arXiv subjects

Manuel Kober-Czerny

Publications and source records attributed to Manuel Kober-Czerny.

5 recordsLinked to original sources

arXiv2026

Accelerated development of amorphous InZnO thin films as transparent conductive Cu diffusion barriers

In light of the increasing supply chain concerns regarding silver for solar cell metallization, the replacement of the silver contacts by copper is desirable. As copper diffuses readily in silicon, deposition of an additional diffusion barrier to protect the respective absorber material stacks is required. We investigate multifunctional layers of transparent conductive oxides (TCOs) from the In-Zn-O system to serve as front electrode and Cu diffusion barrier coating, focusing on purely amorphous layers without grain boundaries to impede copper diffusion. We employ a 2D combinatorial approach to simultaneously screen the Zn/(In+Zn) ratio and the oxygen content in a single materials library deposited by magnetron sputtering without intentional substrate heating. Cu diffusion barrier performance was evaluated by depositing Cu on top of intentionally ultrathin In-Zn-O libraries of 7 nm on silicon wafers and annealing them at temperatures of 200-450°C. Both the formation of copper silicides, as well as the silicon photoluminescence signal were monitored. The first was detected only after the crystallization of the In-Zn-O films and required annealing temperatures of 450°C and above. Even for extended dwell times of 20 h at a relevant process temperature of 200°C, we find no evidence of Cu ingress for most of our fabricated In-Zn-O compositions, whereas Si/Cu stacks without In-Zn-O barriers showed a reduction of their photo-luminescence intensity already after less than 1 h. These results suggest thin amorphous In-Zn-O films with an optimal Zn/(In+Zn) ratio of ~0.12 and intermediate oxygen deficiency as effective transparent conductive Cu diffusion barriers for solar cell applications.

cond-mat.mtrl-sci↗

arXiv2026

Disentangling the origin of degradation in perovskite solar cells via optical imaging and Bayesian inference

Machine learning and computational inference, coupled with experimental data, promise to significantly accelerate our rate of learning in most scientific disciplines. In this study, we develop tools that connect microscopic observations to macroscopic device behaviour, a capability that is essential for accelerating the design of durable energy materials. To this end, we introduce a novel approach that integrates photoluminescence imaging with drift diffusion simulations to understand operation and degradation in fully fabricated perovskite solar cells. By employing Bayesian inference, we generate "inferred maps" of parameters that govern recombination processes present in devices. We track these parameter maps while the devices are aged (70 °C, full spectrum sunlight) to analyse their temporal evolution during degradation. Notably, our approach allows us to distinguish between degradation occurring at the hole or electron transporting layer interface, or within the bulk. Our analysis reveals pronounced spatially non-uniform degradation, with significant macroscopic heterogeneity observed in the optoelectronic parameter maps. We pinpoint the greatest degradation observed in specific regions to stem from the perovskite/transport layer interfaces. Finally, we demonstrate that an amino-silane molecular passivation treatment suppresses this degradation, highlighting its specific role in enhancing device stability. Our approach offers valuable insights for future device fabrication and is a clear exemplification of how advanced Bayesian inference can significantly increase the value of experimental data.

cond-mat.mtrl-sci↗

arXiv2026

Physics-informed time-series forecasting of perovskite photoluminescence stability

Accelerated ageing using elevated temperatures and illumination is one of the most common methods to rapidly study the stability of novel semiconductor materials. However, as the pace of materials discovery continues to accelerate, even faster stability evaluations are needed. A physics-informed time-series forecasting algorithm designed to predict the long-term photoluminescence stability of metal halide perovskites is presented. A diverse experimental dataset of 167 metal halide perovskites is collected, including different crystallinities and compositions. These are stressed using heat and light, while the photoluminescence (PL) is monitored. The >86k collected PL spectra are featurized using a physics-informed model, and a hybrid CNN-LSTM model is trained to forecast the PL intensity during degradation of samples unseen during model training. Notably, the approach generalizes across the material groups and outperforms baseline benchmarks. Furthermore, the physics-based featurization ensures explainability, enabling analysis to identify critical stability descriptors for given predictions. It is expected that this approach will be adapted to other types of time-series data and enables a pathway to significantly reduce experimental testing times.

cond-mat.mtrl-sci↗

arXiv2026

Boron Co-Alloying in AlScN Wurtzite Ferroelectrics: Insights from an 850-Sample Combinatorial Study

AlScN wurtzite ferroelectrics are promising candidates for energy-efficient non-volatile memory. However, AlScN suffers from a high coercive field and reduced cycling endurance, and the limited tunability of its properties constrains further optimization. Co-doping AlScN with boron offers the promise of independently tailoring the chemical and structural properties, making AlScBN an attractive quaternary system. This material has already been explored for a few selected compositions, however, no systematic study of the full AlScBN compositional space exists. A combinatorial approach consisting of gradient deposition with HiPIMS at low temperatures of 250°C and automatic analysis of film properties allowed us to analyze a total of 850 unique samples within the AlScBN phase space. In addition to a full screening of the materials' chemical and structural properties, we fabricate and characterize combinatorial device libraries. XPS charge transfer analysis experimentally confirms that bond ionicity correlates with a reduction in the coercive field for AlScN and AlScBN systems, opposite trends are instead observed for AlBN. While the films maintain a high remanent polarization of 130-150 μC/cm2, Sc and B co-doping reduces the coercive field from 7 MV/cm to 3 MV/cm. Notably, B co-alloying lowers the amount of Sc needed to lower the coercive field, reducing reliance on this scarce element. In addition, we find that co-alloying with B, notably improves cycling endurance, which is related to a reduction in defect density. These results establish AlScBN as a scalable, CMOS-compatible ferroelectric, positioning it as an interesting alternative to AlScN.

cond-mat.mtrl-sci↗

arXiv2022

Robust excitons across the phase transition of two-dimensional hybrid perovskites

Two-dimensional halide perovskites are among intensely studied materials platforms profiting from solution based growth and chemical flexibility. They feature exceptionally strong interactions among electronic, optical as well as vibrational excitations and hold a great potential for future optoelectronic applications. A key feature for these materials is the occurrence of structural phase transitions that can impact their functional properties, including the electronic band gap and optical response dominated by excitons. However, to what extent the phase-transitions in two-dimensional perovskites alter the fundamental exciton properties remains barely explored so far. Here, we study the influence of the phase transition on both exciton binding energy and exciton diffusion, demonstrating their robust nature across the phase transition. These findings are unexpected in view of the associated substantial changes of the free carrier masses, strongly contrast broadly considered effective mass and drift-diffusion transport mechanisms, highlighting the unusual nature of excitons in two-dimensional perovskites.

cond-mat.mes-hall↗