Two-dimensional (2D) spectroscopy combines high temporal and spectral resolution, allowing the observation of ultrafast energy transfer and the separation of homogeneous and inhomogeneous broadening. Typically, 2D spectroscopy is dominated by the lowest-order nonlinear signal for a given phase-matching configuration, while signals of higher order are present but difficult to access separately. Recently, we introduced a technique to separate nonlinear orders in 2D spectroscopy by systematically varying the intensity of the pump pulses and postprocessing appropriately. Here, we unravel the full potential of higher-order 2D spectroscopy by separating multiple nonlinear orders at different multiquantum positions. As an example, we investigate a squaraine dimer. Using a theoretical model, we find excellent qualitative and quantitative agreement throughout all nonlinear orders and multiquantum positions. Our simulations demonstrate the sensitivity and information content hidden in the higher-order spectra such as transition dipole moments and energy levels even of highly excited states. Our results pave the way for establishing higher-order spectroscopy as a unique extension of multidimensional spectroscopy, providing access to highly excited states and their properties encoded in successive orders of nonlinearity.

Betavoltaic energy conversion is a specialized energy harvesting technology using a semiconductor to convert beta radiation from a radiation source into continuous electricity. Many semiconductor materials and radioactive isotopes are viable, making it challenging to benchmark technologies. To facilitate comparison, modeling, and characterization, we introduce three figures of merit: capture efficiency, gain, and gain efficiency. We showcase these metrics by numerically modeling the performance of GaAs, SiC, and GaN p-n and p-i-n junctions under 3H and 63Ni radioisotope irradiation. The capture efficiency describes the energy fraction that can be absorbed by the cell. The gain and gain efficiency quantify carrier multiplier effects and carrier collection effectiveness. The figures of merit study indicates that (1) absorber materials with low atomic numbers improve energy capture in the cell and (2) single-junction betavoltaics do not require complete radiation absorption to obtain maximum operating efficiency.

Interpretation of time-resolved spectroscopies such as transient absorption (TA) or two-dimensional (2D) spectroscopy often relies on the perturbative description of light-matter interaction. In many cases the third order of nonlinear response is the leading and desired term. When pulse amplitudes are high, higher orders of light-matter interaction can both distort lineshapes and dynamics and provide valuable information. Here, we present a general procedure to separately measure the nonlinear response orders in both TA and 2D spectroscopies, using linear combinations of intensity-dependent spectra. We analyze the residual contamination and random errors and show how to choose optimal intensities to minimize the total error in the extracted orders. For an experimental demonstration, we separate the nonlinear orders in the 2D electronic spectroscopy of squaraine polymers up to the 11th order.

Photonic or laser power converters are crucial components in power-by-light systems. However, their use in long-distance applications has been hindered by low efficiencies and output voltages within the optical fiber transmission window of 1.3-1.6 mu m laser wavelengths. Here, we improve and simplify the design and characterization processes for photonic power converters, exceeding 50% efficiency under 1.446 mu m laser light. We develop a calibrated model predicting efficiency gains with increasing bandgap, reaching up to 57% efficiency at a 1.3-mu m wavelength. As a first demonstration, we produce a high-efficiency device designed by the model: a four-junction InGaAsP photonic power converter with a conversion efficiency of 53.6% +/- 1.3% and an output voltage above 2 V under 15.2 W/cm2 of 1.446 mu m laser light. These advances open new, practical pathways for integrating photonic power converters into telecommunication systems and unlock the potential to further optimize their design with machine learning algorithms trained with our predictive model.

Luminescent coupling in multi-junction photovoltaic devices plays an important role in mitigating the negative effects of current mismatch on the performance of the device. We incorporate luminescent coupling into a Poisson/drift-diffusion (PDD) device model and compare results to the PDD model without luminescent coupling. An increase in the spectral response of the device occurs as the number of trap states in the device is reduced, which allows more radiative coupling events to occur. Additionally, the effects of current mismatch on efficiency due to variations in thickness are mitigated due to the presence of luminescent coupling. We observe that the total thickness of the bottom subcell has a strong impact on efficiency where thinner bottom layers lead to an increase in transmission into the substrate.

