Abstract <p>High-k dielectric materials such as HfO<sub>2</sub> have garnered significant attention for their potential applications in advanced electronic devices due to their superior dielectric properties. Particularly, oxygen vacancies within these materials can be strategically utilized to implement memory functionalities. However, the precise analysis of the electrical, chemical, and electrochemical characteristics related to oxygen vacancies remains challenging. In this study, we fabricated a double-gate thin-film transistor (TFT) structure employing HfO<sub>2</sub> as the gate dielectric for both top and bottom gates, with the oxygen vacancy concentration intentionally modulated by introducing a TiO<sub>2</sub> interlayer at the bottom gate stack. This TiO<sub>2</sub> layer effectively increases the oxygen vacancy content within the bottom gate dielectric, facilitating oxygen vacancy migration-based memory operation primarily through the bottom gate. The resulting asymmetry between the top and bottom gates was systematically analyzed using low-frequency noise (LFN) characterization, elucidating for the first time the distinct impacts of oxygen vacancy modulation on device electrical behavior and operational mechanisms. This comprehensive LFN analysis provides critical insights into the fundamental dynamics of defect-mediated memory operation, highlighting the importance of dielectric engineering in optimizing next-generation oxide-based electronic devices.</p> Graphical abstract <p>This study unravels ionic switching dynamics in double-gate HfO2–IGZO TFTs, where a TiO2 scavenging layer modulates oxygen vacancies to enable memory operation. Low-frequency noise spectroscopy reveals a ionic-dependent transition between distinct noise mechanisms, providing fundamental insights into vacancy-driven dynamics and guiding the optimization of high-k dielectric transistors for next-generation computing.</p> <p></p>

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Unraveling ionic switching dynamics in high-k dielectric double-gate transistors via low-frequency noise spectroscopy

  • Soi Jeong,
  • Chang-Hyeon Han,
  • Been Kwak,
  • Ryun-Han Koo,
  • Youngchan Cho,
  • Jangsaeng Kim,
  • Jong-Ho Lee,
  • Daewoong Kwon,
  • Wonjun Shin

摘要

Abstract

High-k dielectric materials such as HfO2 have garnered significant attention for their potential applications in advanced electronic devices due to their superior dielectric properties. Particularly, oxygen vacancies within these materials can be strategically utilized to implement memory functionalities. However, the precise analysis of the electrical, chemical, and electrochemical characteristics related to oxygen vacancies remains challenging. In this study, we fabricated a double-gate thin-film transistor (TFT) structure employing HfO2 as the gate dielectric for both top and bottom gates, with the oxygen vacancy concentration intentionally modulated by introducing a TiO2 interlayer at the bottom gate stack. This TiO2 layer effectively increases the oxygen vacancy content within the bottom gate dielectric, facilitating oxygen vacancy migration-based memory operation primarily through the bottom gate. The resulting asymmetry between the top and bottom gates was systematically analyzed using low-frequency noise (LFN) characterization, elucidating for the first time the distinct impacts of oxygen vacancy modulation on device electrical behavior and operational mechanisms. This comprehensive LFN analysis provides critical insights into the fundamental dynamics of defect-mediated memory operation, highlighting the importance of dielectric engineering in optimizing next-generation oxide-based electronic devices.

Graphical abstract

This study unravels ionic switching dynamics in double-gate HfO2–IGZO TFTs, where a TiO2 scavenging layer modulates oxygen vacancies to enable memory operation. Low-frequency noise spectroscopy reveals a ionic-dependent transition between distinct noise mechanisms, providing fundamental insights into vacancy-driven dynamics and guiding the optimization of high-k dielectric transistors for next-generation computing.