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A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.

Fernando Morilla1, Jesús Vega2, Sebastián Dormido-Canto1

  • 1Departamento de Informática y Automática, UNED, Juan del Rosal 16, 28040 Madrid, Spain.

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Summary
This summary is machine-generated.

This study introduces an Advanced Predictor of Electrical Parameters (APEP) using machine learning to forecast electronic component degradation from radiation. The APEP method accurately predicts component performance changes based on irradiation dose, applicable to various electronic parts.

Keywords:
commercial off-the-shelfdegradation predictionnew spaceradiation effectsunsupervised clustering

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Electronic components are susceptible to degradation when exposed to radiation.
  • Understanding and predicting this degradation is crucial for reliability in radiation environments.
  • Existing methods may not fully capture complex parameter variations with irradiation dose.

Purpose of the Study:

  • To introduce a novel machine learning-based technique for predicting electronic component degradation due to radiation.
  • To demonstrate the applicability of the Advanced Predictor of Electrical Parameters (APEP) for bipolar transistors.
  • To establish a generalizable methodology for predicting radiation-induced degradation in various electronic components.

Main Methods:

  • The Advanced Predictor of Electrical Parameters (APEP) technique employs machine learning algorithms.
  • It involves two key steps: recognizing degradation patterns in a database and predicting degradation for new, unirradiated samples.
  • APEP can be implemented using 'pure data driven' or 'model based' approaches.

Main Results:

  • The APEP technique successfully predicts the degradation of electrical parameters in bipolar transistors under irradiation.
  • The method effectively models how electrical parameters vary with increasing irradiation dose.
  • The predictive accuracy demonstrates the viability of the machine learning approach.

Conclusions:

  • The Advanced Predictor of Electrical Parameters (APEP) offers an innovative and effective method for predicting radiation-induced degradation in electronic components.
  • The methodology is adaptable and can be applied beyond bipolar transistors to other electronic components.
  • This technique enhances the reliability assessment of electronics in radiation-exposed applications.