Defect tolerant device geometries

The term defect tolerance is widely used in literature to describe materials such as lead-halides which exhibit long non-radiative lifetimes of carriers despite possessing a large concentration of point defects. Studies on defect tolerance of materials mostly look at the properties of the host material and/or the chemical nature of defects that affect the capture coefficients of defects. However, the recombination activity of a defect is not only a function of its capture coefficients alone but are also dependent on the electrostatics and the design of the layer stack of a photovoltaic device. Here we study the influence of device geometry on defect tolerance by combining calculations of capture coefficients with device simulations. We derive generic device design principles which can inhibit recombination inside a photovoltaic device for a given set of capture coefficients based on the idea of slowing down the slower of the two processes (electron and hole capture) even further by modifying electron and hole injection into the absorber layer. We use the material parameters and typical p-i-n device geometry representing methylammonium lead halide perovskites solar cells to illustrate the application of our generic design principles to improve specific devices .Comment: 27 pages, 9 Figure

Vision-driven Autocharacterization of Perovskite Semiconductors

In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly accelerating the throughput of the workflow for scientists. We present a framework that uses vision to address specific pain points in the characterization of perovskite semiconductors, a group of materials with the potential to form new types of solar cells. With this approach, we automate the measurement and computation of chemical and optoelectronic properties of perovskites. Our framework proposes the following four key contributions: (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. We demonstrate the key contributions of the proposed framework on eighty samples of unique composition from the formamidinium-methylammonium lead tri-iodide perovskite system and validate the accuracy of each method using human evaluation and X-ray diffraction.Comment: Manuscript 8 pages; Supplemental 7 page

BayesMC

BayesMC : Parameter estimation using Bayesian inference with Markov Chain Monte Carl

Transforming characterization data into information in the case of perovskite solar cells

In many emerging solar cell technologies, it is a significant challenge to extract the electronic properties of materials and interfaces inside a working device from experimental data. In many cases, approaches frequently used in mature technologies such as crystalline silicon are inapplicable as they require many material parameters to be known a-priori , which is rarely the case for novel materials. Based on this challenge for material and device characterization, this perspective discusses the different strategies for data interpretation that have been developed or are in the process of being developed for the specific case of halide perovskite solar cells. The specific focus of this work is to discriminate between experimental data and strategies to extract useful information from data. This information can then be used to make informed decisions about strategies for process and material innovations

What is a deep defect? Combining Shockley-Read-Hall statistics with multiphonon recombination theory

Slow nonradiative recombination is a key factor in achieving high open-circuit voltages or high luminescence yields in any optoelectronic material. Whether a defect is contributing substantially to nonradiative recombination is often estimated by defect statistics based on the model by Shockley, Read, and Hall. However, defect statistics are agnostic to the origin of the capture coefficients and therefore conclude that essentially every defect between the two quasi-Fermi levels is equally likely to be a recombination-active defect. Here, we combine Shockley-Read-Hall statistics with microscopic models for defect-assisted recombination to study how the microscopic properties of a material affect how recombination active a defect is depending on its energy level. We then use material parameters representative of typical photovoltaic absorber materials (CH3NH3PbI3, Si, and GaAs) to illustrate the relevance, but also the limitations of our model

Effect of Doping, Photodoping, and Bandgap Variation on the Performance of Perovskite Solar Cells

Most traditional semiconductor materials are based on the control of doping densities to create junctions and thereby functional and efficient electronic and optoelectronic devices. The technology development for halide perovskites had initially only rarely made use of the concept of electronic doping of the perovskite layer and instead employed a variety of different contact materials to create functionality. Only recently, intentional or unintentional doping of the perovskite layer is more frequently invoked as an important factor explaining differences in photovoltaic or optoelectronic performance in certain devices. Here, numerical simulations are used to study the influence of doping and photodoping on photoluminescence quantum yield and other device relevant metrics. It is found that doping can improve the photoluminescence quantum yield by making radiative recombination faster. This effect can benefit, or harm, photovoltaic performance given that the improvement of photoluminescence quantum efficiency and open-circuit voltage is accompanied by a reduction of the diffusion length. This reduction will eventually lead to inefficient carrier collection at high doping densities. The photovoltaic performance may improve at an optimum doping density which depends on a range of factors such as the mobilities of the different layers and the ratio of the charge carrier capture cross sections

Effect of Doping, Photodoping, and Bandgap Variation on the Performance of Perovskite Solar Cells

Most traditional semiconductor materials are based on the control of doping densities to create junctions and thereby functional and efficient electronic and optoelectronic devices. The technology development for halide perovskites had initially only rarely made use of the concept of electronic doping of the perovskite layer and instead employed a variety of different contact materials to create functionality. Only recently, intentional or unintentional doping of the perovskite layer is more frequently invoked as an important factor explaining differences in photovoltaic or optoelectronic performance in certain devices. Here, numerical simulations are used to study the influence of doping and photodoping on photoluminescence quantum yield and other device relevant metrics. It is found that doping can improve the photoluminescence quantum yield by making radiative recombination faster. This effect can benefit, or harm, photovoltaic performance given that the improvement of photoluminescence quantum efficiency and open-circuit voltage is accompanied by a reduction of the diffusion length. This reduction will eventually lead to inefficient carrier collection at high doping densities. The photovoltaic performance may improve at an optimum doping density which depends on a range of factors such as the mobilities of the different layers and the ratio of the charge carrier capture cross sections

Vision-driven Autocharacterization of Perovskite Semiconductors

In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly accelerating the throughput of the workflow for scientists. We present a framework that uses vision to address specific pain points in the characterization of perovskite semiconductors, a group of materials with the potential to form new types of solar cells. With this approach, we automate the measurement and computation of chemical and optoelectronic properties of perovskites. Our framework proposes the following four key contributions: (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. We demonstrate the key contributions of the proposed framework on eighty samples of unique composition from the formamidinium-methylammonium lead tri-iodide perovskite system and validate the accuracy of each method using human evaluation and X-ray diffraction