Reflecting sub-bandgap photons is crucial for maximizing the efficiency of thermophotovoltaic devices. However, existing metal-deposited reflectors rely on backside metallization, which cannot be grown epitaxially, necessitating additional processing steps. In this study, we fabricate InAs-based thermophotovoltaic devices featuring a straightforward, epitaxially grown sub-bandgap reflector composed of a single layer of n-doped InAs at a doping concentration of 2.4 x 1019 cm-3. This high doping produces long-wavelength metallic-like reflection, and our devices demonstrate high sub-bandgap reflectivity from 3.5 to 17 mu m, achieving up to 93 % reflectivity compared to 30-40 % for designs without the reflector. Using a calibrated optical model, we predict that the sub-bandgap reflectivity of this layer enhances spectral efficiency from 38 % to 79 % under a 600 K normally incident blackbody spectrum. This improvement rivals that of a standard gold back reflector, which achieves a spectral efficiency of 94 %. Additionally, our predictive electrical model, calibrated with fabricated devices, indicates that the reflective layer does not adversely affect the electrical properties of the thermophotovoltaic devices. This sub-bandgap reflector can be integrated into existing InAs-based thermophotovoltaic fabrication processes, eliminating complex substrate removal steps required for traditional gold reflectors.

Machine learning is proving to be a revolutionary tool across many disciplines, including optoelectronic device design. In this report, we compare classical and machine learning enhanced design optimization methodologies. We investigate, as an example case, the design of the complex structures of ten-junction InP lattice matched photonic power converters with In[Formula: see text]Ga[Formula: see text]As absorbers optimized for operation at 1550 nm. We find that the implicit pattern recognition capabilities of dimensionality reduction using principal component analysis accelerate design discovery, optimization, and the understanding of complex optical phenomena in the simulated devices. The dimensionality reduction approach offers over twenty times as many optimal designs with greater variability and with a 15% reduction in computational cost compared to a classical optimization method. Furthermore, we find that the representation of the reduced dimensionality subspace offers an intuitive interpretation of optical phenomena expected to occur in this design problem. This method is general and offers the potential for knowledge discovery, expanded design perspective, and optimization acceleration in conjunction with a significant reduction in computational expense in systems which can be numerically modeled.

We compare some classical and machine-learning enhanced design optimization methodologies. We investigate the design of the complex structures of ten-junction InP latticematched photonic power converters with In0.53 Ga0.47 As absorbers optimized for operation at 1550 nm with 53.6% ± 1.3% conversion efficiency. We find that the implicit pattern recognition capabilities of dimensionality reduction using principal component analysis accelerates design discovery, optimization, and the understanding of complex optical phenomena in the simulated devices.

We present a Poisson/drift-diffusion model that includes valley scattering effects for simulating valley photovoltaic devices. The valley photovoltaic concept is a novel implementation of a hot-carrier solar cell and leverages the valley scattering effect under large electric field to potentially achieve high voltage and high efficiency. Fabricated devices have shown S-shaped current-voltage curves, low fill factor, and thus low efficiency. We hence develop the first device model for valley photovoltaics. Our model includes electric-field-dependent valley scattering rates extracted from previous ensemble Monte Carlo simulations. We show that the condition of nonequilibrium carrier populations in the satellite valleys is not enough for valley photovoltaics to achieve high efficiency. We also show that increasing the built-in electric field of the valley-scattering region does not improve efficiency, contrary to previous suggestion.

Monte Carlo simulations, Poisson-drift-diffusion calculations, and particle swarm optimizations are performed to model and optimize p-n and p-i-n SiC betavoltaic cells operating under 3H and 63Ni radioisotopes. Our calculations show that the electron-hole pair generation profiles reach 2 mu m and 15 mu m-deep in the SiC cells under 3H and 63Ni, respectively. Our optimized designs have a 2 mu m and >10 mu m-thick heterostructure for each radioisotope. They ensure a carrier collection efficiency approaching 99 % over a large range of beta particle energies. The best devices have a betavoltaic conversion efficiency of >20 %.

Near-field thermophotovoltaics (NFTPV) systems have significant potential for waste heat recovery applications, with both high theoretical efficiency and power density, up to 40% and 11W/cm2 at 900 K. Yet experimental demonstrations have only achieved up to 14% efficiency and modest power densities (i.e., 0.75W/cm2). While experiments have recently started to focus on photovoltaic (PV) cells custom-made for NFTPV, many studies still rely on doped silicon radiators. In this work, we design an optimized NFTPV radiator for an indium arsenide-based system and, in the process, investigate models for the permittivity of InAs in the context of NFTPV. Based on existing measurements of InAs absorption, we find that the traditional Drude model overestimates free carrier absorption in InAs. We replace the Drude portion of the InAs dielectric function with a revised model derived from ionized impurity scattering. Using this revised model, we maximize the spectral efficiency and power density of a NFTPV system by optimizing the spectral coupling between a radiator and an InAs PV cell. We find that when the radiator and the PV cell are both made of InAs, a nearly threefold improvement of spectral efficiency is possible compared to a silicon radiator with the same InAs cell. This enhancement reduces subgap thermal transfer while maintaining power output.

