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Microstructural properties of thin-film absorber layers play a vital role in developing high-performance solar cells. Scanning probe microscopy is frequently used for measuring spatially inhomogeneous properties of thin-film solar cells. While powerful, the nanoscale probe can be sensitive to the roughness of samples, introducing convoluted signals and unintended artifacts into the measurement. Here, we apply a glancing-angle focused ion beam (FIB) technique to reduce the surface roughness of CdTe while preserving the subsurface optoelectronic properties of the solar cells. We compare the nanoscale optoelectronic properties “before” and “after” the FIB polishing. Simultaneously collected Kelvin-probe force microscopy (KPFM) and atomic force microscopy (AFM) images show that the contact potential difference (CPD) of CdTe pristine (peak-to-valley roughness > 600 nm) follows the topography. In contrast, the CPD map of polished CdTe (< 20 nm) is independent of the surface roughness. We demonstrate the smooth CdTe surface also enables high-resolution photoluminescence (PL) imaging at a resolution much smaller than individual grains (< 1 μm). Our finite-difference time-domain (FDTD) simulations illustrate how the local light excitation interacts with CdTe surfaces. Our work supports low-angle FIB polishing can be beneficial in studying buried sub-microstructural properties of thin-film solar cells with care for possible ion-beam damage near the surface. 

Rapid progress has been achieved in thin film CdTe solar cells, reaching a power conversion efficiency of 22.1 %. Researchers demonstrated a short-circuit current density (Jsc) of ≈ 31 mA/cm2 and a fill factor (FF) of ≈ 79 %, close to the theoretically calculated maximum values. However, the open-circuit voltage (Voc) remains below 0.9 V, much lower than the estimated Voc of 1.2 V. One strategy to improve the Voc is to implement a passivated back-contact on CdTe that can reduce the recombination by repelling minority carriers at the surface (i.e., electrons in CdTe). An aluminum oxide thin film (Al2O3) is an attractive candidate owing to its innate fixed negative charges (1012 ~ 1013 cm-2). Here, we use a patterned Al2O3 layer on CdTe to produce PERC-like CdTe solar cells (CdTe PERC). 

Rapid progress has been achieved in perovskite solar cells, improving the efficiency from 3.8 % to 25.7 % in less than a decade. However, the stability of perovskites still need to be improved before commercialization. This study reports the thermal stability of perovskites exposed to an ion beam irradiation. Such combined stressors are seen in atomic/nanoscale microscopy, where a perovskite lamella is characterized using a controlled heating/cooling stage. Focused ion beams (FIBs) are frequently used to section perovskites of interest. Previous studies proposed that high-energy electron beams could cause unexpectedly fast thermal degradation. Alternatively, the perovskite surface may be already altered during FIB processes, accelerating the deterioration. Here, we use a grazing angle argon ion (Ar+) beam directly irradiated on methyl-ammonium lead iodide (MAPbI3) to test the impact of ion beams to degradation mechanisms. 

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer’s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.