Search arXivSearch

arXiv · 2308.01132

Integrating transfer matrix method into SCAPS-1D for addressing optical losses and per-layer optical properties in perovskite/Silicon tandem solar cells

Abstract

SCAPS-1D software ignores optical losses and recombination junction (RJ) layer in studying tandem solar cells (TSCs). This paper presents an optoelectronic study of a perovskite/Silicon TSC, comparing the effects of using two different methods of calculating filtered spectra on the photovoltaic performance parameters of tandem device. It is shown that integrating transfer matrix (TM) method into SCAPS-1D addresses per-layer optical losses and provides a platform for optimizing the RJ layer in TSCs. Using Beer-Lambert (BL) method for calculating the filtered spectra transmitted from the perovskite top sub-cell is revealed to overestimate the cell efficiency by ~4%, due to its inability to fully address optical losses. Also, the BL method fails to tackle any issues regarding optical improvement through ITO ad-layer on the RJ. Using TM formalism, the efficiency of the proposed perovskite/Silicon TSC is shown to be increased from 19.81% to 23.10%, by introducing the ITO ad-layer on the RJ. It is the first time that the effect of filtered spectrum calculation method is clearly investigated in simulating TSCs with SCAPS-1D. The results pave the way to introduce the optical loss effects in SCAPS-1D and demonstrate that the BL method that has been used before needs to be revised.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Peymaneh Rafieipour, Aminreza Mohandes, Mohammad Moaddeli, Mansour Kanani. 2023-08-02. Integrating transfer matrix method into SCAPS-1D for addressing optical losses and per-layer optical properties in perovskite/Silicon tandem solar cells. https://arxiv.org/abs/2308.01132

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Active Fourier Spectroscopy: From fixed sampling protocols to adaptive measurement

Fourier-transform spectroscopy (FTS) is based on the Nyquist-Shannon theorem, which dictates a strict sampling protocol for the measurement of continuous signals. Here we introduce Active Fourier Spectroscopy (AFS), a generalization of FTS in which measurements are still taken in the Fourier-conjugate delay domain, but where the most informative acquisition points are selected adaptively and the spectrum is reconstructed by sequential, uncertainty-aware inference rather than by fixed-grid Fourier inversion. Our approach relies on a theoretical insight that the Nyquist condition applies only under complete ignorance about the system under investigation, and shows how to systematically improve upon it when prior knowledge exists. Because AFS recovers conventional FTS whenever the prior is uninformative, its performance never falls below that of conventional sampling. We demonstrate AFS for optical spectroscopy in three settings vital to research and medicine: clinical FTIR-based blood plasma analysis, optical metrology of structured laser beams, and hyperspectral imaging with an RGB camera. In each case AFS reaches a higher target reconstruction quality from far fewer acquisitions and provides complete uncertainty quantification in real time, propagated rigorously through downstream analysis.

physics.optics

Memory in Integrated Photonic Neural Networks: From Physical Mechanisms to Neuromorphic Architectures

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as computational capabilities outpace the ability of memory and interconnects to supply and retrieve data. In contrast, biological neural systems inherently co-localize computation and memory through distributed, dynamical processes. Neuromorphic computing seeks to emulate this paradigm by leveraging physical substrates whose intrinsic dynamics simultaneously encode and process information. Among emerging platforms, silicon photoncis offer a compelling approach due to its high bandwidth, low-loss propagation, and inherent parallelism. This review examines the role of memory in integrated photonic neuromorphic systems, with emphasis on the physical mechanisms that provide volatile (short-term) and non-volatile (long-term) memory in silicon-on-insulator and hybrid silicon-on-insulator platforms. Drawing inspiration from digital, biological, and photonic memory architectures, we classify existing approaches based on their underlying physical principles. We cover implementations ranging from delay lines and slow-light structures to multistable dynamics and structural memory based on charge trapping and phase-change materials. We then discuss how these mechanisms support photonic neural network architectures, including feed-forward, reservoir computing, spiking and hybrid optoelectronic recurrent systems, and assess their relevance for time-dependent singal-processing tasks such as channel equalization in telecommunications. This review aims to establish a unified framework for understanding memory and learning in neuromorphic photonics and outlines key challenges and opportunities for scalable, energy-efficient neuromorphic hardware.

physics.optics

Exoplanet Detection Using Adaptive Quantum-Optimal Measurement

Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be \(10^8\) to \(10^{10}\) times fainter than their host stars and lie at diffraction-limited angular separations, where starlight strongly obscures the companion signal. Here we present an adaptive quantum measurement method for estimating the number, positions, and brightnesses of mutually incoherent point sources in the sub-Rayleigh, ultra-high-contrast regime, operating at contrasts down to \(10^{-8}\) -- five orders of magnitude beyond previous quantum imaging approaches to exoplanet detection. The method adopts a spatial-mode basis that is updated to maximize the quantum Fisher information per detected photon. Estimation is performed by maximum likelihood in log-brightness coordinates, and the source count is determined by Bayesian-information-criterion (BIC) model selection directly from photon-count statistics, without a tunable detection threshold. For point sources within sub-Rayleigh separations and with brightness ratios spanning eight orders of magnitude, the method reconstructs complete scenes with a mean success rate of \(72.5\%\). Furthermore, it is robust to misalignment, maintaining a \(71.3\%\) success rate under offsets of up to six pixels. These results demonstrate that terrestrial exoplanets can be detected below the Rayleigh limit, a regime previously inaccessible to direct imaging.

physics.optics