CN109791039B

CN109791039B - Method for generating three-dimensional information of sample using optical microscope

Info

Publication number: CN109791039B
Application number: CN201780057121.1A
Authority: CN
Inventors: R·苏塔耳曼; J·J·徐
Original assignee: KLA Tencor Corp
Current assignee: KLA Corp
Priority date: 2016-08-10
Filing date: 2017-08-08
Publication date: 2021-03-23
Anticipated expiration: 2037-08-08
Also published as: KR102226779B1; CN109791039A; TWI769172B; KR20190029766A; WO2018031574A9; TW201825860A; SG11201901045UA; WO2018031574A1

Abstract

A method of generating 3D information comprising: altering the distance between the sample and the objective lens of the optical microscope in predetermined steps; capturing an image at each predetermined step; determining a characteristic value for each pixel in each captured image; determining, for each captured image, a maximum characteristic value across a first portion of pixels in the captured image; comparing the maximum characteristic value for each captured image to determine whether a surface of the sample is present at each predetermined step; determining a first captured image focused on vertices of bumps of the sample; determining a second captured image focused on a first surface of the sample based on the characteristic value for each pixel in each captured image; and determining a first distance between the apex of the bump and the first surface.

Description

Method for generating three-dimensional information of sample using optical microscope

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a partial continuation OF a non-provisional U.S. patent application entitled "OPTICAL MEASUREMENT OF OPENING DIMENSIONS IN a WAFER" filed 2016, 10, 31, and claiming priority OF the U.S. patent application IN accordance with the 35u.s.c. § 120 specification, No. 15/338,838. The disclosure of that case is incorporated herein by reference in its entirety. Application 15/338,838 is a continuation-in-part of a non-provisional U.S. patent application entitled "AUTOMATED three-dimensional MEASUREMENT (AUTOMATED 3-D MEASUREMENT)" filed on 10/8/2016 and having serial number 15/233,812 and claiming priority of the same as specified in 35u.s.c. § 120. The disclosure of that case is incorporated herein by reference in its entirety.

Technical Field

The described embodiments relate generally to measuring three-dimensional information of a sample, and more particularly to automatically measuring three-dimensional information in a fast and reliable manner.

Background

Three-dimensional (3-D) measurements of various objects or samples are useful in many different applications. One such application is during wafer level packaging processes. Three-dimensional measurement information of a wafer during different steps of wafer-level manufacturing may provide insight into the presence of wafer processing defects that may be present on the wafer. Three-dimensional measurement information of a wafer during wafer-level manufacturing may provide insight as to the absence of defects before additional money is expended to continue processing the wafer. Three-dimensional measurement information of a specimen is currently collected by manual manipulation of a microscope. The human user focuses the microscope using his eye to determine when the microscope is focused on the surface of the sample. There is a need for improved methods of collecting three-dimensional measurement information.

Disclosure of Invention

In a first novel aspect, three-dimensional (3-D) information of a sample is generated using an optical microscope by: altering the distance between the sample and the objective lens of the optical microscope in predetermined steps; capturing an image at each predetermined step; determining a characteristic value for each pixel in each captured image; determining, for each captured image, a maximum characteristic value across all pixels in the captured image; comparing the maximum characteristic value for each captured image to determine whether a surface of the sample is present at each predetermined step; determining a first captured image focused on a first surface of the sample based on the characteristic value for each pixel in each captured image; determining a second captured image focused on a second surface of the sample based on the characteristic value for each pixel in each captured image; and determining a first distance between the first surface and the second surface.

In a second novel aspect, a three-dimensional (3-D) measurement system comprises: determining a thickness of a translucent layer of the sample; and determining a thickness of a metal layer of the sample, wherein the thickness of the metal layer is equal to a difference between the thickness of the translucent layer and a first distance, wherein a first surface is a top surface of a photoresist layer, and wherein a second surface is a top surface of a metal layer.

In a third novel aspect, three-dimensional (3-D) information of a sample is generated using an optical microscope by: altering the distance between the sample and the objective lens of the optical microscope in predetermined steps; capturing an image at each predetermined step; determining a characteristic value for each pixel in each captured image; determining, for each captured image, a maximum characteristic value across a first portion of pixels in the captured image; comparing the maximum characteristic value for each captured image to determine whether a surface of the sample is present at each predetermined step; determining a first captured image focused on vertices of bumps of the sample; determining a second captured image focused on a first surface of the sample based on the characteristic value for each pixel in each captured image; and determining a first distance between the apex of the bump and the first surface.

In a fourth novel aspect, a maximum characteristic value for each x-y pixel location within a second portion of x-y pixel locations across all captured images is determined, wherein the second portion of x-y pixel locations includes at least some of the x-y pixel locations included in each captured image; determining a subset of the captured images, wherein only captured images that include x-y pixel location maximum characteristic values are included in the subset; and determining that the first captured image is focused on a highest z-position, among all captured images within the subset of captured images, compared to all other captured images within the subset of captured images.

Additional details and embodiments, as well as techniques, are described in the detailed description below. This summary is not intended to define the invention. The invention is defined by the claims.

Drawings

The accompanying drawings, in which like numerals refer to like elements, illustrate embodiments of the invention.

Fig. 1 is a diagram of a semi-automated three-dimensional metrology system 1 that performs automated three-dimensional measurements of a sample.

Fig. 2 is a diagram of a three-dimensional imaging microscope 10 including an adjustable objective lens 11 and an adjustable stage 12.

Fig. 3 is a diagram of a three-dimensional metrology system 20 including a three-dimensional microscope, a sample handler, a computer, a display, and an input device.

Fig. 4 is a diagram illustrating a method of capturing an image when the distance between the objective lens and the stage of the optical microscope is changed.

Fig. 5 is a graph illustrating the distance between the objective lens of an optical microscope and the sample surface, with each x-y coordinate having the largest characteristic value.

Fig. 6 is a three-dimensional diagram of an image rendered using the maximum characteristic value for each x-y coordinate shown in fig. 5.

Fig. 7 is a diagram illustrating a peak mode operation using images captured at various distances.

Fig. 8 is a diagram illustrating peak mode operation using images captured at various distances when the photoresist opening is within the field of view of an optical microscope.

Fig. 9 is a graph illustrating three-dimensional information resulting from peak mode operation.

Fig. 10 is a diagram illustrating a summing mode operation using images captured at various distances.

Fig. 11 is a diagram illustrating false surface detection when operating using a summing mode.

Fig. 12 is a diagram illustrating three-dimensional information resulting from a summing mode operation.

Fig. 13 is a diagram illustrating a range mode operation using images captured at various distances.

FIG. 14 is a diagram illustrating three-dimensional information resulting from range mode operation.

Fig. 15 is a graph illustrating only pixel counts having characteristic values in the first range.

Fig. 16 is a graph illustrating only the pixel counts having the characteristic values in the second range.

Fig. 17 is a flow chart illustrating various steps involved in peak mode operation.

FIG. 18 is a flow chart illustrating various steps involved in range mode operation.

Fig. 19 is a diagram of a captured image (containing a single feature) focused on the top surface of a photoresist layer.

Fig. 20 is a diagram illustrating a first method of generating an intensity threshold.

Fig. 21 is a diagram illustrating a second method of generating an intensity threshold.

Fig. 22 is a diagram illustrating a third method of generating an intensity threshold.

Fig. 23 is a three-dimensional view of a photoresist opening in a sample.

Fig. 24 is a two-dimensional view of the top surface opening of the photoresist shown in fig. 23.

Fig. 25 is a two-dimensional view of the bottom surface opening of the photoresist shown in fig. 23.

Fig. 26 is a captured image focused on the top surface of the photoresist layer.

Fig. 27 is a view illustrating detection of the boundary of the photoresist layer illustrated in fig. 26.

Fig. 28 is a captured image focused on the bottom surface of the photoresist layer.

Fig. 29 is a view illustrating detection of the boundary of the photoresist layer illustrated in fig. 28.

Fig. 30 is a captured image focused on the top surface of a photoresist layer in a trench structure.

Fig. 31 is a view illustrating detection of the boundary of the photoresist layer illustrated in fig. 30.

Figure 32 is a three-dimensional view of a photoresist opening partially filled with metallization.

