Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Fabrication and Simulation of Perovskite Solar Cells

Since the dawning of the industrial revolution, the world has had a need for mass energy production. In the 1950s silicon solar panels were invented. Silicon solar panels have been the main source of solar energy production. They have set the standard for power conversion efficiency for subsequent generations of photovoltaic technology. Solar panels utilize light’s ability to generate an electron hole pair. By creating a PN Junction in the photovoltaic semiconductor, the electron and hole are directed in opposing layers of the solar panel generating the electric current. Second generation solar panels utilized different thin film materials to fabricate solar panels. Materials such as Cadmium Telluride, Copper Indium Gallium Selenide, and amorphous silicon. This technology is now seen commercially available around the world. In the research community a third generation of solar panel technology is being developed. Perovskites are an emerging third generation solar panel technology. Perovskites’ power conversion efficiency have increased from 3.8% to 24.2% over the span of a decade. Perovskite crystals have desirable optical properties such has a high absorption coefficient, long carrier diffusion length, and high photoluminescence. The most prominent types of perovskites for solar cell research are organic metal halide perovskites. These perovskites utilize the desirable properties of organic electronics. Electrochemical techniques such as additives, catalysts, excess of particular chemicals, and variations in antisolvents impact the electronic properties of the perovskite crystal. The perovskite is however on layer of the device. Solar cell devices incorporate multiple layers. The materials for the electron transport layer, hole transport material, and choice of metal electrode have an impact on device performance and the current voltage relationship. Current silicon photovoltaic devices are more expensive than conventional fossil fuel. Modeling perovskite solar cells in a simulated environment is critical for data analytics, real fabrication behavior projection, and quantum mechanics of the semiconductor device. Photovoltaic semiconductors are diodes which produce a current when exposed to light. The ideality factor is a parameter which tells how closely a semiconductor behaves to an ideal diode. In an ideal diode, the only mechanism for hole electron recombination is direct bimolecular recombination. Because there are multiple mechanisms of recombination, there are no real devices with a perfect ideality factor. The types of recombination occurring within a device can be inferred by its ideality factor. In this research. Analyzing fabricated perovskite solar cells using their ideality factor can indicate which type of recombination is dominant in the device. The interaction between the perovskite crystal and transport layers is of high interest as differentials in energy level bands can hinder overall power conversion efficiency and act as a site for nonradiative recombination loss. In addition, the use of Machine Learning (ML) to research and predict the opto-electronic properties of perovskite can greatly accelerate the development of this technology. ML techniques such as Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) can greatly improve the chemical processing and manufacturing techniques. Such tools used to improve this technology have major impacts for the further proliferation of solar energy on a national scale. These tools can also be used to optimize power conversion efficiency of perovskites, This optimization is critical for commercial use of perovskite solar panel technology. Various electrochemical and fabrication strategies are currently being researched in order to optimize power conversion efficiency and minimize energy loss. There are current results which suggest the addition of particular ions in the perovskite crystal have a positive impact on the power conversion efficiency. The qualities of the cell such as crystallinity, defects, and grain size play important roles in the electrical properties of the cell. Along with the quality of the perovskite crystal, its interfacing with the transport layers plays a critical role in the operation of the device. In this thesis, perovskite solar cells are fabricated and simulated to research their optoelectronic properties. The optoelectronic behavior of simulated solar cells is manipulated to match that or cells. By researching this new optoelectronic material in a virtual environment, applicability and plausibility are demonstrated. This legitimizes the continued research of this third-generation solar panel material

Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells

The emerging non-crystalline silicon (c-Si) solar cell technologies are starting to make significant inroads into solar cell markets. Most of the researchers have focused on c-Si solar cell in maximum power points tracking applications of photovoltaic (PV) systems. However, the characteristics of non-c-Si solar cell technologies at maximum power point (MPP) have different trends in current???voltage characteristics. For this reason, determining the optimum operating point is very important for different solar cell technologies to increase the efficiency of PV systems. In this paper, it has been shown that the use of fuzzy system coupled with a discrete wavelet network in Takagi???Sugeno type model structure is capable of identifying the MPP voltage of different non-c-Si solar cells with very high accuracy. The performance of the fuzzy-wavelet network (FWN) method has been compared with other ANN structures, such as radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN). The simulation results show that the single FWN architecture has superior approximation accuracy over the other methods and a very good generalization capability for different operating conditions and different technologie

