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    USE OF LAYOUT ANALYSIS TO ENABLE EFFICIENT AND EFFECTIVE RANDOM DEFECT INSPECTION USING A VECTOR-MODE E-BEAM INSPECTION MACHINE
    1.
    发明申请
    USE OF LAYOUT ANALYSIS TO ENABLE EFFICIENT AND EFFECTIVE RANDOM DEFECT INSPECTION USING A VECTOR-MODE E-BEAM INSPECTION MACHINE 有权

    公开(公告)号：US20250085234A1

    公开(公告)日：2025-03-13

    申请号：US18829227

    申请日：2024-09-09

    Applicant: PDF Solutions, Inc.

    Inventor： Hans Eisenmann ,  Markus Rauscher ,  Christian Sendner

    IPC: G01N21/95

    Abstract: A vector e-beam machine for random defect inspection is disclosed. Contrary to traditional wisdom, it is shown that through a careful choice of target locations, vector machines can provide high-throughput and high coverage even when scanning for random defects. Additionally, by not wastefully scanning locations that provide no additional fault observability, charge accumulation on the wafer—a major concern in e-beam scanning—is reduced.
    In a preferred embodiment, two approaches are combined: 1) Scan/target only at locations where a random failure can be observed. 2) From the defined list of observable locations, scan/target only the points which have the highest efficiency. Use of these and/or other disclosed techniques enables the scanner to target and evaluate a majority of the total observable defects in a single pass.

    Rational Decision-Making Tool for Semiconductor Processes
    7.
    发明申请
    Rational Decision-Making Tool for Semiconductor Processes 有权

    公开(公告)号：US20220327268A9

    公开(公告)日：2022-10-13

    申请号：US17303666

    申请日：2021-06-04

    Applicant: PDF Solutions, Inc.

    Inventor： Tomonori Honda ,  Lin Lee Cheong ,  Lakshmikar Kuravi ,  Bogdan Cirlin

    IPC: G06F30/33 ,  G06N20/00 ,  G06Q30/02

    Abstract: A robust predictive model. A plurality of different predictive models for a target feature are run, and a comparative analysis provided for each predictive model that meet minimum performance criteria for the target feature. One of the predictive models is selected, either manually or automatically, based on predefined criteria. For semi-automatic selection, a static or dynamic survey is generated for obtaining user preferences for parameters associated with the target feature. The survey results will be used to generate a model that illustrates parameter trade-offs, which will be used to finalize the optimal predictive model for the user.

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