This numerical study uses machine learning techniques to enhance the resolution of local near-field probing measurements when the probe is larger than the examined device. The research shows that machine learning can achieve a spatial resolution of λ/10 with a few wavelength-wide probes while keeping the relative error below 3%. It also finds that fully connected neural networks outperform linear regression with limited training data, but linear regression is both sufficient and efficient for larger data sets. These results suggest that similar machine learning methods can improve the resolution of various experimental measurements.
This work presents a numerical investigation into the performance metrics of photodetectors made from monolayer MoS2, a two-dimensional material with unique optoelectronic properties. The study introduces a one-dimensional drift-diffusion framework along with wave propagation in layered media analysis. Results demonstrate a peak quantum efficiency at 561 nm, influenced by the substrate. The precision of the model validates its utility for characterizing MoS2 photodetectors, emphasizing the importance of background inclusion in calculations. The efficient computation makes the model suitable for in-depth device analysis.
KEYWORDS: Photodetectors, Quantum efficiency, Temperature distribution, Indium gallium arsenide, Electric fields, Electrical conductivity, Temperature metrology
We present an approximation method to compute the temperature distribution in photodetectors under steady-state optical excitation. The derived temperature profile assesses the impact on performance metrics like quantum efficiency, bandwidth, and phase noise. Our numerical study reveals that assuming constant room temperature leads to overestimated output current and quantum efficiency and underestimated bandwidth. In contrast, a varying temperature model closely aligns with experimental values. InGaAs’s low thermal conductivity impedes heat dissipation, leading to temperature accumulation. Changing optical excitation while maintaining constant output current results in nonlinear changes in bandwidth, phase noise, and quantum efficiency. These findings aid in understanding and optimizing thermal management in photodetectors under strong optical excitations.
We initially developed an efficient solver to study photodetectors composed of multiple semiconductor layers with varying thicknesses and doping concentrations. Subsequently, we employed it as the forward solver for three different numerical optimization methods aimed at designing Si-Ge photodetectors with larger bandwidth, higher quantum efficiency, and lower phase noise. Our work offers new insights into the design of high-performance photodetectors—a challenging task due to computation time, design constraints, and the complexity of estimating sensitivity to design parameters.
We present a study on the accuracy of three neural network architectures, namely fully-connected neural networks, recurrent neural networks, and attention-based neural networks, in predicting the coupling response of broadband microresonator frequency combs. These frequency combs are crucial for technologies like optical atomic clocks. Optimizing their spectral features, especially the dispersion in coupling to an access waveguide, can be computationally demanding due to the large number of parameters and wide spectral bandwidths involved. To address this challenge, we employ machine learning algorithms to estimate the coupling response at wavelengths not present in the input training data. Our findings demonstrate that when trained with data sets encompassing the upper and lower limits of each design feature, attention mechanisms achieve over 90% accuracy in predicting the coupling rate for spectral ranges six times wider than those used in training. This significantly reduces the computational burden for numerical optimization in ring resonator design, potentially leading to a six-fold reduction in compute time. Moreover, devices with strong correlations between design features and performance metrics may experience even greater acceleration.
We fabricate and characterize mono- and few- layers of MoS2 and WSe2 on glass and SiO2/Si substrates. PbS quantum dots and/or Au nanoparticles are deposited on the fabricated thin metal dichalcogenide films by controlled drop casting and electron beam evaporation techniques. The reflection spectra of the fabricated structures are measured with a spatially resolved reflectometry setup. Both experimental and numerical results show that surface functionalization with metal nanoparticles can enhance atomically thin transition metal dichalcogenides’ absorption and scattering capabilities, however semiconducting quantum dots do not create such effect.
In order to protect optoelectronic and mechanical properties of atomically thin layered materials (ATLMs) fabricated over SiO2/Si substrates, a secondary oxide or nitride layer can be capped over. However, such protective capping might decrease ATLMs’ visibility dramatically. Similar to the early studies conducted for graphene, we numerically determine optimum thicknesses both for capping and underlying oxide layers for strongest visibility of monolayer MoS2, MoSe2, WS2, and WSe2 in different regions of visible spectrum. We find that the capping layer should not be thicker than 60 nm. Furthermore the optimum capping layer thickness value can be calculated as a function of underlying oxide thickness, and vice versa.
Graphene’s controllable optical conductivity and mechanically strong structure make it a suitable material to de- sign tunable localized surface plasmon resonance (LSPR) sensors. In this work, we theoretically and numerically demonstrate that the resonance wavelength of an LSPR sensor can be tuned to any value within a reasonably wide range of wavelengths by changing the voltage applied to graphene layer. Theoretical results reveal a higher sensitivity with respect to regular LSPR sensors.
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