Multifunctional CMOS-integrable and reconfigurable 2D ambipolar tellurene transistors for neuromorphic and in-memory computing
Despite significant efforts to eliminate the von Neumann bottleneck with new two-dimensional (2D) nanomaterial-based cutting-edge device structures, there remains room for exploring alternative computing architectures that leverage 2D nanomaterials. This study introduced a groundbreaking strategy featuring a complementary metal-oxide semiconductor (CMOS)-integrable and reconfigurable ambipolar 2D tellurene (Te) transistor toward non-von Neumann computing architecture. The innovative scenario integrated seamlessly with CMOS technology, utilizing the p/n-switchable ambipolar characteristics inherited from precise Fermi-level alignment <i>via</i> thermal atomic layer deposition. Further, the architecture exhibited remarkable synaptic behavior while maintaining the conventional inverter performance within a compact single 2D Te device architecture. Expanding these findings, we demonstrated a compact programmable CMOS inverter with reduced spatial complexity and also visualized the construction of diverse complementary logic-in-memory computing. The results of this study pave the way for revolutionary in-memory computing that transcends the boundaries of the von Neumann architecture based on 2D nanomaterials.
Authors
- Moonsang Lee (ORCID: https://orcid.org/0000-0003-4178-6989)
- Myung Gwan Hahm (ORCID: https://orcid.org/0000-0002-6432-0578)
- Jungwoo Huh (ORCID: https://orcid.org/0000-0002-3178-2485)
- Min‐Kyu Joo (ORCID: https://orcid.org/0000-0001-7537-1015)
- Mino Yang (ORCID: https://orcid.org/0000-0002-3860-602X)
- Bolim You
- Unjeong Kim
- Yuna Kim (ORCID: https://orcid.org/0000-0002-9387-3796)
Institutions
- Inha University (KR)
- Dongguk University (KR)
- University of Cambridge (GB)
- Sookmyung Women's University (KR)
- Korea Basic Science Institute (KR)
- Inha University Hospital (KR)
Publication Details
- Journal
- Nanoscale Horizons
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5nh00113g
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00