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Indium-gallium–zinc-oxide thin-film transistors (IGZO TFTs) are widely used in numerous applications including displays and are emerging as a promising alternative for flexible IC production due to their high transparency, superior field-effect mobility, and low-temperature processability. However, their stability under different voltage stresses remains a concern, primarily due to carrier trapping in the gate dielectric and point defect creation. This study involves the fabrication of IGZO TFTs and their subsequent bias stress testing in linear and saturation regions. The impact of a passivation layer on top of the active channel is investigated to mitigate bias stress susceptibility. The passivated thin-film transistors (TFTs) exhibit reduced bias stress susceptance, with $\Delta {V}_{T}$ only moderately affected by the positive gate-bias stress (PGBS). This suggests that fewer electrons are being trapped at the interface between the dielectric/semiconductor. Conventional bias stress testing methods for TFTs are time-consuming and depend on air-stable devices. To address this, we introduce a “voltage step-stress” (VSS) approach. This method offers an accelerated way to conduct bias stress measurements without compromising test accuracy, reducing testing time by 8 hours (a 45% relative reduction).

Bioderived and biodegradable electronics have the capability to reduce significantly waste electrical and electronics equipment (WEEE) and can also be applied to other sectors, where degradation to benign by-products is essential, such as marine, farming, or health monitoring. Herein, the authors report biodegradable thin-film transistors (TFTs) arrays based on zinc oxide (ZnO) active layer using molybdenum (Mo) source, drain, and gate electrodes. The developed TFTs were fabricated at room temperature onto a planarized biodegradable substrate surface and achieved an ${I}_{\text {on}}/{I}_{\text {off}}$ ratio of $\sim 4\times 10^{{6}}$ , a threshold voltage of $\sim $ 2.3 V, a field-effect mobility in the saturation region of 1.3 cm $^{{2}}\cdot \text{V}^{-{1}}\cdot \text{s}^{-{1}}$ , and a subthreshold swing of 0.3 $\text{V}\cdot $ dec $^{-{1}}$ and show stable device performance under stability tests. Based upon the successful fabrication of the ZnO TFT array, the demonstration of a UV sensor (phototransistors mode) and simple logic circuits (inverter and both NAND and NOR gate circuits) are presented. Furthermore, a method to “control” the transience was implemented by using a printed heater that could accelerate the decomposition of material, which opens potential avenue for material recovery and zero waste products.

Organic photovoltaics (OPVs) are considered one of the best-performing photovoltaic (PV) technologies from an environmental point of view. Many of the constituent materials possess low embodied energies, which can generally be processed and disposed of in a less energy-intensive manner than other PV technologies. There has been an enormous range of materials used in OPVs; however, identification of the optimal materials and device architectures that provide the best environmental profile within this large search space has yet to have been conducted. This is a non-trivial task because the selection of these materials not only impacts the environmental profile but also on the solar cell efficiency and its operational stability. Here, we have developed a methodology that enables rapid assessment of the trade-off between efficiency, stability, and embodied energy of an OPV using machine learning. To achieve this, a database of OPV data was used, which has been acquired from the literature between 2011 and 2020 and consists of 1580 device data points. Our results highlight the importance of focusing activity on particular transport layers, substrates, and active layer materials, which are discussed further in the manuscript. We demonstrate that the trained and validated models can predict, with a high degree of confidence, the efficiency, stability, and embodied energy of an OPV. The methodology set out in this work provides a means of identifying optimum device configurations in a rapid manner such that the net energy production is maximized, whereas the environmental impact of OPVs is minimized. Materials which show promise toward delivering a positive net energy are PET + barrier layers, PET (substrates), NiOx, ZrOx, CrO2, ZnO, LiF, and MoO3(transport layers). Active layer materials which show promise for delivering a positive net energy are DRCN7T, DR3TSBDT, ZnPc, PDPP4T-2F, PCDTBT (donors), and IT-4F, C61 (acceptors).

The application of data analytical approaches to understand long-term stability trends of organic photovoltaics (OPVs) is presented. Nearly 1900 OPV data points have been catalogued, and multivariate analysis has been applied in order to identify patterns, produce models that quantitatively compare different internal and external stress factors, and subsequently enable predictions of OPV stability to be achieved. Analysis of the weights associated with the acquired predictive model shows that for light stability (ISOS-L) testing, the most significant factor for increasing the time taken to reach 80% of the initial performance (T80) is the substrate and top electrode selection, and the best light stability is achieved with a small molecule active layer. The weights for damp-heat (ISOS-D) testing shows that the type of encapsulation is the primary factor affecting the degradation to T80. The use of data analytics and potentially machine learning can provide researchers in this area new insights into degradation patterns and emerging trends.

Organic photovoltaic (OPV) degradation remains a complex challenge and previous studies have shown the degradation to be a function of multiple stresses, so it can be inaccurate to predict failure rates using single stress tests. In this paper, a new testing methodology whereby multiple stresses are applied simultaneously using a “design of experiment (DOE) approach” is reported and used for predictive aging of modules. Multistress data are used for predictive aging of OPV modules under different stress levels; a general log-linear life model has been adapted and applied in order to predict the life of OPV modules and this is compared to experimental data, which show that a close estimation of simulated lifetime is obtained (within 18% accuracy). The life test models can be used for predicting aging of OPV modules in different geographic locations and could be used to account for different degradation rates due to seasonal climatic variations. Furthermore, by using the DOE data, we show how the major stress factors can be screened and their statistical significance upon degradation quantified using analysis of variance. One of the potential benefits of using this approach for OPV degradation studies is that additional factors could be added to study the impact on degradation to provide a more comprehensive study.

A machine learning approach for extracting information from organic photovoltaic (OPV) solar cell data is presented. A database consisting of 1850 entries of device characteristics, performance and stability data is utilised and a sequential minimal optimisation regression (SMOreg) model is employed as a means of determining the most influential factors governing the solar cell stability and power conversion efficiency (PCE). This is achieved through the analysis of the acquired SMOreg model in terms of the attribute weights. Significantly, the analysis presented allows for identification of materials which could lead to improvements in stability and PCE for each thin film in the device architecture, as well as highlighting the role of different stress factors in the degradation of OPVs. It is found that, for tests conducted under ISOS-L protocols the choice of light spectrum and the active layer material significantly govern the stability, whilst for tests conducted under ISOS-D protocols, the primary attributes are material and encapsulation dependent. The reported approach affords a rapid and efficient method of applying machine learning to enable material identification that possess the best stability and performance. Ultimately, researchers and industries will be able to obtain invaluable information for developing future OPV technologies so that can be realised in a significantly shorter period by reducing the need for time-consuming experimentation and optimisation.