# Compact, Energy-Efficient, High-Speed Electro-Optic Microring Modulator Based on Graphene-TMD 2D Materials

## TL;DR

This paper introduces a new energy-efficient microring modulator using graphene and TMD materials for high-speed data modulation in AI systems.

## Contribution

The novel contribution is a compact, high-speed electro-optic microring modulator using a graphene-TMD interface for energy-efficient data modulation.

## Key findings

- The modulator operates at 26.3 GHz with 5.8 fJ/bit energy consumption and 20 μm² area under 10% coverage.
- It achieves a modulation efficiency of VπL = 0.203 V·cm and an insertion loss of 6.7 dB.
- The device can be manufactured using standard fabrication techniques.

## Abstract

The continued performance scaling of AI gigafactories requires the development of energy-efficient devices to meet the rapidly growing global demand for AI services. Emerging materials offer promising opportunities to reduce energy consumption in such systems. In this work, we propose an electro-optic microring modulator that exploits a graphene (Gr) and transition-metal dichalcogenide (TMD) interface for phase modulation of data-bit signals. The interface is configured as a capacitor composed of a top Gr layer and a bottom WSe2 layer, separated by a dielectric Al2O3 film. This multilayer stack is integrated onto a silicon (Si) waveguide such that the microring is partially covered, with coverage ratios varying from 10% to 100%. In the design with the lowest power consumption, the device operates at 26.3 GHz and requires an energy of 5.8 fJ/bit under 10% Gr-TMD coverage while occupying an area of only 20 μm2. Moreover, a modulation efficiency of VπL = 0.203 V·cm and an insertion loss of 6.7 dB are reported for the 10% coverage. The Gr-TMD-based microring modulator can be manufactured with standard fabrication techniques. This work introduces a compact microring modulator designed for dense system integration, supporting high-speed, energy-efficient data modulation and positioning it as a promising solution for sustainable AI gigafactories.