Enhancement of On-Current and Reliability in InGaZnO Thin-Film Transistors for Synaptic Circuit Applications through Gate Insulator Stack Engineering
A nanometer-scale multilayer gate insulator (GI) engineering strategy is introduced to simultaneously enhance the on-current and bias stability of amorphous InGaZnO thin-film transistors (a-IGZO TFTs). Atomic layer deposition supercycle modifications employ alternating layers of Al<sub>2</sub>O<sub>3</sub>, TiO<sub>2</sub>, and SiO<sub>2</sub> to optimize the gate-oxide stack. Each GI material is strategically selected for complementary functionalities: Al<sub>2</sub>O<sub>3</sub> improves the interfacial quality at both the GI/semiconductor and GI/metal interfaces, thereby enhancing device stability and performance; TiO<sub>2</sub> increases the overall dielectric constant; and SiO<sub>2</sub> suppresses leakage current by serving as a high-energy barrier between Al<sub>2</sub>O<sub>3</sub> and TiO<sub>2</sub>. Layer ordering, particularly separating SiO<sub>2</sub> and TiO<sub>2</sub> with Al<sub>2</sub>O<sub>3</sub>, is crucial for suppressing charge trapping and defect-state density, as verified by electrical performance comparisons of the fabricated metal-insulator-metal capacitors and thin-film transistors (TFTs). The optimized multilayer GI demonstrates reduced insulator leakage current, roughly a 1.76× increase in on-current relative to a single-layer Al<sub>2</sub>O<sub>3</sub> GI, a 1.47× mobility increase, and enhanced bias stability with a -5 mV threshold-voltage shift under positive bias stress. Beyond device-level improvements, the cycling endurance of the 6-transistor 1-capacitor synaptic circuit is assessed, demonstrating faster operation and enhanced weight-update cycling stability with the engineered GI compared to conventional designs. This optimization addresses the inherent mobility-reliability trade-off in IGZO TFT fabrication, enabling improved performance for very-large-scale integration circuits and neuromorphic applications.
Authors
- Ha‐Jun Sung (ORCID: https://orcid.org/0000-0002-1037-4482)
- Minseung Kang (ORCID: https://orcid.org/0000-0001-6576-654X)
- Sang‐Bum Kim (ORCID: https://orcid.org/0000-0001-7460-3750)
- Sang‐Wook Kim (ORCID: https://orcid.org/0000-0002-6345-9084)
- Jeong Hee Han (ORCID: https://orcid.org/0000-0001-8167-2703)
- Jongun Won
- Younjin Jang
- Narae Han
- Youngchae Roh
- Jee-Eun Yang
Institutions
- Seoul National University (KR)
- Samsung (South Korea) (KR)
- Seoul Semiconductor (South Korea) (KR)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-09-05
- DOI
- https://doi.org/10.1021/acsami.5c13069
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Research Foundation of Korea
- Institute for Information and Communications Technology Promotion