Abstract: As the field of perovskite optoelectronics developed, a plethora of strategies has arisen to control their electronic and morphological characteristics for the purpose of producing high efficiency devices. Unfortunately, despite this wealth of deposition approaches, the community experiences a great deal of irreproducibility between different laboratories, batches and preparation methods. Aiming to address this issue, we developed a simple deposition method based on gas quenching that yields smooth films for a wide range of perovskite compositions, in single, double, triple and quadruple cation varieties, and produces planar heterojunction devices with competitive efficiencies, so far up to 20%.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.