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In this work we evaluate the state of the semiconductor manufacturing industry and its challenges and trends. Future trends in the industry are analyzed from three perspectives: the evolution of Industry 4.0, the advances in semiconductor materials, and the impact of the Covid-19 Pandemic. The semiconductor manufacturing industry witnessed an acute decline in the United States and other regions in the two decades prior to the CoVid-19 pandemic. The decline was only uncovered after the chip shortage of 2021 that resulted from the severe supply chain disruption. Trends in the industry were analyzed from three perspectives: Industry 4.0, advances in materials, and the Post-pandemic era. As a result of the evolution of the fourth generation of industry (Industry 4.0), trends in semiconductor manufacturing include robotization, which caused the industry to become the largest market for industrial robotics since 2020, and an all-time peak globalization. The semiconductor industry is a very globalized industry with corporates from different parts of the world taking part in the production of the final product. Although some materials such as carbon and Gallium Nitride show promising trends to replace silicon as the material of choice. It will likely not be before two or three decades when a semiconductor material will be able to replace silicon. Challenges for the industry include the shortage of the trained-workforce, and the added inter-country restrictions that may hinder the globalization of the industry. 

In this paper, we present the results of the evaluation conducted for six train-the-trainer workshops on intelligent industrial robotics that were organized over three years from 2021 to 2023. The workshops targeted STEM faculty of community and technical colleges and high schools. The workshops included factory tours, industry speakers, and hands-on activities on industrial robots and vision system programming. Evaluation of the effectiveness of the workshops was measured using surveys at the end of the workshops, as well as pre-and post-intervention assessments. A six-month follow-up survey was conducted to assess the impact of the workshops on students. Results show that most participants reported that their knowledge of intelligent industrial robotics increased and that the knowledge gained from the workshops is applicable to their work. In addition to that, statistical calculations show that 3,572 ± 1,286  students were impacted by the workshops six months after the workshop completion with a confidence level of 90%. 

Abstract One of the characteristic features of the next-generation of Industry 4.0 is human-centricity, which in turn includes two technological advancements: Artificial Intelligence and the Industrial Metaverse. In this work, we assess the impact that AI played on the advancement of three technologies that emerged to be cornerstones in the fourth generation of industry: intelligent industrial robotics, unmanned aerial vehicles, and additive manufacturing. Despite the significant improvement that AI and the industrial metaverse can offer, the incorporation of many AI-enabled and Metaverse-based technologies remains under the expectations. Safety continues to be a strong factor that limits the expansion of intelligent industrial robotics and drones, whilst Cybersecurity is effectively a major limiting factor for the advance of the industrial metaverse and the integration of blockchains. However, most research works agree that the lack of the skilled workforce will no-arguably be the decisive factor that limits the incorporation of these technologies in industry. Therefore, long-term planning and training programs are needed to counter the upcoming shortage in the skilled workforce. 

In this work we investigate the electrical properties of phospholipid bilayer membranes (LBMs) formed from phosphatidylserine by analyzing two experimental setups. The Electrochemical Impedance Spectra (EIS) of phosphatidylserine show that a lipid bilayer membrane formed from this phospholipid has an average specific electrical resistance of 3.466 k Ω.cm 2 and an average capacitance of 0.385 µF/cm 2 . Some of the major factors that affect the LBM resistance include electroporation, the method of deposition, and the surface tension in microchannels for supported LBMs. Therefore, wide apertures remain the most accurate method for supporting LBMs. 

A low-cost remote supervisory control capability is added to a packaging process, in which a low-voltage signal is used to communicate between a distant HMI control panel and a PLC network using the AC power line as a communication medium. The network is a star-topology and uses a Mater-slave protocol. Remote Supervisory control is achieved using a user-defined toolbox of control functions. In this system, a Programmable Logic Controller (PLC) is used to control a process and interface with the operator through a Human Machine Interface (HMI) Panel. A star topology ethernet network is used to connect the PLCs and the HMI panel. 

In this paper we demonstrate two applications of a low-cost remote supervisory control and data acquisition system in two models. The first model is demonstrated with a Profibus-DP protocol based system in which a master Programmable Logic Controller (PLC) unit with control inputs and display outputs controls the speed and monitors the overload condition of a DC motor that is connected to a slave PLC in real time. In the upgraded model, a Profinet protocol is used to connect PLCs, and a power-line communication link is used to remotely connect the control HMI to the network. In both models, remote Supervisory control is achieved using user-defined control functions that act altogether as a block-oriented function library or toolbox. High levels of performance are achieved in real time control and data acquisition in both models.