Applied Materials, Inc.

Applied Materials Israel Limited

Vicky Svidenko

Youval Nehmadi

Rinat Shimshi

Alexander T Schwarm

Sundar Jawaharlal

Ariel Ben-Porath

Ofer Bokobza

Erez Ravid

Gilad Almogy

Dapeng Wang

Danny Cam Toan Lu

Jacob Joseph Orbon, Jr

Tzay-Fa (Jeff) Su

Michel Ranjit Frei

Lior Levin

Chuck Luu

Sundar Jawaharlah

Rinat Shishi

Jacob J Orbon, Jr

Michel Marriott

Embodiments of the present invention provide methods and apparatuses for determining factors for design consideration in yield analysis of semiconductor fabrication. In one embodiment, a computer-implemented method for determining factors for design consideration in yield analysis of semiconductor fabrication includes obtaining a geometric characteristic of a defect on a chip and obtaining design data of the chip, where the design data is associated with the defect. The method further includes determining a criticality factor of the defect based on the geometric characteristic and the design data, and outputting the criticality factor.

Method and apparatus for determining factors for design consideration in yield analysis

In one embodiment, an inline defect analysis method includes receiving geometric characteristics of individual defects and design data corresponding to the individual defects, determining which of the individual defects are likely to be nuisance defects using the geometric characteristics and the corresponding design data, and refraining from sampling the defects that are likely to be nuisance defects.

Inline defect analysis for sampling and SPC

Embodiments of the present invention generally relate to a module that can test and analyze various regions of a solar cell device in an automated or manual fashion after one or more steps have been completed in the solar cell formation process. The module used to perform the automated testing and analysis processes can also be adapted to test a partially formed solar cell at various stages of the solar cell formation process within an automated solar cell production line. The automated solar cell production line is generally an arrangement of automated processing modules and automation equipment that is used to form solar cell devices.

Solar parametric testing module and processes

In one embodiment, a method for predicting yield during the design stage includes receiving defectivity data identifying defects associated with previous wafer designs, and dividing the defects into systematic defects and random defects. For each design layout of a new wafer design, yield is predicted separately for the systematic defects and the random defects. A combined yield is then calculated based on the yield predicted for the systematic defects and the yield predicted for the random defects.

Stage yield prediction

One embodiment of the present invention sets forth a computer-implemented method for tuning laser scribe parameters during the fabrication of a solar module. The method includes analyzing the visual appearance of a laser scribe to extract various morphological parameters related to the quality of a laser scribe process used to produce the scribe. Based on the morphological parameters, the laser scribe parameters may be modified in-situ to achieve settings that are optimal for performing laser scribing in each layer of the solar module. As a result, laser scribe process cycle time may be minimized while providing better indication of the laser scribe process stability and quality relative to the prior art approaches.

Scribe process monitoring methodology

In one embodiment, a method for predicting yield includes calculating a criticality factor (CF) for each of a plurality of defects detected in an inspection process step of a wafer, and determining a yield-loss contribution of the inspection process step to the final yield based on CFs of the plurality of defects and the yield model built for a relevant design. The yield-loss contribution of the inspection process step is then used to predict the final yield for the wafer.

Dynamic inline yield analysis and prediction of a defect limited yield using inline inspection defects

Grain size variations within a copper film are quantified by analyzing an SEM image of a portion of the copper film, determining an approximate total length of grain boundaries within the SEM image, and calculating a grain boundary density based on the approximate total length of the grain boundaries and the area of the copper film represented in the SEM image. The calculated grain boundary density allows for correlating plating and anneal process parameters, as well as electrical and reliability performance.

System and method for performing post-plating morphological Cu grain boundary analysis

Methods and apparatus for categorizing defects on workpieces, such as semiconductor wafers and masks used in lithographically writing patterns into such wafers are provided. For some embodiments, by analyzing the layout in the neighborhood of the defect, and matching it to similar defected neighborhoods in different locations across the die, defects may be categorized by common structures in which they occur.

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Sunnyvale, CA, United States of America

Vicky Svidenko