Ultrafast dual-band all-optical logic gate design using CMOS compatible alternative plasmonic materials with IMI geometry and nanoring resonators
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Updated: Nov 2, 2026

Fabrication and Operation of a Nano-Optical Conveyor Belt
Published on: August 26, 2015
Md Faiyaz Abrar Fahim1, Abu S M Mohsin1, Mohammed Belal Hossain Bhuian1
1Nanotechnology, AI, IoT and Applied Machine Learning Research Group (Nano-Bio-AI), Department of Electrical and Electronic Engineering, BRAC University Dhaka Bangladesh mfaiyazabrarfahim@gmail.com asm.mohsin@bracu.ac.bd.
Abstract:
The accelerating demand for ultrafast, scalable, energy-efficient and miniaturized computing has exposed the physical limits of conventional CMOS electronics, prompting the transition toward optical and plasmonic architectures. Existing metallic (plasmonic) nanoparticles primarily gold and silver suffer from high losses, thermal instability, and incompatibility with CMOS fabrication, constraining large scale integration. To address this gap, we propose a dual band, CMOS compatible material platform, Insulator-Metal-Insulator (IMI) nanostructure leveraging alternative plasmonic materials - Indium Tin Oxide (ITO) and the dielectric Silicon Nitride (Si3N4) for ultrafast all-optical logic gate (AOLG) realization. The designed hybrid IMI waveguide-nanoring resonator demonstrates high transmission efficiency of 86.15% (λ = 1173.8 nm) & 80.69% (λ = 2054.7 nm) at two optical bands and low insertion losses. The compact (400 × 400 nm2) footprint supports all seven fundamental logic operations (NOT, AND, OR, NAND, NOR, XOR, XNOR) within a single architecture. Quantitative analysis reveals an extinction ratio exceeding 18 dB and ultrafast femtosecond response times, demonstrating competitive simulated optical performance subject to experimental validation. This CMOS compatible dual band architecture demonstrates a promising route toward energy efficient photonic integrated circuits (PICs) and opto-electronic integrated circuits (OEICs), enabling the next generation of AI driven, neuromorphic, and high-speed optical computing platforms.

