and wherein the first surface is a top surface of a passivation layer. A method of producing an identical three-dimensional (3-D) information using an optical microscope, the method comprising: changing a distance between the sample and an objective lens of the optical microscope in a predetermined step; capturing at each predetermined step An image, wherein a first surface of the sample and a second surface of the sample are in a field of view of each of the captured images; determining a characteristic value of each of the pixels in each captured image; Determining, for each captured image, a count of pixels having a characteristic value within a first range of one of the first portions of one of the pixels, wherein all pixels not having one of the first range of characteristic values are not included in the pixel Counting; determining whether a surface of the sample exists at each predetermined step based on the pixel count of each captured image. Determining a first captured image focused on one of the vertices of one of the bumps of the sample; determining, based on the characteristic value of each pixel in each of the captured images, focusing on one of the first surfaces of the sample And capturing an image; and determining a first distance between the vertex of the bump and the first surface. The method of claim 13, wherein the optical microscope comprises a stage, wherein the sample is supported by the stage, wherein the optical microscope is adapted to communicate with a computer system, wherein the computer system includes adapted to store each A memory device is captured, wherein the optical microscope is selected from the group consisting of a confocal microscope, a structured illumination microscope, and an interferometer. The method of claim 13, wherein the determining of the first captured image further comprises: determining a maximum characteristic value of one of xy pixel positions in a second portion of one of xy pixel positions across all captured images, Wherein the second portion of the xy pixel location includes at least some of the xy pixel locations included in each captured image; determining a subset of the captured images, wherein only one xy pixel location maximum characteristic value is included The captured image is included in the subset; and determining that all of the captured images in the captured image subset are compared to all of the captured image subsets Other captured images are focused on the highest z position. The method of claim 13, wherein the first portion of the pixel comprises all of the pixels included in the captured image, and wherein the characteristic value of each pixel is selected from the group consisting of intensity, contrast, and fringe contrast. The method of claim 13, wherein the first portion of pixels comprises less than all of the pixels included in the captured image. The method of claim 15, wherein the second portion of pixels comprises all of the pixels included in the captured image. The method of claim 15, wherein the spatial relationship between the first portion of the pixel and the second portion of the pixel is fixed. The method of claim 15, wherein the second portion of the pixel is continuous and centered at the vertex of the bump. A method of producing an identical three-dimensional (3-D) information using an optical microscope, the method comprising: changing a distance between the sample and an objective lens of the optical microscope in a predetermined step; capturing at each predetermined step An image, wherein a first surface of the sample and a second surface of the sample are in a field of view of each of the captured images; determining a characteristic value of each of the pixels in each captured image; Determining a position z of one of the vertices of one of the bumps of the sample; determining, based on the characteristic value of each pixel in each of the captured images, a first captured image focused on one of the first surfaces of the sample; Determining a first distance between the vertex of the bump and the first surface. The method of claim 21, wherein the determining of the z-position of the vertex comprises: identifying a plurality of x, y, z pixel locations across all of the captured images, wherein the plurality of x, y, z pixel locations and the A top surface of one of the bumps is associated; applying a best-fit algorithm to generate a continuous three-dimensional estimate of the top surface of the bump; and determining a maximum height of the continuous three-dimensional estimate.