This thesis focuses on identifying and classifying defects in STT-MRAM technology using novel and machine learning approaches. The thesis discusses the basic principles of STT-MRAM and the semiconductor chip manufacturing process and test stages. The research aims to develop novel methods and explore machine-learning approaches to diagnose defects in STT-MRAM devices. The current defect identification methodologies have shown certain cost, speed, and scalability limitations. The thesis presents DAT-based and ML-based Diagnosis methodologies to identify and classify STT-MRAM unique defects to address these challenges. The methods are evaluated and validated on experimental wafers performed at IMEC in Leuven, Belgium.

DAT-based Diagnosis involves automated defect identification in STT-MRAM based on identifying features automatically extracted from specialized measurements targeting the unique defects, Pinhole, Intermediate State, SAF Flip, and Back-Hopping. ML-based Diagnosis uses machine learning techniques to classify defects using MTJ features extracted from low-cost measurements. Data collected from electrical measurements on experimental STT-MRAM devices serve as the basis for evaluating the developed methodologies. The thesis also discusses data analysis, including data visualization, feature correlations, and outlier analysis for future research. Furthermore, a machine learning training process is performed, including hyperparameter optimization and evaluation using F-score and B-accuracy metrics to assess the model's performance and the ability to generalize on unseen data.

DAT-based Diagnosis aims to maximize the defect detection accuracy at the expense of measurement costs. In contrast, ML-based Diagnosis minimizes the measurement cost while maximizing the detection accuracy for robust and balanced classification. However, the DAT-based Diagnosis is not verified using PFA to validate the defect types identified by the developed methodology. Furthermore, the ML-based Diagnosis uses training data labeled by the unverified DAT-based Diagnosis approach to train machine learning models. Despite these limitations, the results have shown valuable insights into defect identification and classification, proving a robust framework for diagnosing STT-MRAM devices. Additionally, a scientific paper is submitted on march-based diagnosis, adapting the DAT-based Diagnosis method to industrial chips that are limited in extracting the identifying features.