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 V_πL = 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.

In this work, we investigate the spectral, information-theoretic, and thermodynamic properties of a fractal Schrödinger system with position-dependent mass subject to an effective semiconductor-like confinement. We employ a fractal momentum operator and a Von Roos Hamiltonian with BenDaniel–Duke ordering to obtain exact analytical solutions for the energy spectrum and wave functions. The interplay between the fractal parameter $\alpha$, the effective lattice scale $l_0$, and the harmonic confinement strength $\omega$ is explored. We perform a comprehensive analysis of the Shannon entropy, Fisher information, and Fisher–Shannon complexity in both coordinate and momentum spaces. Our results demonstrate that these parameters directly control the localization–delocalization transition and the informational architecture of the quantum states, while satisfying the entropic and Fisher uncertainty relations. Furthermore, we derive the exact partition function and the corresponding thermodynamic properties (free energy, internal energy, entropy, and specific heat) of the system. The analytical framework presented offers valuable insights into the spectral, information-theoretic, and thermodynamic behavior of quantum systems in fractal semiconductor-like environments.

In this paper, we study Dirac fermions confined in a graphene quantum ring with wedge disclinations under an external magnetic flux. The topological defect is modeled through a conical geometry, inducing effective gauge contributions in the angular sector of the Dirac Hamiltonian. In this case, analytical expressions for the energy spectrum are obtained under infinite-mass boundary conditions, revealing a nontrivial interplay between curvature, magnetic flux, and angular momentum quantization. Within an information-theoretic framework, we compute the Shannon entropies in position and momentum spaces. Also, the results show that both magnetic flux and topological charge significantly affect localization properties, redistributing uncertainty between conjugate spaces while preserving the entropic uncertainty relation. In this context, our results demonstrate that geometric defects act as tunable mechanisms for controlling spectral and informational properties in graphene-based mesoscopic systems.

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 a novel 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 graphene 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%. The proposed device design and its key figures of merit, including energy efficiency, are analyzed. Simulation results indicate that the microring modulator achieves low energy consumption and high-speed operation while maintaining a compact footprint. In particular, the device operates at 39.1 GHz with an energy consumption of 8.3 fJ/bit under 25% Gr–TMD coverage, whilst occupying an area of only 20 μm2. Furthermore, a modulation efficiency of VπL= 0.082 V·cm and an insertion loss of 6.8 dB are obtained for the 25% coverage. The proposed Gr–TMD-based microring modulator demonstrates significant potential for high-speed, energy-efficient data modulation, contributing to the development of more sustainable AI gigafactories.