Monolithic integration of sub-volt memristor-driven pixels for capacitorless active-matrix micro-LED displays
Abstract Active-matrix (AM) micro light-emitting diode (micro-LED) displays rely on transistors and capacitors, thereby limiting integration density, power efficiency, and manufacturing simplicity. While the monolithic integration of transistors onto micro-LED chips or complementary metal oxide semiconductor-based driving circuits has been investigated, these approaches still face challenges such as complex processing, unstable transistor performance, and dependence on capacitor-integrated thin-film transistor (TFT) backplanes. In this study, we present a capacitorless AM micro-LED display architecture driven by a germanium telluride memristor (GeTe memristor), monolithically integrated with the micro-LED chip. The GeTe memristor demonstrates multilevel switching, strong drive capability, and ultra-low operating voltages (SET < 0.2 V, RESET > −0.2 V) along with excellent thermal and electrical stability. Notably, its fabrication requires no thermal annealing, thus simplifying array-level integration compared to conventional TFT-based systems. Using the aforementioned architecture, we successfully demonstrated a capacitorless 12 × 12 AM micro-LED array capable of displaying alphabetic characters. This approach simplifies the manufacturing process and improves pixel density. Hence, the energy-efficient GeTe memristor offers a promising alternative to conventional TFT–capacitor configurations, thereby enabling the development of low-power, high-resolution display systems.
Authors
- Kyung Rock Son
- Seok Hee Hong
- Tukaram D. Dongale (ORCID: https://orcid.org/0000-0003-2536-6132)
- Tae Geun Kim (ORCID: https://orcid.org/0000-0001-6211-1134)
- Sung-Jin Choi (ORCID: https://orcid.org/0000-0003-1256-7832)
- Ho Jin Lee
- Sim Hun Yuk
Institutions
- Shivaji University (IN)
- Korea University (KR)
- Samsung (South Korea) (KR)
- Saveetha University (IN)
Publication Details
- Journal
- International Journal of Extreme Manufacturing
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1088/2631-7990/ae1a22
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73