Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >10⁶ cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

Advanced data analysis tools and techniques are important for semiconductor companies to gain competitive advantage. In particular, yield prediction tools, which fully utilize production data, help to improve operational efficiency and reduce production costs. This paper introduces a novel and scalable framework for semiconductor manufacturing Final Test (FT) yield prediction leveraging machine learning techniques. This framework is able to predict FT yield at wafer fabrication stage, so that FT low yield problems can be caught at an earlier production stage compared to past studies. Our work presents a robust solution to automatically handle both numerical and categorical production related data without prior knowledge of the low yield root cause. Gaussian Mixture Models, One Hot Encoder and Label Encoder techniques are adopted for data pre-processing. To improve model performance for both binary and multi-class classification, model selection and model ensemble using the F1-macro method is demonstrated. The framework has been applied to three mass production products with different wafer technologies and manufacturing flows. All of them achieved high F1-macro test score indicative of the robustness of our framework.

Nanostructures are playing significant roles in the development of new functions and the enhancement of the existing functions of industrial devices such as sensors, transistors, diodes, lithium-ion batteries, and photovoltaic cells, due to their piezoelectricity, biocompatibility, and pyroelectrical characteristics. This research focused on the review of the characteristics of different nanostructures and nanomaterials such as ZnO, ZnS, MoS₂, GO, TiO₂, SnO₂, and Fe₂O₃, their fabrication techniques, growth mechanisms, and applications. The factors affecting the growth mechanisms, the crystallographic natures, growth models of anisotropic nanostructures, and growth of nanocrystals, were also highlighted in this research. The existence of lattice mismatch, differential thermal expansion, and high deposition temperature, have affected uniform deposition of nanoparticles on substrates and caused heteroepitaxy, which has resulted in defective nanostructures. Although heteroepitaxy has negatively affected the characteristics and potential applications of nanostructures, it has also opened new research frontiers for potential new applications of nanostructures. The use of nanostructures for gas sensing is attributed to the high specific area, change of resistance on exposure to gases, and high photoconduction abilities, while the photon-carrier collection abilities and anti-reflectance qualities are vital for solar photovoltaic cells. Nanostructures have also been used as coating pigments to prevent corrosion of facilities, reduce urban heat islands and energy consumptions, due to the near infrared (NIR) reflective characteristics.