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Optimizing Hybrid Metrology: Rigorous Implementation of Bayesian and Combined Regression.

Mark-Alexander Henn1, Richard M Silver1, John S Villarrubia1

  • 1Engineering Physics Division, National Institute of Standards and Technology, 100 Bureau Drive MS 8212, Gaithersburg, MD, USA 20899-8212.

Journal of Micro/Nanolithography, MEMS, and MOEMS : JM3
|December 19, 2015
PubMed
Summary
This summary is machine-generated.

Hybrid metrology combines multiple measurement techniques for advanced semiconductor analysis. This approach enhances 3-D structure characterization and uncertainty estimation, addressing key industry challenges.

Keywords:
Bayesian data analysiselectromagnetic simulationhybrid metrologysensitivity and uncertainty evaluation

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Area of Science:

  • Metrology
  • Semiconductor Manufacturing
  • Data Analysis

Background:

  • Hybrid metrology combines diverse measurement techniques for enhanced critical dimension determination.
  • The semiconductor industry increasingly relies on hybrid metrology for accurate 3-D structure characterization.
  • Accurate uncertainty estimation is crucial for reliable metrology results.

Purpose of the Study:

  • To explore the benefits and challenges of hybrid metrology in the semiconductor industry.
  • To present a hybrid metrology approach combining optical critical dimension (OCD) and scanning electron microscope (SEM) measurements.
  • To address error analysis challenges, particularly the impact of correlated errors on measurement uncertainty.

Main Methods:

  • Integration of optical critical dimension (OCD) and scanning electron microscope (SEM) measurement data.
  • Development of methodologies for error analysis in hybrid metrology.
  • Utilizing hypothetical examples and real measurement data to validate the approach.

Main Results:

  • Demonstration of hybrid metrology's potential for feasible 3-D attribute measurements.
  • Identification of challenges in error analysis, especially concerning correlated errors and the chi-squared function.
  • Illustration of solutions for comparing results from different instrument models.

Conclusions:

  • Hybrid metrology offers improved quantitative characterization and uncertainty estimation for 3-D structures.
  • Addressing systematic and correlated errors is essential for robust hybrid metrology.
  • The presented methods provide solutions for reliable data integration and analysis in hybrid metrology.