Figure 33 is a cross-sectional view of a photoresist opening partially filled with metallization.

Figure 34 is a three-dimensional view of a photoresist opening with metallization.

FIG. 35 is a cross-sectional view of a photoresist opening with metallization.

Fig. 36 is a three-dimensional view of a metal pillar over a passivation layer.

Fig. 37 is a cross-sectional view of a metal pillar over a passivation layer.

Fig. 38 is a three-dimensional view of metal over a passivation layer.

Fig. 39 is a cross-sectional view of metal over a passivation layer.

FIG. 40 is a cross-sectional view illustrating the measurement of a translucent material proximate to a metallized surface.

Fig. 41 is a diagram illustrating peak mode operation using images captured at various distances when the photoresist opening is within the field of view of an optical microscope.

Fig. 42 is a graph illustrating three-dimensional information derived from the peak mode operation illustrated in fig. 41.

Fig. 43 is a diagram of a captured image focused on the top surface of a photoresist layer in a trench structure, including the profiles of a first analysis area a and a second analysis area B.

Fig. 44 is a three-dimensional view of a bump over a passivation structure.

Fig. 45 is a top view of a bump over a passivation structure, including the profile of a first analysis area a and a second analysis area B.

Fig. 46 is a top view illustrating the adjustment analysis area a and the analysis area B when the entire bump is not positioned in the original analysis area a.

Fig. 47 is a cross-sectional view of a bump over a passivation structure.

Fig. 48 is a diagram illustrating peak mode operation using images captured at various distances when only the photoresist layer is within region B of the field of view of the optical microscope.

Fig. 49 is a graph illustrating three-dimensional information resulting from the peak mode operation of fig. 48.

Detailed Description

Reference will now be made in detail to background examples and some embodiments of the invention, examples of which are illustrated in the accompanying drawings. In the following description and claims, relational terms, such as "top," "lower," "upper," "lower," "top," "bottom," "left," and "right," may be used to describe the relative orientation between different portions of the described structure, and it is understood that the described overall structure may be oriented in three dimensions in virtually any manner.

Fig. 1 is a diagram of a semi-automated three-dimensional metrology system 1. The semi-automated three-dimensional metrology system 1 includes 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 three-dimensional metrology system 1 is to capture multiple images of an object and automatically generate three-dimensional information describing various surfaces of the object. This is also referred to as "scanning" of the object. The wafer 3 is an example of an object analyzed by the semi-automated three-dimensional metrology system 1. The object may also be referred to as a sample. In operation, a wafer 3 is placed on the stage 2 and the semi-automated three-dimensional metrology system 1 begins a process of automatically generating three-dimensional information describing the surface of the wafer 3. In one example, the semi-automated three-dimensional metrology system 1 begins by pressing a designated key on a keyboard (not shown) connected to the computer 4. In another example, the semi-automated three-dimensional metrology system 1 begins by sending a start command to the computer 4 across a network (not shown). The semi-automated three-dimensional metrology system 1 may also be configured to interface with an automated wafer handling system (not shown) that removes wafers after their scanning is complete and inserts new wafers for scanning.

A fully automated three-dimensional metrology system (not shown) similar to the semi-automated three-dimensional metrology system of fig. 1; however, fully automated three-dimensional metrology systems also include robotic handlers that can automatically pick up and place wafers on the stage without human intervention. In a similar manner, a fully automated three-dimensional metrology system may also automatically pick up and remove a wafer from a stage using a robotic handler. A fully automated three-dimensional metrology system may be desirable during the production of many wafers because it avoids possible contamination of human operators and improves time efficiency and overall cost. Alternatively, a semi-automated three-dimensional metrology system 1 may be desirable during research and development activities when only a small number of wafers need to be measured.

Fig. 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 may be a confocal microscope, a structured illumination microscope, an interferometric microscope, or any other type of microscope well known in the art. The confocal microscope will measure the intensity. The structured illumination microscope will measure the contrast of the projected structure. The interferometer microscope will measure the 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 as the height of the stage (on which the wafer rests) is adjusted. This results in multiple images of the wafer being captured while the wafer is positioned at various distances away from the selected lens. In one alternative example, the wafer is placed on a fixed stage and the position of the objective lens is adjusted, thereby altering the distance between the objective lens and the sample without moving the stage. In another example, the stage may be adjusted in the x-y direction and the objective lens may be adjusted in the z direction.

The captured images may be stored locally in a memory included in the three-dimensional imaging microscope 10. Alternatively, the captured images may be stored in a data storage device included in the computer system, wherein the three-dimensional microscope 10 transfers the captured images to the computer system across a data communication link. Examples of data communication links include: a Universal Serial Bus (USB) interface, an ethernet connection, a firewire bus interface, a wireless network (e.g., WiFi).

Fig. 3 is a diagram of a three-dimensional metrology system 20 including a three-dimensional microscope 21, a sample handler 22, a computer 23, a display 27 (optional), and an input device 28. The three-dimensional metrology system 20 is an example of a system included in the semi-automated three-dimensional metrology system 1. The computer 23 includes a processor 24, a storage device 25, and a network device 26 (optional). The computer outputs information to the user via the display 27. If the display 27 is a touch screen device, the display may also be used as an input device. The input device 28 may 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 the three-dimensional microscope for image capture ("microscope control data"). For example, the correct objective lens needs to be selected, the resolution of the image to be captured needs to be selected, and the mode in which the captured image is stored needs to be selected. 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 ("handler control data"). For example, the correct height (z direction) adjustment needs to be selected and the correct horizontal (x-y direction) alignment needs to be selected.

During operation, the computer 23 causes the sample handler/stage 22 to adjust 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 the focal plane and capture at least one image. The computer 23 will then cause the stage to move in the z direction so that the distance between the sample and the objective of the optical microscope is changed. Once the stage is moved to the new position, the computer 23 will cause the optical microscope to capture a second image. This process continues until an image is captured at each desired distance between the objective lens of the optical microscope and the sample. The images captured at each distance are transferred from the three-dimensional microscope 21 to a computer 23 ("image data"). The captured image is stored in a storage device 25 included in the computer 23. In one example, computer 23 analyzes the captured image and outputs three-dimensional information to display 27. In another example, the computer 23 analyzes the captured image and outputs three-dimensional information to a remote device via the network 29. In yet another example, the computer 23 does not analyze the captured image, but rather sends the captured image to another device for processing via the network 29. The three-dimensional information may include a three-dimensional image rendered based on the captured image. The three-dimensional information may not include any images, but rather data based on various characteristics of each captured image.

Fig. 4 is a diagram illustrating a method of capturing an image while changing the distance between the objective lens of the optical microscope and the sample. In the embodiment illustrated in FIG. 4, each image comprises 1000 by 1000 pixels. In other embodiments, an image may include various pixel configurations. In one example, the spacing between successive distances is fixed to a predetermined amount. In another example, the spacing between successive distances may not be fixed. This unfixed spacing between images in the z-direction may be advantageous in case only part of the z-direction scan of the sample requires additional z-direction resolution. The z-direction resolution is based on the number of images captured per unit length in the z-direction, so capturing additional images per unit length in the z-direction will increase the measured z-direction resolution. Conversely, capturing 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 a focal plane located at a distance 1 from the objective lens of the optical microscope. The optical microscope then captures an image, which is stored in a storage device (i.e., "memory"). Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 2. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 3. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 4. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 5. The optical microscope then captures an image, which is stored in a storage device. The process 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 alternative embodiment, the distance between the objective of the optical microscope and the sample is fixed. Instead, the optical microscope includes a zoom lens that allows the optical microscope to alter the focal plane of the optical microscope. In this way, the focal plane of the optical microscope varies across N different focal planes when the stage and the sample supported by the stage are fixed. An image is captured for each focal plane and stored in a storage device. The captured images across all of the various focal planes are then processed to determine three-dimensional information of the sample. This embodiment requires a zoom lens that can provide sufficient resolution across all focal planes and introduce minimal image distortion. In addition, calibration between each zoom position and the resulting focal length of the zoom lens are required.

