We report on measurement of dielectric constant, mid-gap defect density, Urbach energy of tail states in CH 3 NH 3 PbI x Cl 1Àx perovskite solar cells. Midgap defect densities were estimated by measuring capacitance vs. frequency at different temperatures and show two peaks, one at 0.66 eV below the conduction band and one at 0.24 eV below the conduction band. The attempt to escape frequency is in the range of 2 Â 10 11 /s. Quantum efficiency data indicate a bandgap of 1.58 eV.

Pyroelectric coefficients were measured for 20 nm thick crystalline hafnium zirconium oxide (Hf1-xZrxO2) thin films across a composition range of 0 ≤ x ≤ 1. Pyroelectric currents were collected near room temperature under zero applied bias and a sinusoidal oscillating temperature profile to separate the influence of non-pyroelectric currents. The pyroelectric coefficient was observed to correlate with zirconium content, increased orthorhombic/tetragonal phase content, and maximum polarization response. The largest measured absolute value was 48 μCm−2 K−1 for a composition with x = 0.64, while no pyroelectric response was measured for compositions which displayed no remanent polarization (x = 0, 0.91, and 1).

Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.

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We apply computer vision and machine learning methods to analyze two datasets of microstructural images. A transfer learning pipeline utilizes the fully connected layer of a pre-trained convolutional neural network as the image representation. An unsupervised learning method uses the image representations to discover visually distinct clusters of images within two datasets. A minimally supervised clustering approach classifies micrographs into visually similar groups. This approach successfully classifies images both in a dataset of surface defects in steel, where the image classes are visually distinct and in a dataset of fracture surfaces that humans have difficulty classifying. We find that the unsupervised, transfer learning method gives results comparable to fully supervised, custom-built approaches.