Highly mismatched alloys are a class of semiconductor alloys with large electronegativity differences between the alloying elements. We predict that the absorption spectrum due to transitions between the split bands of a doped highly mismatched alloy with a conduction band anticrossing shows qualitative features revealing the fractional distribution of states in the split bands and providing valuable insight into their electronic structure. Our prediction is based on the analysis of the joint densities of states for both direct and indirect transitions between the split bands. In particular, we predict a peak near the absorption edge, which arises due to the suppression of direct transitions at large momenta. As a result of the suppression of direct transitions, indirect transitions dominate the spectrum away from the edge of absorption. We present analytic forms of the near-absorption-edge and large-energy behaviors of the spectra, comparing them with the asymptotic forms of absorption from a single deep impurity level.

p-i-n and p-n betavoltaic cells are modeled combining Monte Carlo and drift-diffusion simulations to assess device performance under a nickel-63 radioactive source. Semiconductor layer thicknesses and doping were optimized using a particle swarm optimization algorithm to maximize the power output. Simulations show that beta particles can penetrate deeply in semiconductor materials, creating electron-hole pairs up to 10 µm deep. A current gain multiplier of 6500 and 5100, compared to the beta particle flux, were demonstrated, respectively, for the optimal p-i-n devices and p-n devices. We show that the p-i-n heterostructures present an enhanced carrier collection efficiency over their p-n counterparts due to their wider depletion region, and that introducing an intrinsically doped region enables an increase in betavoltaic device power output by 9 %.

I will discuss the status of intermediate band (IB) solar cells and of a recently proposed hot carrier solar cell concept called valley photovoltaics (VPV). I will discuss the development of a trio of models, with varying computational cost, for understanding and designing advanced concept photovoltaics. I will describe Simudo, a Poisson/drift-diffusion solver for advanced PV, and use it to validate a semi-analytic model for IB devices, which allows rapid exploration of the design space. Including realistic nonradiative losses, efficient IBSCs will be easier to make with higher bandgap materials than are most commonly studied, and I will show experiments and design concepts for GaN-based IB devices. The VPV concept is that hot carriers can be maintained in and extracted from metastable valleys of the conduction band. Devices so far have JV curves with a strong S-shape and low power generation. We adapt Simudo to treat VPV and well reproduce experimental JV curves. I will discuss the various origins of the S-shape. I will introduce an equivalent circuit model for the S-shape and connect it to physics in the device, giving insight into the obstacles to achieving high efficiency.

Photonic power converters (PPCs) are photovoltaic cells that convert monochromatic light into electric power. The impact of luminescent coupling (LC) on InGaAs-based PPCs is studied. Multi-junction PPCs are simulated using an experimentally validated drift-diffusion model, and the contribution of LC is quantified. Up to 85% of the photons emitted across the InGaAs layers are re-absorbed in the dual-junction device considered. This number increases to 96% when a back reflector is included due to improved light management. Interference effects produced by multiple reflections are examined as a function of the emission angle.

We present a global analysis of luminescence and electrical measurements of valley-photovoltaic (VPV) devices using an equivalent circuit approach. VPV devices use valley scattering effects, similar to the Gunn effect, to maintain a high temperature carrier population, but existing devices have shown an S shape in their current-voltage characteristics, which results in a low fill factor. We propose an equivalent circuit that can simultaneously fit measured photoluminescence and electrical data of these VPV devices. We show that the S shape originates from a reverse diode and use a Poisson/drift-diffusion model to show that it can arise from either a heterojunction barrier or from the valley scattering process. We demonstrate that, as long as our equivalent circuit describes the VPV devices, they will not achieve high efficiency.

Dimensionality reduction (DR) has been an integral part of exploratory data analysis and feature selection in a multitude of machine learning applications. In particular, it has shown to be useful in multi-parameter photonics design problems where the objective function landscape offers a range of optimized designs. This talk will cover several recent advances in the methodology as well new design applications. Progress on sampling from data-efficient non-linear DR techniques will be presented, along with a method to reduce the computational load of data collection incurred by high fidelity solvers. New design problems in integrated silicon photonics as well as multijunction photonic power converters will be presented as the study cases.

We have developed a machine learning empowered computational framework to facilitate design space exploration for optoelectronic devices. In this work, we apply dimensionality reduction and clustering machine learning algorithms to identify optimal ten-junction C-band photonic power converter (PPC) designs. We outline our framework, design optimization procedure, calibrated optoelectronic model, and experimental calibration devices. We report on top performing device designs for on-substrate and flat back-reflector architectures. We comment on the design sensitivity for these PPCs and on the applicability of dimensionality reduction and clustering algorithms to assist in optoelectronic device design.