Fig. 5 is a graph illustrating the distance between the objective lens and the sample of an optical microscope, with each x-y coordinate having the largest characteristic value. Once the images are captured and stored for each distance, the characteristics of each pixel of each image may be analyzed. For example, the light intensity of each pixel of each image may be analyzed. In another example, the contrast of each pixel of each image may be analyzed. In yet another example, the fringe contrast for each pixel of each image may be analyzed. The contrast of a pixel may be determined by comparing the intensity of the pixel with the intensities of a preset number of surrounding pixels. For additional description of how contrast information is generated, see U.S. patent application entitled "three-dimensional Optical Microscope (3-D Optical Microscope)" entitled U.S. patent application serial No. 12/699,824 by James sanctuary women (James Jianguo Xu), et al, filed on 3.2.2010 (the subject matter of which is incorporated herein by reference).

Fig. 6 is a three-dimensional diagram of a three-dimensional image rendered using the maximum characteristic value for each x-y coordinate shown in fig. 5. All pixels with an X-position between 1 and 19 have the largest characteristic value at a z-direction distance of 7. All pixels with an X-position between 20 and 29 have the largest characteristic value at a z-direction distance of 2. All pixels with an X-position between 30 and 49 have the largest characteristic value at a z-direction distance of 7. All pixels with an X-position between 50 and 59 have the largest characteristic value at a z-direction distance of 2. All pixels with an X-position between 60 and 79 have the largest characteristic value at a z-direction distance of 7. In this way, the three-dimensional image illustrated in FIG. 6 may be generated using the maximum characteristic value per x-y pixel across all captured images. Additionally, where distance 2 is known and distance 7 is known, the well depth illustrated in FIG. 6 may be calculated by subtracting distance 7 from distance 2.

Peak mode operation

Fig. 7 is a diagram illustrating a peak mode operation using images captured at various distances. As discussed above with respect to fig. 4, the optical microscope is first adjusted to focus on a plane located at a distance 1 from the objective lens of the optical microscope. The optical microscope then captures an image, which is stored in a storage device (i.e., "memory"). Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 2. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 3. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 4. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 5. The optical microscope then captures an image, which is stored in a storage device. The process continues for N different distances between the objective lens and the stage of the optical microscope. Information indicating which image is associated with each distance is also stored in the storage device for processing.

Instead of determining the maximum characteristic value for each x-y location across all captured images at various z-distances, the maximum characteristic value is determined in peak mode operation across all x-y locations in a single captured image at one z-distance. In other words, for each captured image, the maximum characteristic value across all pixels included in the captured image is selected. As illustrated in fig. 7, the pixel location having the largest characteristic value will likely vary between different captured images. The characteristic may be intensity, contrast, or fringe contrast.

Fig. 8 is a diagram illustrating peak mode operation using images captured at various distances when the Photoresist (PR) opening is within 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 openings also have a depth of a certain depth in the z direction. The top view in fig. 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 on the top surface of the wafer. This results in increased characteristic values (intensity/contrast/fringe contrast) in pixels that receive light reflected from the bottom surface of the PR opening compared to pixels that receive light reflected from other surfaces (the top surface of the wafer) that are out of focus. At distance 3, the optical microscope is not focused on the top surface of the wafer or the bottom surface of the PR opening. Thus, at distance 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, an increase in the maximum specific value (intensity/contrast/fringe contrast) was measured due to the difference in the refractive index of air and the refractive index of the photoresist layer. Fig. 11 and the accompanying text describe this phenomenon in more detail. At distance 6, the optical microscope is focused on the top surface of the wafer, but not on the bottom surface of the PR opening. This results in increased characteristic values (intensity/contrast/fringe contrast) in pixels receiving light reflected from the top surface of the wafer compared to pixels receiving light reflected from other surfaces out of focus (bottom surfaces of PR openings). Once the maximum characteristic value from each captured image is determined, the results can be utilized to determine at which distances the surface of the wafer is positioned.

Fig. 9 is a graph illustrating three-dimensional information resulting from peak mode operation. As discussed with respect to fig. 8, the maximum characteristic values of the images captured at

distances

1, 3, and 5 have maximum particular values that are less than the maximum particular values of the images captured at

distances

2, 4, and 6. The curves of maximum characteristic values at various z-distances may contain noise due to environmental effects, such as vibration. To minimize this noise, standard smoothing methods, such as Gaussian filtering (Gaussian filtering) with some kernel size, may be applied prior to further data analysis.

One method of comparing the maximum characteristic values is performed 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 at which each "peak" exists. The maximum characteristic value at each distance at which a peak is found is then compared to determine the distance measured to the maximum characteristic value. In the case of fig. 9, a peak will be found at distance 2, which serves as an indication that the surface of the wafer is located at distance 2.

Another method of comparing the maximum characteristic values is performed by comparing each maximum characteristic value with a preset threshold value. The threshold value may be calculated based on wafer material, distance, and specifications of the optical microscope. Alternatively, the threshold may be determined by empirical testing prior to automated processing. In either case, the maximum characteristic value of each captured image is compared to a threshold value. If the maximum characteristic value is greater than the threshold value, it is determined that the maximum characteristic value indicates the presence of the surface of the wafer. If the maximum characteristic value is not greater than the threshold value, it is determined that the maximum characteristic value is not indicative of the surface of the wafer.

Summing mode operation

Fig. 10 is a diagram illustrating a summing mode operation using images captured at various distances. As discussed above with respect to fig. 4, the optical microscope is first adjusted to focus on a plane located at a distance 1 from the objective lens of the optical microscope. The optical microscope then captures an image, which is stored in a storage device (i.e., "memory"). Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 2. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 3. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 4. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 5. The optical microscope then captures an image, which is stored in a storage device. The process 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.

Rather than determining the maximum characteristic value across all x-y locations in a single captured image at one z-distance, the characteristic values for all x-y locations of each captured image are added together. In other words, for each captured image, the characteristic values for all pixels included in the captured image are summed together. The characteristic may be intensity, contrast, or fringe contrast. A summed characteristic value that is substantially greater than the average summed characteristic value for adjacent z distances indicates that the surface of the wafer is present at that distance. However, this approach may also result in false positives (false positives) as described in fig. 11.

Fig. 11 is a diagram illustrating false surface detection when operating using a summing mode. The wafer illustrated in fig. 11 includes a silicon substrate 30 and a photoresist layer 31 deposited on top of the silicon substrate 30. The top surface of the silicon substrate 30 is positioned at a distance 2. The top surface of photoresist layer 31 is positioned at a distance 6. The image captured at distance 2 will result in a substantially greater sum of characteristic values 12 than other images captured at distances where the surface of the wafer is not present. The image captured at distance 6 will result in a substantially greater sum of the characteristic values 16 than other images captured at distances where the surface of the wafer is not present. At this point, the summing mode operation appears to be a valid indicator of the presence of the surface of the wafer. However, an image captured at distance 4 will result in a substantially greater sum of the characteristic values of other images captured at distances where the surface of the wafer is not present. This is a problem because, as clearly shown in fig. 11, the surface of the wafer is not positioned at distance 4. Instead, an increase in the sum of the characteristic values at distance 4 is an artifact of the surfaces located at

distances

2 and 6. A major portion of the light that irradiates the photoresist layer is not reflected, but travels into the photoresist layer. The angle at which this light travels changes due to the difference in the refractive indices of air and photoresist. The new angle is closer to normal than the light angle that irradiates the top surface of the photoresist. The light travels to the top surface of the silicon substrate below the photoresist layer. The light is then reflected by the highly reflective silicon substrate layer. As the reflected light exits the photoresist layer and enters 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 redirection, reflection and second redirection of the radiated light causes the optical microscope to observe an increase in the characteristic value (intensity/contrast/fringe contrast) at distance 4. This example illustrates that whenever a sample includes transparent material, the summing mode operation will detect surfaces that are not present on the sample.

Fig. 12 is a diagram illustrating three-dimensional information resulting from a summing mode operation. This graph illustrates the results of the phenomenon illustrated in fig. 11. A large value of the summed characteristic value at distance 4 falsely indicates the presence of a surface at distance 4. There is a need for a method that does not produce a false positive indication of the presence of the surface of the wafer.

