Ultrathin nanocapacitor assembled via atomic layer deposition

Author(s)

Medina, Javier Alonso Lopez ; Mejia-Salazar, J. Ricardo ; Carvalho, William O. F. ; Mercado, Cesar Lopez ; Nedev, N. ; Gomez, Faustino Reyes ; Oliveira Jr, Osvaldo N. ; Farias, M. H. ; Tiznado, Hugo

• Document type: Journal article
• Source: Nanotechnology; v. 35, n. 50, p. 11-pg., 2024-12-09.

Abstract

We fabricated ultrathin metal-oxide-semiconductor (MOS) nanocapacitors using atomic layer deposition. The capacitors consist of a bilayer of Al2O3 and Y2O3 with a total thickness of similar to 10 nm, deposited on silicon substrate. The presence of the two materials, each slab being similar to 5 nm thick and uniform over a large area, was confirmed with transmission electron microscopy and x-ray photoelectron spectroscopy. The capacitance in accumulation varied from 1.6 nF (at 1 MHz) to similar to 2.8 nF (at 10 kHz), which is one to two orders of magnitude higher than other nanocapacitors. This high capacitance is attributed to the synergy between the dielectric properties of ultrathin Al2O3 and Y2O3 layers. The electrical properties of the nanocapacitor are stable within a wide range of temperatures, from 25 degrees C to 150 degrees C, as indicated by capacitance-voltage (C-V). Since the thickness-to-area ratio is negligible, the nanocapacitor could be simulated as a single parallel plate capacitor in COMSOL Multiphysics, with good agreement between experimental and simulation data. As a proof-of-concept we simulated a MOS field effect transistor device with the nanocapacitor gate dielectric, whose drain current is sufficiently high for micro and nanoelectronics integrated circuits, including for applications in sensing. (AU)

• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 23/08999-9 - Machine Learning-Driven Optimization of Magnetoplasmonic Biosensors
• Grantee: William Orivaldo Faria Carvalho
• Support Opportunities: Scholarships in Brazil - Post-Doctoral