Ferroelectric Field-Effect Transistors (FeFETs) are promising candidates for non-volatile memory (NVM) technologies, especially in embedded systems and edge computing. However, due to their physical characteristics, FeFETs exhibit unique defects—such as Threshold Voltage Shifting (TVS) caused by trap charges in the oxide layer—that are not captured by conventional defect models. This study adopts the Device-Aware Test (DAT) methodology to model these defects by incorporating their impact into the electrical parameters, calibrated using measurement data. Defect injection, circuit-level simulations, and fault analysis are performed to derive realistic fault models. Finally, the March algorithm and Design-for-Test (DfT) techniques are proposed to effectively detect these defects.

Device-Aware Test for Threshold Voltage Shifting in FeFET / Wang, Changhao; Kolahimahmoudi, Nima; Bellarmino, Nicolo; Cantoro, Riccardo; Yuan, Sicong; Xun, Hanzhi; Chen, Danyang; Yin, Chujun; Taouil, Mottaqiallah; Fieback, Moritz; Li, Xiuyan; Wang, Lin; Li, Chaobo; Hamdioui, Said. - (2025), pp. 410-413. ( IEEE International Test Conference 2025 San Diego (USA) 20-26 September 2025) [10.1109/ITC58126.2025.00052].

Device-Aware Test for Threshold Voltage Shifting in FeFET

Wang, Changhao;Kolahimahmoudi, Nima;Bellarmino, Nicolo;Cantoro, Riccardo;Hamdioui, Said
2025

Abstract

Ferroelectric Field-Effect Transistors (FeFETs) are promising candidates for non-volatile memory (NVM) technologies, especially in embedded systems and edge computing. However, due to their physical characteristics, FeFETs exhibit unique defects—such as Threshold Voltage Shifting (TVS) caused by trap charges in the oxide layer—that are not captured by conventional defect models. This study adopts the Device-Aware Test (DAT) methodology to model these defects by incorporating their impact into the electrical parameters, calibrated using measurement data. Defect injection, circuit-level simulations, and fault analysis are performed to derive realistic fault models. Finally, the March algorithm and Design-for-Test (DfT) techniques are proposed to effectively detect these defects.
2025
979-8-3315-7041-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3003708