Range mode operation

Fig. 13 is a diagram illustrating a range mode operation using images captured at various distances. As discussed above with respect to fig. 4, the optical microscope is first adjusted to focus on a plane located at a distance 1 from the objective lens of the optical microscope. The optical microscope then captures an image, which is stored in a storage device (i.e., "memory"). Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 2. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 3. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 4. The optical microscope then captures an image, which is stored in a storage device. Next, the stage was adjusted so that the distance between the objective lens of the optical microscope and the sample was distance 5. The optical microscope then captures an image, which is stored in a storage device. The process 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.

Rather than determining the sum of all characteristic values across all x-y locations in a single captured image at one z-distance, a count of pixels in the single captured image having characteristic values within a particular range is determined. In other words, for each captured image, a count of pixels having a characteristic value within a particular range is determined. The characteristic may be intensity, contrast, or fringe contrast. The pixel count at a particular z-distance that is substantially greater than the average pixel count at adjacent z-distances indicates the presence of the surface of the wafer at that distance. This approach reduces the false positives depicted in fig. 11.

FIG. 14 is a diagram illustrating three-dimensional information resulting from range mode operation. With knowledge of the different material types and optical microscope configurations present on the wafer, an expected range of characteristic values may be determined for each material type. For example, the photoresist layer will reflect a relatively small amount of light (i.e., 4%) that radiates the top surface of the photoresist layer. The silicon layer will reflect light that radiates the top surface of the silicon layer (i.e., 37%). The redirected reflection from the top surface of the photoresist layer observed at distance 4 (i.e., 21%) will be substantially greater than the reflection observed at distance 6; however, the redirected reflection from the top surface of the silicon substrate observed at distance 4 (i.e., 21%) will be substantially less than the reflection observed at distance 2. Thus, when looking for the top surface of the photoresist layer, a first range centered on the expected characteristic value of the photoresist may be used to filter out pixels having characteristic values outside the first range, thereby filtering out pixels having characteristic values that do not originate from reflections of the top surface of the photoresist layer. The pixel count across all distances generated by applying the first range of property values is illustrated in FIG. 15. As shown in fig. 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 the plurality of distances fall within a first range. However, applying the first range before counting pixels still serves to make the pixel count at the desired surface more prominent than other pixel counts at other distances. This is illustrated in fig. 15. After applying the first range, the pixel count at distance 6 is greater than the pixel counts at

distances

2 and 4, while before applying the first range, the pixel count at distance 6 is less than the pixel counts at distances 2 and 4 (as shown in fig. 14).

In a similar manner, when looking for the top surface of the silicon substrate layer, a second range centered on the expected characteristic value of the silicon substrate layer may be used to filter out pixels having characteristic values outside the second range, thereby filtering out pixels having characteristic values that do not originate from reflections of the top surface of the silicon substrate layer. The pixel count across all distances generated by applying the second range of property values is illustrated in FIG. 16. This range of applications reduces false indications that the wafer surface is positioned at distance 4 by knowing the expected property values of all materials present on the scanned wafer. As discussed with respect to fig. 15, some but not necessarily all pixels from other distances (surfaces) are filtered out by the application range. However, when the measured characteristic values at multiple distances do not fall within the same range, then the result of applying the range will eliminate all pixel counts from other distances (surfaces). Fig. 16 illustrates this case. In fig. 16, a second range is applied before generating the pixel count at each distance. The result of applying the second range is to count only the pixels at distance 2. This produces a very clear indication that the surface of the silicon substrate is located at a distance 2.

It should be noted that to reduce the effects caused by potential noise (e.g., environmental vibrations), a standard smoothing operation (e.g., gaussian filtering) may be applied to the total pixel count along the z-distance before any peak search operation is performed.

Fig. 17 is a flow chart 200 illustrating various steps involved in peak mode operation. In step 201, the distance between the specimen and the objective lens of the optical microscope is changed in predetermined steps. In step 202, an image is captured at each predetermined step. In step 203, characteristics of each pixel in each captured image are determined. In step 204, for each captured image, the maximum characteristic across all pixels in the captured image is determined. In step 205, the maximum characteristics of each captured image are compared to determine if there is a surface of the sample at each predetermined step.

FIG. 18 is a flowchart 300 illustrating various steps involved in range mode operation. In step 301, the distance between the sample and the objective lens of the optical microscope is changed in predetermined steps. In step 302, an image is captured at each predetermined step. In step 303, characteristics of each pixel in each captured image are determined. In step 304, for each captured image, a count of pixels having a characteristic value within a first range is determined. In step 305, it is determined whether there is a surface of the sample at each predetermined step based on the pixel count of each captured image.

FIG. 19 is a diagram of a captured image including a single feature. One example of a feature is an opening in the photoresist layer that is circular in shape. Another example of a feature is a trench-shaped opening in a photoresist layer, such as an unplated redistribution line (RDL) structure. During the wafer fabrication process, it is advantageous to measure various characteristics of photoresist openings in wafer layers. Measurement of the photoresist opening provides detection of defects in the structure prior to metal plating into the hole. For example, if the photoresist opening is not of the correct size, the plating RDL width will be wrong. Detecting this type of defect may prevent further fabrication of defective wafers. Preventing further fabrication of defective wafers 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 light reflected from the top surface of the photoresist layer is greater than the measured intensity of light reflected from the opening in the photoresist layer. As discussed in more detail below, information associated with each pixel in the captured image may be used to generate an intensity value for each pixel in the captured image. The intensity value of each pixel may then be compared to an intensity threshold to determine whether each pixel is associated with a first region of the captured image (e.g., the top surface of the photoresist layer) or with a second region of the captured image (e.g., the photoresist opening region). This can be done by: (i) first applying an intensity threshold to the measured intensity of each pixel in the captured image; (ii) classifying all pixels having intensity values below an intensity threshold as being associated with a first region of the captured image; (iii) classifying all pixels having intensity values above an intensity threshold as being associated with a second region of the captured image; and (iv) defining the feature as a group of pixels within the same region that neighbor other pixels associated with the same region.

The captured image shown in fig. 19 may be a color image. Each pixel of the color image includes red, blue, and green (RBG) channel values. Each of these color values may be combined to produce a single intensity value for each pixel. Various methods for converting the RBG value of each pixel to a single intensity value are described below.

The first method is to convert three color channels into intensity values using three weighting values. In other words, each color channel has its own weighting value or conversion factor. The three conversion factors may be modified using a default set of three conversion factors defined in the system recipe or based on their sample measurement requirements. The second method is to subtract the color channel of each pixel from the default color channel value of each color channel, then convert this result to an intensity value using the conversion factor discussed in the first method. A third approach is to convert the color to an intensity value using a "color difference" scheme. In a color difference scheme, the resulting pixel intensity is defined by how close the color of the pixel is compared to predefined fixed red, green, and blue color values. One example of a color difference is a weighted vector distance between the color value of a pixel and a fixed color value. Yet another method of "color difference" is a color difference method with fixed color values automatically derived from the image. In one example, the border region where the image is known to have a background color. A weighted average of the colors of the border region pixels may 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 may be compared to the intensity of each pixel to determine the image region to which the pixel belongs. In other words, pixels having an intensity value above the intensity threshold indicate that the pixel receives light reflected from the first surface of the sample, and pixels having an intensity value below the intensity threshold indicate that the pixel does not receive light reflected from the first surface of the sample. Once each pixel in the image is mapped to a region, the approximate shape of the feature focused in the image can be determined.

Fig. 20, 21, and 22 illustrate three different methods of generating intensity thresholds that can be used to distinguish between pixels that measure light reflected from the top surface of the photoresist layer and pixels that measure light that is not reflected from the top surface of the photoresist layer.

FIG. 20 illustrates a first method of generating an intensity threshold for analyzing a captured image. In this first method, a pixel count is generated for each measured intensity value. This type of map is also referred to as a histogram. Once a pixel count per intensity value is generated, an intensity range between a peak count of pixels originating from measured light reflected from the photoresist layer and a peak count of pixels originating from measured light not reflected from the photoresist layer may be determined. Selecting intensity values within the intensity range as intensity thresholds. In one example, the midpoint between two peak counts is selected as the threshold intensity. In other examples that fall within the disclosure of the present disclosure, other intensity values between two peak counts may be used.

FIG. 21 is a second method of generating an intensity threshold 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. This determination may be made by physical measurement, 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 photoresist open area. This determination may be made by physical measurement, 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, all pixels having intensities within the second to last percentage of the intensities of all pixels are selected. In step 315, all selected pixels are analyzed.

