20240046022. METHODS FOR SAMPLE SCHEME GENERATION AND OPTIMIZATION simplified abstract (ASML Netherlands B.V.)

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METHODS FOR SAMPLE SCHEME GENERATION AND OPTIMIZATION

Organization Name

ASML Netherlands B.V.

Inventor(s)

Pierluigi Frisco of Eindhoven (NL)

METHODS FOR SAMPLE SCHEME GENERATION AND OPTIMIZATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240046022 titled 'METHODS FOR SAMPLE SCHEME GENERATION AND OPTIMIZATION

Simplified Explanation

The patent application describes a method for generating sample schemes based on measurement data associated with different locations. The method involves analyzing the measurement data to identify statistically different groups of locations and configuring a sample scheme generation algorithm based on these groups. The method also includes obtaining constraints and key performance indicators associated with the sample scheme across one or more substrates and using them in a multi-objective genetic algorithm for sample scheme generation. The locations can define regions spanning multiple fields and the analysis of the measurement data involves stacking across these fields using different sub-sampling techniques.

  • The method involves obtaining measurement data associated with a set of locations.
  • The measurement data is analyzed to determine statistically different groups of locations.
  • A sample scheme generation algorithm is configured based on the statistically different groups.
  • Constraints and key performance indicators associated with the sample scheme are obtained.
  • A multi-objective genetic algorithm is used for sample scheme generation.
  • The locations can define regions spanning multiple fields.
  • The analysis of the measurement data involves stacking across the spanned fields using different sub-sampling techniques.

Potential applications of this technology:

  • Agricultural research: The method can be used to generate sample schemes for studying crop performance across different fields and substrates.
  • Environmental monitoring: The method can be applied to generate sample schemes for monitoring pollution levels or biodiversity across various locations.
  • Quality control: The method can be used to generate sample schemes for inspecting product quality across different manufacturing sites.

Problems solved by this technology:

  • Efficient sample scheme generation: The method provides a systematic approach for generating sample schemes that capture the variability across different locations.
  • Statistical analysis: The method includes analyzing measurement data to identify statistically different groups of locations, allowing for more accurate and representative sample schemes.
  • Optimization: The use of a multi-objective genetic algorithm helps optimize the sample scheme generation process by considering multiple constraints and key performance indicators.

Benefits of this technology:

  • Improved data collection: The generated sample schemes ensure that data is collected from diverse locations, leading to a more comprehensive understanding of the studied phenomena.
  • Cost and time savings: The method optimizes the sample scheme generation process, reducing the number of required measurements and minimizing the time and resources needed for data collection.
  • Enhanced decision-making: The statistically different groups identified in the analysis provide valuable insights for decision-making processes, allowing for targeted actions and interventions.


Original Abstract Submitted

a method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. a method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. the locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.

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