IL320494A - Creating a dense defect probability map for use in a computational guided inspection machine learning model - Google Patents

Creating a dense defect probability map for use in a computational guided inspection machine learning model

Info

Publication number
IL320494A
IL320494A IL320494A IL32049425A IL320494A IL 320494 A IL320494 A IL 320494A IL 320494 A IL320494 A IL 320494A IL 32049425 A IL32049425 A IL 32049425A IL 320494 A IL320494 A IL 320494A
Authority
IL
Israel
Prior art keywords
computational
creating
machine learning
learning model
inspection machine
Prior art date
Application number
IL320494A
Other languages
Hebrew (he)
Inventor
Fuming Wang
Original Assignee
Asml Netherlands Bv
Fuming Wang
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Asml Netherlands Bv, Fuming Wang filed Critical Asml Netherlands Bv
Publication of IL320494A publication Critical patent/IL320494A/en

Links

Classifications

    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/70—Microphotolithographic exposure; Apparatus therefor
    • G03F7/70483—Information management; Active and passive control; Testing; Wafer monitoring, e.g. pattern monitoring
    • G03F7/70605—Workpiece metrology
    • G03F7/706835—Metrology information management or control
    • G03F7/706839—Modelling, e.g. modelling scattering or solving inverse problems
    • G03F7/706841—Machine learning
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/70—Microphotolithographic exposure; Apparatus therefor
    • G03F7/70483—Information management; Active and passive control; Testing; Wafer monitoring, e.g. pattern monitoring
    • G03F7/70605—Workpiece metrology
    • G03F7/70616—Monitoring the printed patterns
    • G03F7/7065—Defects, e.g. optical inspection of patterned layer for defects
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/70—Microphotolithographic exposure; Apparatus therefor
    • G03F7/70483—Information management; Active and passive control; Testing; Wafer monitoring, e.g. pattern monitoring
    • G03F7/70605—Workpiece metrology
    • G03F7/70681—Metrology strategies
    • G03F7/706833—Sampling plan selection or optimisation, e.g. select or optimise the number, order or locations of measurements taken per die, workpiece, lot or batch
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • H10P74/203—

Landscapes

  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)
  • Manufacturing & Machinery (AREA)
  • Computer Hardware Design (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Power Engineering (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
IL320494A 2022-11-11 2023-10-17 Creating a dense defect probability map for use in a computational guided inspection machine learning model IL320494A (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263383456P 2022-11-11 2022-11-11
PCT/EP2023/078857 WO2024099710A1 (en) 2022-11-11 2023-10-17 Creating a dense defect probability map for use in a computational guided inspection machine learning model

Publications (1)

Publication Number Publication Date
IL320494A true IL320494A (en) 2025-06-01

Family

ID=88506660

Family Applications (1)

Application Number Title Priority Date Filing Date
IL320494A IL320494A (en) 2022-11-11 2023-10-17 Creating a dense defect probability map for use in a computational guided inspection machine learning model

Country Status (7)

Country Link
EP (1) EP4616255A1 (en)
JP (1) JP2025540579A (en)
KR (1) KR20250108661A (en)
CN (1) CN120188113A (en)
IL (1) IL320494A (en)
TW (1) TW202436863A (en)
WO (1) WO2024099710A1 (en)

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9768082B2 (en) * 2009-02-13 2017-09-19 Hermes Microvision Inc. Method and machine for examining wafers
KR102576687B1 (en) * 2016-08-15 2023-09-08 에이에스엠엘 네델란즈 비.브이. Method for enhancing the semiconductor manufacturing yield
US11060981B2 (en) * 2018-03-20 2021-07-13 Applied Materials Israel Ltd. Guided inspection of a semiconductor wafer based on spatial density analysis
US11423529B2 (en) * 2020-02-18 2022-08-23 Applied Materials Isreal Ltd. Determination of defect location for examination of a specimen
JP2022018205A (en) * 2020-07-15 2022-01-27 東京エレクトロン株式会社 Abnormality detection method and abnormality detection device
US11307150B2 (en) * 2020-08-17 2022-04-19 Applied Materials Israel Ltd. Automatic optimization of an examination recipe
EP4244677A1 (en) * 2020-11-13 2023-09-20 ASML Netherlands B.V. Active learning-based defect location identification
WO2022128694A1 (en) * 2020-12-18 2022-06-23 Asml Netherlands B.V. Training machine learning models based on partial datasets for defect location identification

Also Published As

Publication number Publication date
EP4616255A1 (en) 2025-09-17
WO2024099710A1 (en) 2024-05-16
JP2025540579A (en) 2025-12-16
TW202436863A (en) 2024-09-16
KR20250108661A (en) 2025-07-15
CN120188113A (en) 2025-06-20

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