FIG. 22 illustrates a third method of determining an intensity threshold. In step 321, the predetermined intensity threshold is stored in memory. In step 322, the intensity of each pixel is compared to a stored intensity threshold. In step 323, all pixels having intensity values less than the intensity threshold are selected. In step 324, the selected pixel is analyzed.

Regardless of how the intensity threshold is generated, the threshold intensity value is used to approximately determine where the boundary of the feature in the captured image is located. The approximate boundaries of the features will then be used to determine a more accurate measure of the boundaries of the features, as discussed below.

Fig. 23 is a three-dimensional view of the photoresist opening shown in fig. 19. Various photoresist opening measurements are of interest during the fabrication process, such as the areas of the top and bottom openings, the diameters of the top and bottom openings, the circumferences of the top and bottom openings, the cross-sectional widths of the top and bottom openings, and the depths of the openings. The first measurement is the top surface open area. Fig. 8 (and accompanying text) describes how to select an image focused on the top surface of a photoresist opening and an image focused on the bottom surface of the photoresist opening from a plurality of images obtained at different distances from the sample. Once the image focused on the top surface is selected, the image focused on the top surface of the photoresist opening can be used to determine the top opening measurement described above. Likewise, once the image focused on the bottom surface of the photoresist opening is selected, the bottom opening measurement described above can be determined using the image focused on the bottom surface of the photoresist opening. As discussed in U.S. patent application serial No. 12/699,824 entitled "three-dimensional Optical Microscope" (3-D Optical Microscope) "filed at 2/2010 by James licensing women, et al, supra, and James, Xu (James Jianguo Xu), the subject matter of which is incorporated herein by reference, a pattern or grid can be projected onto the surface of a specimen as multiple images are captured. In one example, an image including a projected pattern or grid is used to determine photoresist opening measurements. In another example, a new image captured at the same z-distance that does not include a pattern or grid is used to determine a photoresist opening measurement. In the latter example, a new image without a projected pattern or grid on the sample provides a "sharper" image, which provides easier detection of the boundaries of the photoresist openings.

Fig. 24 is a two-dimensional view of the top surface opening shown in fig. 23. The two-dimensional diagram clearly shows the boundaries of the top surface opening (i.e., the top surface of the PR opening at a distance 6 in the z-direction) (solid line) 40. The boundary is tracked using the best fit line (dashed line 41). Once the best fit line tracking is generated, the diameter, area and circumference of the best fit line 41 may be generated.

Fig. 25 is a two-dimensional view of the bottom surface opening illustrated in fig. 23. The two-dimensional plot clearly shows the boundaries of the bottom surface opening (i.e., the top surface of the PR opening at a distance 2 in the z-direction) (solid line 42). The boundary is tracked using the best fit line (dashed line 43). Once the best fit line tracking is generated, the bottom surface opening diameter, area and circumference of the best fit line can be calculated.

In this example, the best fit line is automatically generated by a computer system in communication with the optical microscope. The best fit line may be generated by analyzing transitions between dark and light portions of the selected image, as discussed in more detail below.

Fig. 26 is a two-dimensional image of an opening in a photoresist layer. The image is focused 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 whether the pixel belongs to the top surface of the photoresist or an opening in the photoresist. The intensity change from the transition between the top surface of the photoresist and the opening in the photoresist may span multiple pixels and multiple intensity levels. The image background intensity may not be uniform. Therefore, further analysis is required to determine the exact pixel location of the boundary of the photoresist. To determine the pixel location of a single surface transition point, intensity averages are obtained in adjacent bright areas outside the transition region and intensity averages are obtained in adjacent dark areas outside the transition region. The intermediate intensity value between the average of adjacent bright areas and the average of adjacent dark areas is used as an intensity threshold to distinguish whether a pixel belongs to the top surface of the photoresist or an opening in the photoresist. This intensity threshold may be different than the previously discussed intensity threshold used to select features within a single captured image. Once the intermediate intensity 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 above the intensity threshold, the pixel is determined to be a photoresist pixel. If the pixel intensity is below the intensity threshold, the pixel is determined to be an open area pixel. Multiple boundary points may be determined in this manner and used to fit the shape. The fitted shape is then used to calculate all desired dimensions of the top opening of the photoresist. In one example, the fitted shape may be selected from the group having: circular, square, rectangular, triangular, oval, hexagonal, pentagonal, etc.

Fig. 27 illustrates the variation in measured intensity across neighboring regions around the luminance transition of fig. 26. At the leftmost part of the neighboring region, the measured intensity is higher 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 neighboring region. The measured light intensity drops to a minimum range at the rightmost portion of the neighboring region because there is no top surface of the photoresist layer in the rightmost portion of the neighboring region. Fig. 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. A boundary point where the top surface of the photoresist ends is positioned at the intersection of the measured intensity and the threshold intensity. This process is repeated at different adjacent regions located along the brightness transition. Boundary points are determined for each neighboring region. The boundary points of each neighboring region are then used to determine the size and shape of the top surface boundary.

Fig. 28 is a two-dimensional image of an opening in a photoresist layer. An image is focused on the bottom surface of the photoresist opening. In this example, the light reflected from the bottom surface of the photoresist opening area is bright because the microscope is focused on the bottom surface of the photoresist opening. The light reflected from the photoresist region is also relatively bright because the substrate is a silicon or metal seed layer with high reflectivity. Light reflected from the boundary of the photoresist layer is dark due to light scattering caused by the boundary of the photoresist. The measured intensity of each pixel is used to determine whether 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 photoresist opening region can span multiple pixels and multiple intensity levels. The image background intensity may not be uniform. Therefore, further analysis is required to determine the exact pixel location of the photoresist opening. To determine the pixel location of the boundary point, the location of the pixel with the smallest intensity is determined within the neighboring pixels. Multiple boundary points may be determined in this manner and used to fit the shape. The fit shape is then used to calculate the desired size of the bottom opening.

Fig. 29 illustrates the variation in measured intensity across neighboring regions around the luminance transition of fig. 28. At the rightmost portion of the adjacent region, the measured intensity is higher 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 a brightness transition of the neighboring region. The measured light intensity rises to a relatively high intensity range at the rightmost portion of the adjacent region due to light reflection from the substrate surface. Fig. 29 plots this change in measured intensity across adjacent regions. Boundary points indicating where the boundary of the photoresist opening is located may then be determined by finding the location of the minimum measured intensity. The boundary point is positioned where the minimum measured intensity is located. The process is repeated at different adjacent regions located along the brightness transition. Boundary points are determined for each neighboring region. The boundary points of each neighboring region are then used to determine the size and shape of the bottom surface boundary.

Fig. 30 is a two-dimensional image of a trench structure (e.g., an unplated redistribution line (RDL) structure) in a photoresist layer. The image is focused 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. Light reflected from the openings in the photoresist layer is darker because less light is reflected from the open trench regions. The intensity of each pixel is used to determine whether the pixel belongs to the top surface of the photoresist or an open area in the photoresist. The intensity change from the transition between the top surface of the photoresist and the open area in the photoresist may span multiple pixels and multiple intensity levels. The image background intensity may not be uniform. Therefore, further analysis is required to determine the exact pixel location of the boundary of the photoresist. To determine the pixel location of a single surface transition point, intensity averages are obtained in adjacent bright areas outside the transition region and intensity averages are obtained in adjacent dark areas outside the transition region. The intermediate intensity value between the average of adjacent bright areas and the average of adjacent dark areas is used as an intensity threshold to distinguish between top surface photoresist reflections and non-top surface photoresist reflections. Once the intermediate intensity threshold is determined, the intermediate intensity threshold is compared to all adjacent pixels to determine the boundary between the top surface pixel and the photoresist opening area. If the pixel intensity is above the intensity threshold, the pixel is determined to be a top surface photoresist pixel. If the pixel intensity is below the intensity threshold, the pixel is determined to be a photoresist opening area pixel. Multiple boundary points may be determined in this manner and used to fit the shape. The fitted shape is then used to calculate all desired dimensions of the photoresist opening of the trench, such as the trench width.

