Comprehensive Analysis of Random Telegraph Signal Noise (RTN) Using a Physics-Informed Machine [...]

Imec India Private Limited; Vlaams-Brabant; Sur place; EUR 20 000 - 40 000; Plein temps

Résumé du poste

A leading technology research institute in Belgium is offering an internship and thesis project focused on the analysis of random telegraph signal noise (RTN) in advanced NAND memory devices. Candidates with a Master’s degree in Engineering Science, Nanotechnology, Materials Engineering, or Physics are encouraged to apply. The role involves hands-on data measurement and interpretation, providing valuable experience in cutting-edge methodologies in device reliability.

Qualifications

• Master of Engineering Science or Master of Science required.
• Background in Nanoscience & Nanotechnology, Materials Engineering, or Physics needed.

Responsabilités

• Measure and analyze random telegraph signal noise (RTN) on advanced 3-dimensional NAND test devices.
• Propose improvements for measurement and analysis methodologies.
• Extract time constants of individual charging defects related to the materials in the NAND stack.

Description du poste

/ Comprehensive Analysis of Random Telegraph Signal Noise (RTN) Using a Physics-Informed Machine Learning Framework on Advanced 3-demensional NAND Memory Devices

Comprehensive Analysis of Random Telegraph Signal Noise (RTN) Using a Physics-Informed Machine Learning Framework on Advanced 3-demensional NAND Memory Devices

Master projects/internships - Leuven | More than two weeks ago

Discover hands-on how a single defect in a material can influence the reliability of advanced memory devices

This project aims at measuring and analysing random telegraphsignal noise (RTN) on advanced 3-dimensional memory devices by using a recentlydeveloped physics-informed machine learning framework.

RTN, caused by discrete charge trapping and de-trapping indefects inside the material, is one of the measured reliability issues onscaled devices since the amplitude of the RTN increases with decreasing deviceareas. However, RTN generated bymultiple defects creates very complex data, resulting in difficulties ofsystematic and comprehensive analysis.

Recently, imec developed a physics-informed machinelearning framework based on a Bayesian-based algorithm together with affinitypropagation clustering, which can efficiently and accurately analyse complexRTN data with multiple traps. In addition to the novel analysis framework, imechas developed advanced measurement methodologies and statistical analysistechniques to extract, interpret and understand the reliability physics reflectingin RTN phenomena.

This project provides opportunities and experiences tolearn the physics-informed machine learning framework, advanced electrical measurementsmethodologies, and statistical analysis techniques, as well as the reliability physicsof novel devices.

ProjectTasks:

• The student will measure and analyse random telegraphsignal noise (RTN) on advanced 3-dimensional NAND test devices
• The student will propose possible improvementsof the measurement and analysis methodologies
• The student will extract time constants ofindividual charging defects, relate these properties to the materials in theNAND stack, and investigate the trends with geometry of the devices.

For more information, please contact Yusuke Higashi ([e-mail]).

Type of project: Combination of internship and thesis, Internship, Thesis

Duration: >6 months

Required degree: Master of Engineering Science, Master of Science

Required background: Nanoscience & Nanotechnology, Materials Engineering, Physics

Supervising scientist(s): For further information or for application, please contact: Yusuke Higashi ([e-mail] )

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