# US 2017/0284944 A1 · System and Method for Wafer Inspection with a Noise Boundary Threshold > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0284944 A1 - Document ID: US20170284944A1 - Status: Published application - Kind: A1 - Application number: 15/388,577 - Filed: 2016-12-22 - Published: 2017-10-05 - Earliest claimed: 2016-04-04 - Assignees: KLA-Tencor Corporation - Applicants: KLA-Tencor Corporation - Inventors: Xuguang Jiang; Yong Zhang - Web page: https://patentgrep.com/patent/US20170284944A1 ## CPC - G01N 21/9501: {Semiconductor wafers (manufacturing processes per se of semiconductor devices implementing a measuring step H10P74/20)} - G06T 5/40: using histogram techniques - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/0004: {Industrial image inspection} - G06T 7/136: involving thresholding - H10P 74/00: Testing or measuring during manufacture or treatment of wafers, substrates or devices - H10P 74/203: {Structural properties, e.g. testing or measuring thicknesses, line widths, warpage, bond strengths or physical defects} - H10P 74/23: {characterised by multiple measurements, corrections, marking or sorting processes} - G01N 21/956: Inspecting patterns on the surface of objects {(contactless testing of electronic circuits G01R31/308; testing currency G07D; manufacturing processes per se of semiconductor devices implementing a measuring step H10P74/20)} - G01N 2021/8883: {involving the calculation of gauges, generating models} - G01N 2021/8887: {based on image processing techniques} - G06T 2207/10061: from scanning electron microscope - G06T 2207/30148: Semiconductor; IC; Wafer - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - H10P: GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10 ## Abstract A method includes receiving one or more images of three or more die of a wafer, determining a median intensity value of a set of pixel intensity values acquired from a same location on each of the three or more die, determining a difference intensity value for the set of pixel intensity values by comparing the median intensity value of the set of pixel intensity values to each pixel intensity value, grouping the pixel intensity values into an intensity bin based on the median intensity value of the set of pixel intensity values, generating an initial noise boundary based on a selected difference intensity value in the intensity bin, generating a final noise boundary by adjusting the initial noise boundary, generating a detection boundary by applying a threshold to the final noise boundary, and classifying one or more pixel intensity values outside the detection boundary as a defect. ## Claims ### Claim 1 A wafer inspection system, comprising: an inspection sub-system; a stage configured to secure a wafer; and a controller communicatively coupled to the inspection sub-system, wherein the controller includes one or more processors configured to execute a set of program instructions stored in memory, wherein the program instructions are configured to cause the one or more processors to: receive one or more images of three or more die of the wafer from the inspection sub-system; determine a median intensity value of a set of pixel intensity values acquired from a same location on each of the three or more die; determine a difference intensity value for the set of pixel intensity values by comparing the median intensity value of the set of pixel intensity values to each pixel intensity value; group each of the pixel intensity values into an intensity bin based on the median intensity value of the set of pixel intensity values; generate an initial noise boundary based on a selected difference intensity value in the intensity bin; generate a final noise boundary by adjusting the initial noise boundary; generate a detection boundary by applying a threshold to the final noise boundary; and classify one or more pixel intensity values outside the detection boundary as a defect. ### Claim 2 (depends on 1) The system in claim 1, wherein the selected difference intensity value includes: a largest positive difference intensity value in the intensity bin, a smallest negative difference intensity value in the intensity bin, or a selectable difference intensity value below the largest positive difference intensity value or above the smallest negative difference intensity value. ### Claim 3 (depends on 1) The system in claim 1, wherein the program instructions are configured to cause the one or more processors to: select an adjacent minimum intensity bin value; group one or more intensity bins based on the adjacent minimum intensity bin value; determine a minimum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; generate a first adjusted noise boundary from the initial noise boundary by reducing the intensity value of the one or more intensity bins to the minimum intensity value; select an adjacent maximum intensity bin value; group one or more intensity bins based on the adjacent maximum intensity bin value; determine a maximum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; and generate a second adjusted noise boundary from the first adjusted noise boundary by increasing the intensity value of the one or more intensity bins to the maximum intensity value. ### Claim 4 (depends on 1) The system in claim 1, wherein the program instructions are configured to cause the one or more processors to: select an adjacent maximum intensity bin value; group one or more intensity bins based on the adjacent maximum intensity bin value; determine a maximum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; generate a first adjusted noise boundary from the initial noise boundary by increasing the intensity value of the one or more intensity bins to the maximum intensity value; select an adjacent minimum intensity bin value; group one or more intensity bins based on the adjacent minimum intensity bin value; determine a minimum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; generate a second adjusted noise boundary from the first adjusted noise boundary by reducing the intensity value of the one or more intensity bins to the minimum intensity value. ### Claim 5 (depends on 1) The system in claim 1, wherein the program instructions are further configured to cause the one or more processors to: select an adjacent mean intensity bin value; group one or more intensity bins based on the adjacent mean intensity bin value; determine a mean intensity value for the one or more intensity bins by comparing the second adjusted noise boundary value of each intensity bin; and generate a third adjusted noise boundary from the second adjusted noise boundary by setting the intensity value of the one or more intensity bins to the mean intensity value. ### Claim 6 (depends on 1) The system in claim 1, wherein the program instructions are further configured to cause the one or more processors to: generate the final noise boundary by interpolating the third adjusted noise boundary. ### Claim 7 (depends on 1) The system in claim 1, wherein the program instructions are further configured to cause the one or more processors to: determine a bright noise boundary for each of the three or more dies. ### Claim 8 (depends on 1) The system in claim 1, wherein the program instructions are further configured to cause the one or more processors to: determine a dark noise boundary for each of the three or more dies. ### Claim 9 (depends on 1) The system in claim 1, wherein the inspection sub-system comprises one or more imaging channels, wherein the one or more imaging channels acquire the one or more images of the three or more die of the wafer. ### Claim 10 (depends on 1) The system in claim 1, wherein the program instructions are further configured to cause the one or more processors to: receive the one or more images of the one or more die of the wafer acquired by the one or more channels of the inspection sub-system; generate one or more reconstructed images by combining the one or more images acquired by the one or more channels of the inspection sub-system; and classify one or more pixels outside the detection boundary in the one or more reconstructed images as a defect. ### Claim 11 A method, comprising: receiving one or more images of three or more die of a wafer from an inspection sub-system; determining a median intensity value of a set of pixel intensity values acquired from a same location on each of the three or more die; determining a difference intensity value for the set of pixel intensity values by comparing the median intensity value of the set of pixel intensity values to each pixel intensity value; grouping the pixel intensity values into an intensity bin based on the median intensity value of the set of pixel intensity values; generating an initial noise boundary based on a selected difference intensity value in the intensity bin; generating a final noise boundary by adjusting the initial noise boundary; generating a detection boundary by applying a threshold to the final noise boundary; and classifying one or more pixel intensity values outside the detection boundary as a defect. ### Claim 12 (depends on 11) The method in claim 11, wherein the selected difference intensity value includes: a largest positive difference intensity value in the intensity bin, a smallest negative difference intensity value in the intensity bin, or a selectable difference intensity value below the largest positive difference intensity value or above the smallest negative difference intensity value. ### Claim 13 (depends on 11) The method in claim 11, further comprising: selecting an adjacent minimum intensity bin value; grouping one or more intensity bins based on the adjacent minimum intensity bin value; determining a minimum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; generating a first adjusted noise boundary from the initial noise boundary by reducing the intensity value of the one or more intensity bins to the minimum intensity value; selecting an adjacent maximum intensity bin value; grouping one or more intensity bins based on the adjacent maximum intensity bin value; determining a maximum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; and generating a second adjusted noise boundary from the first adjusted noise boundary by increasing the intensity value of the one or more intensity bins to the maximum intensity value. ### Claim 14 (depends on 11) The method in claim 11, further comprising: selecting an adjacent maximum intensity bin value; grouping one or more intensity bins based on the adjacent maximum intensity bin value; determining a maximum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; generating a first adjusted noise boundary from the initial noise boundary by increasing the intensity value of the one or more intensity bins to the maximum intensity value; selecting an adjacent minimum intensity bin value; grouping one or more intensity bins based on the adjacent minimum intensity bin value; determining a minimum intensity value for the one or more intensity bins by comparing the intensity value of each intensity bin; generating a second adjusted noise boundary from the first adjusted noise boundary by reducing the intensity value of the one or more intensity bins to the minimum intensity value. ### Claim 15 (depends on 11) The method in claim 11, further comprising: selecting an adjacent mean intensity bin value; grouping one or more intensity bins based on the adjacent mean intensity bin value; determining a mean intensity value for the one or more intensity bins by comparing the second adjusted noise boundary value of each intensity bin; and generating a third adjusted noise boundary from the second adjusted noise boundary by setting the intensity value of the one or more intensity bins to the mean intensity value. ### Claim 16 (depends on 11) The method in claim 11, further comprising: generating the final noise boundary by interpolating the third adjusted noise boundary. ### Claim 17 (depends on 11) The method in claim 11, further comprising: determining a bright noise boundary for each of the three or more dies. ### Claim 18 (depends on 11) The method in claim 11, further comprising: determining a dark noise boundary for each of the three or more dies. ### Claim 19 (depends on 11) The method in claim 11, wherein the inspection sub-system comprises one or more imaging channels, wherein the one or more imaging channels acquire the one or more images of the three or more die of the wafer. ### Claim 20 (depends on 11) The method in claim 11, further comprising: receiving the one or more images of the one or more die of the wafer acquired by the one or more channels of the inspection sub-system; generating one or more reconstructed images by combining the one or more images acquired by the one or more channels of the inspection sub-system; and classifying one or more pixels outside the detection boundary in the one or more reconstructed images as a defect.