Fig. 31 illustrates the variation in measured intensity across neighboring regions around the luminance transition of fig. 30. At the leftmost part of the neighboring region, the measured intensity is higher 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 neighboring region. The measured light intensity drops to a minimum range at the rightmost portion of the neighboring region because there is no top surface of the photoresist layer in the rightmost portion of the neighboring region. Fig. 31 plots this variation in measured intensity across adjacent regions. A boundary point indicating the end of the top surface of the photoresist layer may then be determined by applying a threshold intensity. A boundary point where the top surface of the photoresist ends is positioned at the intersection of the measured intensity and the threshold intensity. This process is repeated at different adjacent regions located along the brightness transition. Boundary points are determined for each neighboring region. The boundary points of each neighboring region are then used to determine the size and shape of the top surface boundary.

With respect to fig. 26-31, pixel intensity is merely one example of pixel characteristics of pixels that may be used to distinguish different regions in an image. For example, pixels from different regions in an image may also be similarly distinguished using the wavelength or color of each pixel. Once the boundaries between each region are precisely defined, the boundaries are then used to determine the Critical Dimension (CD) of the PR opening, such as its diameter or width. Typically, 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). This type of cross-calibration is necessary to ensure measurement accuracy in production monitoring tools.

Figure 32 is a three-dimensional view of a photoresist opening partially filled with metallization. The opening in the photoresist layer is in the shape of a trench, such as a plated redistribution line (RDL) structure. During the wafer fabrication process, it is advantageous to measure various characteristics of the metallization deposited into the photoresist openings while the photoresist is still intact. For example, if the thickness of the metal is not thick enough, additional metal can be plated at all times, as long as the photoresist has not been stripped. The ability to discover potential problems while the wafer is still in the reworkable stage prevents further fabrication of defective wafers and saves material and processing costs.

Figure 33 is a cross-sectional view of a photoresist opening partially filled with metallization. Fig. 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 illustrated in fig. 33. 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 the "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. Another measurement of the thickness of the photoresist area is required to determine the thickness of the metallization. As discussed above with respect to fig. 11, the photoresist regions are translucent and have a refractive index different from the refractive index of the open air. Thus, the focal plane of the captured image focused on 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. It is not desirable to filter out erroneous surface measurements, but the thickness of the photoresist area is now required. Fig. 40 illustrates how a portion of incident light that is not reflected from the top surface of a photoresist region travels through the photoresist region at a different angle than 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 is D '(the measured z-position of the captured image focused on light reflected from the top surface of the photoresist region minus the measured z-position of the captured image focused on light reflected from the bottom surface of the photoresist region), the measured thickness D' clearly illustrated in FIG. 40 is not close to the actual thickness D of the photoresist region. However, errors 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. 40, where 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. 40, where 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 takes into account the fact that: the refractive index of the photoresist varies depending on the wavelength and the spherical aberration of the objective lens can affect the z-position measurement when imaged through the transparent medium. Thus, the actual thickness of the photoresist region can be calculated using the z-position of the focal plane of the captured image focused on the light reflected from the bottom surface of the photoresist region, so long as an appropriate calibration procedure is followed.

Once the correction equation is applied to the measured thickness of the photoresist region, the true thickness of the photoresist region may be obtained. Referring again to fig. 33, the thickness of the metallization can now be calculated. The thickness of the metallization is equal to the thickness of the photoresist region minus the difference between 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 a circular photoresist opening with metallization. FIG. 35 is a cross-sectional view of a circular photoresist opening with metallization shown in FIG. 34. The cross-sectional view of fig. 35 is similar to the cross-sectional view of fig. 33. Fig. 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 the "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. Another measurement of the thickness of the photoresist area is required to determine the thickness of the metallization. As discussed above with respect to fig. 11, the photoresist regions are translucent and have a refractive index different from the refractive index of the open air. Thus, the focal plane of the captured image focused on 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. Fig. 40 illustrates how a portion of incident light that is not reflected from the top surface of a photoresist region travels through the photoresist region at a different angle than 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 is D '(the measured z-position of the captured image focused on light reflected from the top surface of the photoresist region minus the measured z-position of the captured image focused on light reflected from the bottom surface of the photoresist region), the measured thickness D' clearly illustrated in FIG. 40 is not close to the actual thickness D of the photoresist region. However, errors 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. 40, where 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. 40, where 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 takes into account the fact that: the refractive index of the photoresist varies depending on the wavelength and the objective lens spherical aberration can affect the z position measurement when imaged through the transparent medium. Thus, as long as an appropriate calibration process is followed, the actual thickness of the photoresist region can be calculated using the z-position 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 region can be obtained. Referring again to fig. 35, the thickness of the metallization can now be calculated. The thickness of the metallization is equal to the thickness of the photoresist region minus the difference between the z-position of the top surface of the photoresist region and the z-position of the top surface of the metallization.

Fig. 36 is a three-dimensional view of a metal pillar over a passivation layer. Fig. 37 is a cross-sectional view of a metal pillar over the passivation layer shown in fig. 36. Fig. 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 illustrated in fig. 37. 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 the "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 fig. 11, the translucent material (e.g., photoresist regions or passivation layers) has a refractive index that is different from the refractive index of the open air. Therefore, the focal plane of the captured image 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. Fig. 47 illustrates how a portion of incident light that is not reflected from the top surface of the passivation layer travels through the passivation layer at a different angle than 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 is D '(the measured z-position of the captured image focused on light reflected from the top surface of the passivation region minus the measured z-position of the captured image focused on light reflected from the bottom surface of the passivation region), the measured thickness D' clearly illustrated in fig. 47 is not close to the actual thickness D of the passivation layer. However, errors introduced by the refractive index of the passivation layer may be removed by applying a correction calculation to the measured thickness of the passivation layer. A first correction calculation is shown in fig. 47, where 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. 47, where the actual thickness of the passivation layer (D) is equal to the measured thickness of the passivation layer (D') multiplied by the refractive index of the passivation layer plus an offset value. The second correction calculation is more general and takes into account the fact that: the refractive index of the passivation layer varies depending on the wavelength and the objective lens spherical aberration can affect the z-position measurement when imaged through the transparent medium. Thus, the z-position of the focal plane of the captured image focused on the light reflected from the bottom surface of the passivation layer is used to calculate the actual thickness of the passivation layer as long as the proper calibration procedure is followed.

Once the correction program is applied to the measured thickness of the passivation layer, the true thickness of the passivation layer may be obtained. Referring again to fig. 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.

Fig. 38 is a three-dimensional view of metal over a passivation layer. In this particular case, the metal structure shown is a redistribution line (RDL). Fig. 39 is a cross-sectional view of the metal over the passivation layer shown in fig. 38. Fig. 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 the "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 fig. 11, the translucent material (e.g., photoresist regions or passivation layers) has a refractive index that is different from the refractive index of the open air. Therefore, the focal plane of the captured image 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. Fig. 40 illustrates how a portion of incident light that is not reflected from the top surface of the passivation layer travels through the passivation layer at a different angle than 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 is D '(the measured z-position of the captured image focused on light reflected from the top surface of the passivation region minus the measured z-position of the captured image focused on light reflected from the bottom surface of the passivation region), the measured thickness D' clearly illustrated in fig. 40 is not close to the actual thickness D of the passivation layer. However, errors introduced by the refractive index of the passivation layer may be removed by applying a correction calculation to the measured thickness of the passivation layer. A first correction calculation is shown in fig. 40, where 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. 40, where the actual thickness of the passivation layer (D) is equal to the measured thickness of the passivation layer (D') multiplied by the refractive index of the passivation layer plus an offset value. The second correction calculation is more general and takes into account the fact that: the refractive index of the passivation layer varies depending on the wavelength and the objective lens spherical aberration can affect the z-position measurement when imaged through the transparent medium. Thus, the actual thickness of the passivation layer can be calculated using the z-position of the focal plane of the captured image focused on the light reflected from the bottom surface of the passivation layer, as long as the proper calibration procedure is followed.

Once the correction equation is applied to the measured thickness of the passivation layer, the true thickness of the passivation layer may be obtained. Referring again to fig. 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.

