17825087. COMPUTING DEVICES FOR PREDICTING ELECTRICAL TESTS, ELECTRICAL TEST PREDICTION APPARATUSES HAVING THE SAME, AND OPERATING METHODS THEREOF simplified abstract (Samsung Electronics Co., Ltd.)

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COMPUTING DEVICES FOR PREDICTING ELECTRICAL TESTS, ELECTRICAL TEST PREDICTION APPARATUSES HAVING THE SAME, AND OPERATING METHODS THEREOF

Organization Name

Samsung Electronics Co., Ltd.

Inventor(s)

Seyoung Park of Hwaseong-si (KR)

Gwangnae Gil of Yongin-s (KR)

Sola Woo of Suwon-si (KR)

Jonghyun Lee of Hwaseong-si (KR)

COMPUTING DEVICES FOR PREDICTING ELECTRICAL TESTS, ELECTRICAL TEST PREDICTION APPARATUSES HAVING THE SAME, AND OPERATING METHODS THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 17825087 titled 'COMPUTING DEVICES FOR PREDICTING ELECTRICAL TESTS, ELECTRICAL TEST PREDICTION APPARATUSES HAVING THE SAME, AND OPERATING METHODS THEREOF

Simplified Explanation

Abstract

A method is disclosed for operating an electrical test prediction apparatus. The method involves determining a relationship between first electrical test (ET) data and electrical die sorting (EDS) data. The first ET data corresponds to a subset of semiconductor chips on a wafer, while the EDS data is obtained by measuring the state of each chip on the wafer using a testing device. The method further includes predicting second ET data for a region of the wafer other than the subset by performing machine learning on the determined relationship.

Bullet Points

  • The patent application describes a method for predicting electrical test data for a region of a wafer using machine learning.
  • The method involves determining a relationship between first electrical test data and electrical die sorting data.
  • The first electrical test data corresponds to a subset of semiconductor chips on the wafer, while the electrical die sorting data is obtained by measuring the state of each chip on the wafer.
  • By performing machine learning on the determined relationship, the method predicts second electrical test data for a region of the wafer other than the subset.

Potential Applications

  • Semiconductor manufacturing: The method can be applied in the semiconductor industry to predict electrical test data for regions of a wafer that have not been directly tested. This can help in optimizing manufacturing processes and improving yield.
  • Quality control: The predicted electrical test data can be used for quality control purposes, allowing manufacturers to identify and address potential issues in the production of semiconductor chips.
  • Process optimization: By analyzing the relationship between electrical test data and electrical die sorting data, the method can provide insights into the manufacturing process and help optimize it for improved efficiency and performance.

Problems Solved

  • Limited testing coverage: Traditional testing methods may only cover a subset of semiconductor chips on a wafer, leaving other regions untested. The method described in the patent application solves this problem by predicting electrical test data for the untested regions.
  • Time and cost efficiency: Predicting electrical test data using machine learning can save time and cost compared to physically testing each chip on the wafer individually.
  • Data analysis and interpretation: The method addresses the challenge of analyzing and interpreting the relationship between electrical test data and electrical die sorting data, allowing for accurate predictions.

Benefits

  • Improved yield: By predicting electrical test data for untested regions, manufacturers can identify and address potential issues early on, leading to improved yield and reduced waste.
  • Cost savings: Predicting electrical test data using machine learning can save costs associated with physical testing equipment and resources.
  • Process optimization: The method provides insights into the relationship between electrical test data and electrical die sorting data, enabling manufacturers to optimize their processes for better efficiency and performance.


Original Abstract Submitted

A method of operating an electrical test prediction apparatus includes determining a relationship between first electrical test (ET) data, corresponding to at least one shot region comprising a subset of a plurality of semiconductor chips of a wafer, and electrical die sorting (EDS) data, obtained by measuring a state of each chip on the wafer by a testing device, and predicting second ET data, corresponding to an region of the wafer other than the at least one shot region by performing machine learning on the relationship.

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