Characterization and Optimization of Radiation at Nano Scale: Applications in Solar Cell Design

High energy needs and environmental concerns associated with fossil fuels have raised the demand for efficient and clean alternatives of power generation. Solar cell technology is one of the most promising options of reliable renewable power sources despite high costs. Thin film solar cells offer the potential for reduction in the cost per kilowatt-hour due to the lower material usage. Nevertheless, most thin film solar cells suffer from low efficiency, though advancements in the science of near field radiation have led to substantial improvements in their optical efficiency. Many design challenges remain to be overcome for the wide-scale commercialization of thin film solar cells. In this dissertation, a numerical study is conducted for optical, optoelectrical and scattering performance enhancement of subwavelength optical devices (i.e., thin film solar cells and light trapping nanoparticles). The proposed design framework of thin film solar cells is based on learning based optimization and characterization methods, which utilize approximations of time consuming simulations. Additionally, a free form nanoparticle design procedure using evolutionary shape optimization is detailed. The background of thin film solar cells and a comprehensive literature review of the thin film solar cell design approaches are provided in Chapters 2 and 3, respectively. The optical enhancement of thin film solar cells using nanoparticles with different shapes is studied in Chapter 4. In Chapter 5, an approximate formulation for optoelectrical efficiency of thin film solar cells is developed to accelerate the design optimization. The learning based design methodology that is introduced in Chapter 5 is further improved in Chapter 6 using a knowledge transfer concept (also known as transfer learning). In this chapter, multiple sets of material combinations are optimized and compared with each other in terms of their optoelectrical efficiencies. In Chapter 7, nanoparticles are designed for maximum scattering, which is desired for enhanced optical performance, using a nonparametric evolutionary design method. In Chapter 8, a predictive model for scattering of arbitrarily shaped nanoparticles using descriptive geometric features is proposed. Overall, this dissertation has led to significant contributions in the field of thin film solar cell design. The results show that the computational burden of the thin film solar cell design can be overcome significantly without sacrificing accuracy. Furthermore, the design methods developed for this dissertation can easily be transferred to other engineering areas involving repetitive, time consuming simulations for design optimization, such as other photonic design problems and integrated circuit design

Multilayer Plasmonic Nanostructures for Improved Sensing Activities Using a FEM and Neurocomputing-Based Approach

In order to obtain optimized elementary devices (photovoltaic modules, power transistors for energy efficiency, high-efficiency sensors) it is necessary to increase the energy conversion efficiency of these devices. A very effective approach to achieving this goal is to increase the absorption of incident radiation. A promising strategy to increase this absorption is to use very thin regions of active material and trap photons near these surfaces. The most effective and cost-effective method of achieving such optical entrapment is the Raman scattering from excited nanoparticles at the plasmonic resonance. The field of plasmonics is the study of the exploitation of appropriate layers of metal nanoparticles to increase the intensity of radiation in the semiconductor by means of near-field effects produced by nanoparticles. In this paper, we focus on the use of metal nanoparticles as plasmonic nanosensors with extremely high sensitivity, even reaching single-molecule detection. The study conducted in this paper was used to optimize the performance of a prototype of a plasmonic photovoltaic cell made at the Institute for Microelectronics and Microsystems IMM of Catania, Italy. This prototype was based on a multilayer structure composed of the following layers: glass, AZO, metal and dielectric. In order to obtain good results, it is necessary to use geometries that orthogonalize the absorption of light, allowing better transport of the photocarriers—and therefore greater efficiency—or the use of less pure materials. For this reason, this study is focused on optimizing the geometries of these multilayer plasmonic structures. More specifically, in this paper, by means of a neurocomputing procedure and an electromagnetic fields analysis performed by the finite elements method (FEM), we established the relationship between the thicknesses of Aluminum-doped Zinc oxide (AZO), metal, dielectric and their main properties, characterizing the plasmonic propagation phenomena as the optimal wavelengths values at the main interfaces AZO/METAL and METAL/DIELECTRIC