Fig. 41 is a diagram illustrating peak mode operation using images captured at various distances when the photoresist opening is within the field of view of an optical microscope. The captured image illustrated in fig. 41 is obtained from a sample similar to the sample structure shown in fig. 32. The structure is a metal-plated trench structure. The top view of the sample shows the area of the photoresist openings (metallization) in the x-y plane. The PR openings also have a depth in the z-direction (higher than the metallization) of a certain depth. The top view in fig. 41 below shows images captured at various distances. At distance 1, the optical microscope is not focused on the top surface of the photoresist region or the top surface of the metallization. At distance 2, the optical microscope is focused on the top surface of the metallization, but not on the top surface of the photoresist region. This results in increased characteristic values (intensity/contrast/fringe contrast) in pixels receiving light reflected from the top surface of the metallization as compared to pixels receiving light reflected from other surfaces (top surfaces of the photoresist regions) that are out of focus. At distance 3, the optical microscope is not focused on the top surface of the photoresist region or the top surface of the metallization. Thus, at distance 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, an increase in the maximum characteristic value (intensity/contrast/fringe contrast) was measured due to the difference in the refractive index of air and the refractive index of the photoresist region. Fig. 11, 40 and the accompanying text describe this phenomenon in more detail. At distance 6, the optical microscope is focused on the top surface of the photoresist region, but not on the top surface of the metallization. This results in increased characteristic values (intensity/contrast/fringe contrast) in pixels that receive light reflected from the top surface of the photoresist region compared to pixels that receive light reflected from other surfaces that are out of focus (top surfaces of the metallization). Once the maximum characteristic value from each captured image is determined, the results may be utilized to determine at which distances each surface of the wafer is positioned.

Fig. 42 is a graph illustrating three-dimensional information derived from the peak mode operation illustrated in fig. 41. As discussed with respect to fig. 41, the maximum characteristic values of the images captured at

distances

1, 3, and 5 have a maximum characteristic value that is lower than the maximum characteristic values of the images captured at

distances

2, 4, and 6. The curves of maximum characteristic values at various z-distances may contain noise due to environmental effects, such as vibration. To minimize this noise, standard smoothing methods, such as Gaussian filtering (Gaussian filtering) with a certain kernel size, may be applied prior to further data analysis.

One method of comparing the maximum characteristic values is performed 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 at which each "peak" exists. The maximum characteristic value at each distance at which a peak is found is then compared to determine the distance measured to the maximum characteristic value. In the case shown in fig. 42, a peak will be found at distance 2, which serves as an indication that the surface of the sample is located at distance 2.

Another method of comparing the maximum characteristic values is performed by comparing each maximum characteristic value with a preset threshold value. The threshold value may be calculated based on the sample material, the distance, and the specification of the optical microscope. Alternatively, the threshold may be determined by empirical testing prior to automated processing. In either case, the maximum characteristic value of each captured image is compared to a threshold value. If the maximum characteristic value is greater than the threshold, then it is determined that the maximum characteristic value indicates the presence of a surface of the sample. If the maximum characteristic value is not greater than the threshold, then it is determined that the maximum characteristic value is not indicative of the surface of the sample.

Alternative uses of the peak mode method described above, the range mode method described in fig. 13, and related text may be used to determine the z-position of different surfaces of the sample.

Fig. 43 is a diagram of a captured image focused on the top surface of a photoresist layer in a trench structure, including the profiles of a first analysis area a and a second analysis area B. As discussed above, the entire field of view of each captured image may be used to generate three-dimensional information. However, it is advantageous to have the option of generating three-dimensional information using only a selectable part of the field of view (region a or region B). In one example, the user selects the area using a mouse or touch screen device in communication with the computer that processes the captured image. Once selected, a different threshold may be applied to each region to more effectively pick out a particular surface peak as shown in fig. 42. This case is illustrated in fig. 43. When it is desired to obtain three-dimensional information about the top surface of the metallization, a selectable portion of the field of view (region a) is set to encompass multiple regions of the metallization because the property values associated with the metal surface are typically greater than the property values associated with the photoresist, so a high threshold can be applied to region a to filter out the property values associated with the photoresist to improve detection of metal surface peaks. Alternatively, when it is desired to acquire three-dimensional information about the top surface of the photoresist region, a selectable portion of the field of view (region B) is set as a cell positioned in the center of the image. The characteristic values associated with photoresist surfaces are typically relatively weak compared to the characteristic values associated with metal surfaces. The quality of the original signal used to determine the characteristic value calculation is optimal around the center of the field of view enclosed within region B. By setting an appropriate threshold for region B, weak property peaks of the photoresist surface can be more effectively detected. The user can set and adjust the area a and area B and the thresholds used within each area via a graphical interface that displays an overhead image of the sample and save them in the recipe for automated measurements.

Fig. 44 is a three-dimensional view of a bump over a passivation structure. Fig. 45 is a top view of a bump over the passivation structure shown in fig. 44, including the contours of the first analysis region a and the second analysis region B. Region a may be set such that region a will always include the vertices of the metal bumps during automated sequence measurements. The region B does not enclose any portion of the metal bump and only encloses a portion of the passivation layer. Analyzing only area a of all captured images provides pixel filtering such that most of the pixels analyzed include information about the metal bumps. Analyzing region B of all captured images provides pixel filtering such that all pixels analyzed contain information about the passivation layer. The application of the user selectable analysis region provides pixel filtering based on location rather than pixel value. For example, when the position of the top surface of the passivation layer is required, region B may be applied and all effects caused by the metal bump may be eliminated immediately from the analysis. In another example, when the location of the vertices of the metal bumps is desired, region a may be applied and all effects caused by a large passivation layer region may be eliminated immediately from the analysis.

In some instances, it is also useful to fix the spatial relationship between region a and region B. When measuring metal bumps of known size (such as illustrated in fig. 44 and 45), it is useful to fix the spatial relationship between region a and region B to provide consistent measurements, since region a is always used to measure the three-dimensional information of the metal bump and region B is always used to measure the three-dimensional information of the passivation layer. Furthermore, when region a and region B have a fixed spatial relationship, adjustment of one region automatically causes adjustment of the other region. This scenario is illustrated in fig. 46. Fig. 46 is a top view illustrating the adjustment analysis area a and the analysis area B when the entire bump is not positioned in the original analysis area a. This may occur for a variety of reasons, such as imprecise placement of the sample by the handler or process variations during sample manufacture. Regardless of the reason, the area a needs to be adjusted to be properly centered on the top of the metal bump. Region B is also adjusted to ensure that region B does not include any portion of the metal bump. When the spatial relationship between region a and region B is fixed, then the adjustment of region a automatically causes a realignment of region B.

Fig. 47 is a cross-sectional view of a bump over the passivation structure illustrated in fig. 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 not substantially greater than the distance between the predetermined steps of the optical microscope (i.e., the passivation layer is relatively thin), the z-position of the top surface of the passivation layer may not be easily detected and measured. The difficulty arises due to the small percentage of light reflected from the top surface of the passivation layer compared to the large percentage of light reflected from the bottom surface of the passivation layer. In other words, the characteristic value peak associated with the top surface of the passivation layer is sufficiently weak compared to the characteristic value peak associated with the bottom surface of the passivation layer. When the captured image at the predetermined step focused on the high intensity reflection from the bottom surface of the passivation layer is less than several predetermined steps away from the captured image at the predetermined step focused on the low intensity reflection from the top surface of the passivation layer, the reflection received from the bottom surface of the passivation layer cannot be distinguished from the reflection received from the top surface of the passivation layer. This problem can be solved by the operation of different methods.

In a first approach, a predetermined total number of steps across a scan may be increased in order to provide additional resolution across the entire scan. For example, the predetermined number of steps across the same scan distance may be doubled, which would result in doubling the z-resolution of the scan. This approach will also result in doubling the amount of images captured during a single scan. The resolution of the scan may be increased until the characteristic peak measured from the top surface reflection can be distinguished from the characteristic peak measured from the bottom surface reflection. Fig. 49 illustrates a scenario in which sufficient resolution is provided in the scan to distinguish reflections from the top and bottom surfaces of the passivation layer.

In the second method, the predetermined total number of steps is also increased, however, only a portion of the steps are used to capture the image and the remainder are skipped.

In a third method, the distance between the predetermined steps may be altered such that the distance between the steps is smaller in the vicinity of 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 less resolution outside the vicinity of the passivation layer. This method does not require the addition of an additional predetermined step to the scan, but rather redistributes the predetermined steps in a non-linear fashion to provide additional resolution where needed at the expense of lower resolution without requiring high resolution.

For additional description of how To improve scan resolution, see U.S. patent application entitled "three-dimensional Microscope Including Insertable Components To Provide Multiple Imaging and Measurement Capabilities" (3D Microscope incorporating instruments Imaging and Measurement Capabilities) "entitled" U.S. patent application serial No. 13/333,938 by James licensing, et al, filed 2011, 12/21 (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 may be determined.

The height of the apex of the metal bump relative to the top surface of the passivation layer ("bump height over passivation layer") is also a measure of interest. The bump height 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 vertex of the bump may 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 for each x-y pixel position across 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-location and the z-location containing the largest characteristic value is stored in the array. The result of performing this process 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 additional description of how to generate three-dimensional information, see U.S. patent application entitled "three-dimensional Optical Microscope (3-D Optical Microscope)" entitled U.S. patent application serial No. 12/699,824 and U.S. patent No. 8,174,762 (the subject matter of which is incorporated herein by reference) filed on 3.2.2010 by James, sanctual women, et al.

In a second method, the z-position of the apex of the bump is determined by generating a fitted three-dimensional model of the surface of the bump and then calculating the peak of the surface of the bump using the three-dimensional model. In one example, this may 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 the three-dimensional model. A three-dimensional model may be generated using a second order polynomial function fitted to the data. Once the three-dimensional 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 vertex of the bump is determined, the bump height above the passivation layer may be calculated by subtracting the z-position of the top surface of the passivation layer from the z-position of the vertex of the bump.

Fig. 48 is a diagram illustrating peak mode operation using images captured at various distances when only the passivation layer is within region B of the field of view of the optical microscope. By analyzing only the pixels within region B (shown in fig. 45), all pixel information about the metal bumps is excluded. Therefore, the three-dimensional information generated by analyzing the pixels within the region B will be affected only by the passivation layer present in the region B. The captured image illustrated in fig. 48 is obtained from a sample similar to the sample structure shown in fig. 44. The structure is a metal bump over a 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 the pixels within the region B are selected, the metal bumps are not visible in the top view. The top view in fig. 48 below shows the images captured at each distance. 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 in the refractive index of air and the refractive index of the passivation layer, an increase of a maximum specific value (intensity/contrast/fringe contrast) was measured. Fig. 11, 40 and the accompanying text describe this phenomenon in more detail. At distance 3, the optical microscope is not focused on the top surface of the passivation layer or the bottom surface of the passivation layer. Thus, at distance 3, the maximum characteristic value will be substantially lower than the characteristic value measured at distance 2. At distance 4, the optical microscope is focused on the top surface of the passivation layer, which results in an increased characteristic value (intensity/contrast/fringe contrast) in pixels receiving light reflected from the top surface of the passivation layer compared to pixels receiving light reflected from other surfaces that are out of focus. At

distances

5, 6, and 7, the optical microscope is not focused on the top surface of the passivation layer or the bottom surface of the passivation layer. Thus, at

distances

5, 6 and 7, the maximum characteristic value will be substantially lower than the characteristic values measured at

distances

2 and 4. Once the maximum characteristic value from each captured image is determined, the results may be utilized to determine at which distances each surface of the sample is positioned.

Fig. 49 is a graph illustrating three-dimensional information resulting from the peak mode operation of fig. 48. Due to the pixel filtering provided by analyzing only the pixels within region B of all captured images, peak mode operation provides only an indication of the surface of the passivation layer at 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 locations. The lowest of the two indicated z-position locations is the wrong "ghost surface" where the 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 erroneous measurements due to light reflections from metal bumps positioned on the same sample.

Although certain specific embodiments are described above for instructional purposes, the teachings of this patent document have general applicability and are not limited to the specific embodiments described above. Accordingly, various modifications, adaptations, and combinations of the various features of the described embodiments can be practiced without departing from the scope of the invention as set forth in the claims.

Claims

1. A method of generating three-dimensional information of a sample using an optical microscope, the method comprising:

altering the distance between the sample and the objective lens of the optical microscope in predetermined steps;

capturing an image at each predetermined step, wherein a first surface of the sample and a second surface of the sample are within a field of view of each of the captured images;

determining a characteristic value for each pixel in each captured image, wherein the characteristic value for each pixel is selected from the group consisting of intensity, contrast, and streak contrast;

determining, for each captured image, a maximum characteristic value across a first portion of pixels in the captured image;

comparing the maximum characteristic value for each captured image to determine whether a surface of the sample is present at each predetermined step;

determining a z-position of a vertex of a bump of the sample;

determining a z-position of a first surface of the sample based on a maximum characteristic value of each captured image; and

determining a first distance between the vertex of the bump and the first surface based on a z-position of the vertex and a z-position of the first surface, wherein the bump is a metal bump.

2. The method of claim 1, wherein the optical microscope includes 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 a memory device adapted to store each captured image, and wherein the optical microscope is selected from the group consisting of a confocal microscope, a structured illumination microscope, and an interferometer.

3. The method of claim 1, wherein determining the z-position of the vertex of the bump of the sample comprises:

determining a maximum characteristic value for each x-y pixel location within a second portion of x-y pixel locations across all captured images, wherein the second portion of x-y pixel locations includes at least some of the x-y pixel locations included in each captured image;

determining a subset of the captured images, wherein only captured images that include x-y pixel location maximum characteristic values are included in the subset; and

determining that, among all captured images within the subset of captured images, a first captured image is focused on a highest z-position compared to all other captured images within the subset of captured images, wherein the highest z-position is the z-position of the vertex of the bump of the sample.

4. The method of claim 1, wherein the first portion of pixels includes all pixels included in the captured image.

5. The method of claim 1, wherein the first portion of pixels includes less than all pixels included in the captured image.

6. The method of claim 3, wherein the second portion of pixels includes all pixels included in the captured image.

7. The method of claim 3, wherein the second portion of pixels includes less than all pixels included in the captured image.

8. The method of claim 1, wherein the first portion of pixels do not receive light reflected from the metal bumps.

9. The method of claim 3, wherein the second portion of pixels receive light reflected from the vertices of the metal bumps.

10. The method of claim 3, wherein a spatial relationship between the first portion of pixels and the second portion of pixels is fixed.

11. The method of claim 3, wherein the second portion of pixels are continuous and centered at the vertex of the bump.

12. The method of claim 1, wherein the first surface is a top surface of a passivation layer.

13. A method of generating three-dimensional information of a sample using an optical microscope, the method comprising:

determining, for each captured image, a pixel count having characteristic values within a first range across a first portion of pixels, wherein all pixels not having characteristic values within the first range are not included in the pixel count;

determining whether a surface of the sample is present at each predetermined step based on the pixel count of each captured image;

determining a z-position of a vertex of a bump of the sample;

determining a z-position of a first surface of the sample based on the pixel count of each captured image; and

14. The method of claim 13, wherein the optical microscope includes 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 a memory device adapted to store each captured image, and wherein the optical microscope is selected from the group consisting of a confocal microscope, a structured illumination microscope, and an interferometer.

15. The method of claim 13, wherein determining the z-position of the vertex of the bump of the sample further comprises:

16. The method of claim 13, wherein the first portion of pixels includes all pixels included in the captured image.

17. The method of claim 13, wherein the first portion of pixels includes less than all pixels included in the captured image.

18. The method of claim 15, wherein the second portion of pixels includes all pixels included in the captured image.

19. The method of claim 15, wherein a spatial relationship between the first portion of pixels and the second portion of pixels is fixed.

20. The method of claim 15, wherein the second portion of pixels are continuous and centered at the vertex of the bump.

21. A method of generating three-dimensional information of a sample using an optical microscope, the method comprising:

determining a z-position of a vertex of a bump of the sample;

determining a z-position of a first surface of the sample based on the characteristic value for each pixel in each captured image; and

22. The method of claim 21, wherein the determination of the z-position of the vertex comprises:

identifying a plurality of x, y, z pixel locations across all captured images, wherein the plurality of x, y, z pixel locations are associated with a top surface of the bump;

applying a best fit algorithm to generate a continuous 3-D estimate of the top surface of the bump; and

determining a maximum height of the successive 3-D estimates.