Article Dans Une Revue Microelectronics Reliability Année : 2025

New statistical analysis methodology to forecast the memory cell behavior before reliability test

Résumé

In this paper, a machine learning method is proposed implementing the Principal Component Analysis to study the statistical EEPROM endurance degradation. This technique is firstly applied to an UV irradiated memory array. Then, the Density Based Spatial Clustering of Applications with Noise and the Gaussian Mixture Model are presented to extract the minority population of cells. The reliability test study demonstrated the ability of the proposed technique to correlate electrical parameters to forecast the quality and performance of a memory array. Compared to the classical threshold voltage (V th ) analysis, this method is more effective for predicting which population will experience greater degradation.

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hal-05560717 , version 1 (07-04-2026)

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S. Perrin, V. Della Marca, T. Kempf, Marc Bocquet, L. Welter, et al.. New statistical analysis methodology to forecast the memory cell behavior before reliability test. Microelectronics Reliability, 2025, 168, pp.115659. ⟨10.1016/j.microrel.2025.115659⟩. ⟨hal-05560717⟩
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