Towards Oxide Electronics:a Roadmap

At the end of a rush lasting over half a century, in which CMOS technology has been experiencing a constant and breathtaking increase of device speed and density, Moore's law is approaching the insurmountable barrier given by the ultimate atomic nature of matter. A major challenge for 21st century scientists is finding novel strategies, concepts and materials for replacing silicon-based CMOS semiconductor technologies and guaranteeing a continued and steady technological progress in next decades. Among the materials classes candidate to contribute to this momentous challenge, oxide films and heterostructures are a particularly appealing hunting ground. The vastity, intended in pure chemical terms, of this class of compounds, the complexity of their correlated behaviour, and the wealth of functional properties they display, has already made these systems the subject of choice, worldwide, of a strongly networked, dynamic and interdisciplinary research community. Oxide science and technology has been the target of a wide four-year project, named Towards Oxide-Based Electronics (TO-BE), that has been recently running in Europe and has involved as participants several hundred scientists from 29 EU countries. In this review and perspective paper, published as a final deliverable of the TO-BE Action, the opportunities of oxides as future electronic materials for Information and Communication Technologies ICT and Energy are discussed. The paper is organized as a set of contributions, all selected and ordered as individual building blocks of a wider general scheme. After a brief preface by the editors and an introductory contribution, two sections follow. The first is mainly devoted to providing a perspective on the latest theoretical and experimental methods that are employed to investigate oxides and to produce oxide-based films, heterostructures and devices. In the second, all contributions are dedicated to different specific fields of applications of oxide thin films and heterostructures, in sectors as data storage and computing, optics and plasmonics, magnonics, energy conversion and harvesting, and power electronics

OPTIMISATION OF SPECTRAL PROPERTIES OF NANOPHOTONIC STRUCTURES

The optimisation of the spectral properties of nanophotonic structures is of great importance as it enables precise control over light-matter interactions, leading to improved performance in various applications such as radiative cooling, visible colour generation, spectral filtering, photovoltaics, and sensing, imaging, and light-emitting devices. This thesis focuses on optimising the spectral properties to achieve two distinct functionalities: designing a broadband selective radiative cooler and a narrowband selective visible appearance coloured structure. Additionally, the thermal regulation capability of an aramid fabric was explored to demonstrate its potential for personal thermal management. The thesis begins with an introduction and a review of recent developments in plasmonic and dielectric-coloured structures that manipulate optically generated resonances to produce vivid colours. It initially demonstrates that spectra with a Lorentzian profile can achieve high-performance colours with high purity and a broad colour gamut. Then, a combination of a symmetry-broken structure and an index-matched anti-reflective layer, supporting high-Q resonance, was demonstrated to enhance the colour impression while suppressing the contribution of higher-order modes outside the main resonance peak. The thesis then explores nanophotonic spectral control, specifically focusing on designing a passive radiative cooler. It reviews recent progress in daytime radiative-cooling technology and identifies the constraints hindering emitter performance enhancement. To address these constraints, the thesis demonstrates the design of a broadband thermal emitter that satisfies the stringent requirements of passive radiative cooling. The design parameters are optimised using both conventional optimisation algorithm and deep-learning models. The theoretical analysis shows that the conventional method is computationally expensive and resource-intensive, requiring multiple iterations to achieve the desired response. To overcome the limitations, an auto ML-based convolutional neural network was identified as a more robust method for predicting optimal design than other networks. Finally, the thesis experimentally investigates a wearable fabric’s optical and thermal properties to assess its suitability for providing local thermal comfort. The thermal-regulation ability of the fabric was validated based on a heat-transfer model. A performance analysis shows that the fabric is suitable for use in round-the-clock thermal management, trapping body heat inside during colder nights and releasing excess heat during intense sunny days. The findings of this thesis have the potential to pave the way for further developments in the spectral control of nanophotonic structures

Deep learning in light-matter interactions

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light-matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics