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.

This paper presents the design and implementation of a 64-Multi-Phased Time-to-Digital Converter (TDC64) architecture on a low-cost Cyclone V FPGA. Operating at 500 MHz, the architecture successfully achieves a theoretical resolution of 31.25 picoseconds (ps). The modular design leverages a multi-phased counter methodology to significantly enhance temporal granularity. The performance was comprehensively characterized in two stages. Internal analysis, using the Signal Tap Logic Analyzer, confirmed the design's integrity, yielding a measurement result of 9660 for a 300 ns interval, representing a low deviation of approximate 0.62 %. Linearity tests conducted over a 20 ns span showed excellent performance with differential nonlinearity (DNL) ranging from +0.053 to -0.101 and integral nonlinearity (INL) between -0.192 and -0.218. External testing, utilizing a waveform generator and oscilloscope, revealed an uncompensated resolution of 47.43 ps (34 % deviation). Mitigating the noise, the compensated resolution for an interval of 300 ns result in 9,596, a resolution of 31.263 picoseconds, which represents 0.04%. A remarkable result close to the theoretically expected value. The mean values were employed to evaluate the linearity, yielding DNL of +0,0560 and -0,0129, as well as INL of +0,0484 and -0,2104. Representing high linearity and high resolution compared with a previous noisy entry signal. This work demonstrates that high-end timing performance is attainable on cost-effective FPGA platforms.

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.

This work addresses the qualification of photovoltaic micromodules intended for energy-harvesting applications in Internet of Things systems and proposes a novel qualification method based on highresolution electronic signature masks obtained through Time-Domain Reflectometry. The method applies frequency-to-time conversion of measured S11 parameters acquired using a Vector Network Analyzer under laboratory conditions. Accelerated reliability testing is employed as a sample preparation stage to validate the proposed qualification approach and to contextualize micromodules within a framework beyond existing regulatory standards, which currently exclude PV devices below 5 Wp. The qualification method based on pass/fail TDR signature masks demonstrated in this study has the potential to become a powerful tool for quality inspection of photovoltaic micromodules by assessing deterioration over time. The research introduces TDR technology—traditionally associated with high-frequency telecommunications—into the photovoltaic contex for research and development, manufacturing inspection, and certification purposes.

This article investigates the qualification of photovoltaic micromodules intended for energy harvesting in Internet-of-Things systems, with emphasis on the degradation mechanisms induced by accelerated environmental aging typical of tropical conditions. The study employs multiple complementary characterization techniques—including pulsed I–V measurements, electroluminescence imaging, and impedance spectroscopy—combined with multivariate statistical analysis to support decision-making regarding acceptance criteria, fault prediction models, maintenance scheduling, and failure mode clustering.In addition, dimensionality-reduction methods are explored to extract the most relevant indicators from empirical datasets and to improve interpretability when dealing with highly correlated or redundant variables. The work addresses a regulatory gap affecting photovoltaic devices below 5 Wp, a class of modules whose deployment is rapidly expanding in remote, autonomous, and low-power IoT applications, yet remains largely unsupported by existing reliability and certification standards.