Abstract:
Systeme und Verfahren zur Detektion von Defekten auf einer Probe auf Grundlage von Strukturinformation werden bereitgestellt. Ein System beinhaltet ein oder mehrere Computersubsysteme, die dazu ausgebildet sind, die von einem Detektor eines Inspektionssubsystems in einem Array-Gebiet auf einer Probe erzeugte Ausgabe in zumindest erste und zweite Segmente der Ausgabe zu trennen, auf Grundlage eines Merkmals/von Merkmalen einer Struktur/von Strukturen in dem Array-Gebiet, so dass die Ausgabe in unterschiedlichen Segmenten an unterschiedlichen Positionen in dem Array-Gebiet erzeugt worden ist, in denen die Struktur(en), die unterschiedliche Werte des Merkmals/der Merkmale haben, ausgebildet sind. Das Computersubsystem/die Computersubsysteme sind auch dazu ausgebildet, Defekte auf der Probe zu detektieren, indem sie eine oder mehrere Defektdetektionsmethoden auf die Ausgabe anwenden, abhängig davon, ob sich die Ausgabe in dem ersten Segment oder dem zweiten Segment befindet.
Abstract:
Systems and methods for detecting defects on a specimen based on structural information are provided. One system includes one or more computer subsystems configured for separating the output generated by a detector of an inspection subsystem in an array area on a specimen into at least first and second segments of the output based on characteristic(s) of structure(s) in the array area such that the output in different segments has been generated in different locations in the array area in which the structure(s) having different values of the characteristic(s) are formed. The computer subsystem(s) are also configured for detecting defects on the specimen by applying one or more defect detection methods to the output based on whether the output is in the first segment or the second segment.
Abstract:
INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property 1#111011110111010101111101 11010011111111011110 HE 10111011111011110111111 Organization International Bureau (10) International Publication Number (43) International Publication Date .....0\"\" WO 2018/089459 Al 17 May 2018 (17.05.2018) WIPO I PCT (51) International Patent Classification: Santosh; 5540 Cooney Place, San Jose, California 95123 GO1N 21/95 (2006.01) GO1N 21/88 (2006.01) (US). SHIFRIN, Eugene; 463 Liquidambar Way, Sunny- vale, California 94086 (US). LEE, Hucheng; 1159 Kent- (21) International Application Number: wood Avenue, Cupertino, California 95014 (US). MUR- PCT/US2017/060589 RAY, Benjamin; 17261 NW LaPaloma Lane, Beaver- (22) International Filing Date: ton, California 97006 (US). MATHEW, Ashok; 34782 Si- 08 November 2017 (08.11.2017) ward Drive, Fremont, California 94539 (US). BHASKAR, (25) Filing Language: English Chetana; 1061 Queensbridge Court, San Jose, California 95120 (US). GAO, Lisheng; 21164 Toll Gate Road, Sarato- (26) Publication Language: English ga, California 95070 (US). (30) Priority Data: (74) Agent: MCANDREWS, Kevin et al.; KLA-Tencor Corp., 62/420,409 10 November 2016 (10.11.2016) US Legal Department, One Technology Drive, Milpitas, Cali- 62/443,810 09 January 2017 (09.01.2017) US fornia 95035 (US). 62/455,948 07 February 2017 (07.02.2017) US (81) Designated States (unless otherwise indicated, for every 15/804,980 06 November 2017 (06.11.2017) US kind of national protection available): AE, AG, AL, AM, (71) Applicant: KLA-TENCOR CORPORATION [US/US]; AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, Legal Department, One Technology Drive, Milpitas, Cali- CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, fornia 95035 (US). DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, (72) Inventors: BRAUER, Bjorn; 16698 NW Tucson Street, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, _ Beaverton, Oregon 97006 (US). BHATTACHARYYA, = = (54) Title: HIGH SENSITIVITY REPEATER DEFECT DETECTION = — = Computer subsystem(s) 300 _ _ = = = — = 28 26 24 20 16 18 2 1 ill. '' •• 34 /-10 I A os % 32 = , No = 14 30 = 22 = = Fig. 1 ,-, .. 11 (57) : Systems and methods for detecting defects on a reticle are provided. One system includes computer subsystem(s) that CN include one or more image processing components that acquire images generated by an inspection subsystem for a wafer, a main 7 1. user interface component that provides information generated for the wafer and the reticle to a user and receives instructions from the C:N user, and an interface component that provides an interface between the one or more image processing components and the main user °O interface. Unlike currently used systems, the one or more image processing components are configured for performing repeater defect 0 ---. detection by applying a repeater defect detection algorithm to the images acquired by the one or more image processing components, *1 : and the repeater defect detection algorithm is configured to detect defects on the wafer using a hot threshold and to identify the defects 11 c;;;; ) that are repeater defects. N O [Continued on next page] WO 2018/089459 Al MIDEDIMOMOIDEIREEMOOMMEIMOHNOCHINVOIMIE MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG). Published: — with international search report (Art. 21(3))
Abstract:
Methods and systems for determining a position of output generated by an inspection subsystem in design data space are provided. One method includes selecting one or more alignment targets from a design for a specimen. At least a portion of the one or more alignment targets include built in targets included in the design for a purpose other than alignment of inspection results to design data space. At least the portion of the one or more alignment targets does not include one or more individual device features. One or more images for the alignment target(s) and output generated by the inspection subsystem at the position(s) of the alignment target(s) may then be used to determine design data space positions of other output generated by the inspection subsystem in a variety of ways described herein.
Abstract:
Computer-implemented methods, carrier media, and systems for selecting polarization settings for an inspection system for inspection of a layer of a wafer are provided. One method includes detecting a population of defects on the layer of the wafer using results of each of scans of the wafer performed with different combinations of polarization settings of the inspection system for illumination and collection of light scattered from the wafer. The method also includes identifying a subpopulation of the defects for each of the different combinations, each of which includes the defects that are common to at least two of the different combinations, and determining a characteristic of a measure of signal-to-noise for each of the subpopulations. The method further includes selecting the polarization settings for the illumination and the collection to be used for the inspection corresponding to the subpopulation having the best value for the characteristic.
Abstract:
The present invention includes searching imagery data in order to identify one or more patterned regions on a semiconductor wafer, generating one or more virtual Fourier filter (VFF) working areas, acquiring an initial set of imagery data from the VFF working areas, defining VFF training blocks within the identified patterned regions of the VFF working areas utilizing the initial set of imagery data, wherein each VFF training block is defined to encompass a portion of the identified patterned region displaying a selected repeating pattern, calculating an initial spectrum for each VFF training block utilizing the initial set of imagery data from the VFF training blocks, and generating a VFF for each training block by identifying frequencies of the initial spectrum having maxima in the frequency domain, wherein the VFF is configured to null the magnitude of the initial spectrum at the frequencies identified to display spectral maxima.
Abstract:
A processor-based method for detecting defects in an integrated circuit, by creating an image of at least a portion of the integrated circuit with a sensor, grouping pixels of the image into bins based at least in part on a common characteristic of the pixels that are grouped within a given bin, creating a histogram of the pixels in each of the bins, calculating a mean value of the histogram for each of the bins, comparing the mean value for each of the bins to a threshold value, flagging as defect candidates those bins where the mean value of the bin varies from the threshold value by more than a predetermined amount, and performing signature detection on the bins that are flagged as defect candidates, where the image of the integrated circuit is not directly compared to any other image of an integrated circuit.
Abstract:
Computer-implemented methods, computer-readable media, and systems for selecting one or more parameters for inspection of a wafer are provided.