Manufacturing of Photovoltaic Devices, Power Electronics and Batteries for Local Direct Current Power Based Nanogrid

To meet the current and future demands of electrical power for household, industrial, commercial and transport sectors, the energy infrastructure has to undergo changes in terms of generation, distribution and consumption. Due to the shortcomings of nuclear and fossil fuel based power generation, the emergence of renewable energy has provided a very lucrative option. With the advent of low-cost photovoltaics (PV) panels and our ability to generate, store and use electrical energy locally without the need for long-range transmission, the world is about to witness transformational changes in electricity infrastructures. For local nano-grids, direct current (DC) -based system has several distinct advantages that are demonstrated through theoretical and experimental results. A PV- powered and local DC power based nano-grids can be more efficient, reliable, cyber secured, and can easily adopt internet of things (IoT) platforms. With DC generation, storage and consumption, significant amount of energy can be saved that are wasted in back and forth conversion between AC and DC. In case of geomagnetic disturbances, such nano-grids will be more resilient compared to centralized distribution network. Free-fuel, i.e. sunlight, based local DC nano-grid can be the sustainable and cost effective solution for underdeveloped, developing and developed economies. To take advantage of this, the manufacturing of PV, power electronics and batteries have to follow the best practices that aid process control, quality improvement and potential cost reduction. Without proper process control, the variation will result in yield loss, inferior performance and higher cost of production. On many instances, these issues were not considered, and some technology such as perovskite solar cell, received a lot of attention as a disruptive technology. Through detailed technical and economic assessments, it was shown that the variability and lack of rigorous process control will result in a lower efficiency when perovskite thin film solar cells are connected together to form a module. Due to stability and performance reasons, it was showed the perovskite solar cell is not ideal for 2-terminal or 4-terminal multi-junction/tandem configuration with silicon cells. Power electronics also play a vital role in PV systems. The challenges and design rules for silicon carbide (SiC) and gallium nitride (GaN) based power device manufacturing were analyzed. Based on it, advanced process control (APC) based single wafer processing (SWP) tools for manufacturing SiC and GaN power devices are proposed. For energy storage, batteries play an important role in PV installation. Li-ion technology will become the preferred storage due to its capabilities. Incorporation of advanced process control, rapid thermal processing, Industrial IoT, etc. can reduce variability, improve performance and reduce quality-check failures and bring down the cost of electrochemical batteries. The combined approaches in manufacturing of PV, power electronics and batteries will have a very positive impact in the growth of PV powered DC –based nano-grids

Design of Experiment Methods in High Speed Signal Via Transition in Printed Circuit Board

With increase in data transfer speeds between integrated circuits, memories and connectors, the interconnects in printed circuit boards require careful design and optimization. A critical part of an interconnect is via transition. To meet the design goals and maintain signal integrity, every part of the interconnect needs to be carefully designed. Designing via involves variation of several parameters and it is extremely important to understand their contribution to find the usable parameters yielding best possible performance. Out of these numerous combinations of parameters, Design of Experiments (DOE) can offer much needed understanding, suggest possible values and speed up the design process. In this manuscript, two DOE methods (Box-Behnken Design and Central Composite Design) are used to understand the behavior of a pair of vias operating in differential mode

Further Cost Reduction of Battery Manufacturing

The demand for batteries for energy storage is growing with the rapid increase in photovoltaics (PV) and wind energy installation as well as electric vehicle (EV), hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV). Electrochemical batteries have emerged as the preferred choice for most of the consumer product applications. Cost reduction of batteries will accelerate the growth in all of these sectors. Lithium-ion (Li-ion) and solid-state batteries are showing promise through their downward price and upward performance trends. We may achieve further performance improvement and cost reduction for Li-ion and solid-state batteries through reduction of the variation in physical and electrical properties. These properties can be improved and made uniform by considering the electrical model of batteries and adopting novel manufacturing approaches. Using quantum-photo effect, the incorporation of ultra-violet (UV) assisted photo-thermal processing can reduce metal surface roughness. Using in-situ measurements, advanced process control (APC) can help ensure uniformity among the constituent electrochemical cells. Industrial internet of things (IIoT) can streamline the production flow. In this article, we have examined the issue of electrochemical battery manufacturing of Li-ion and solid-state type from cell-level to battery-level process variability, and proposed potential areas where improvements in the manufacturing process can be made. By incorporating these practices in the manufacturing process we expect reduced cost of energy management system, improved reliability and yield gain with the net saving of manufacturing cost being at least 20%

Ultra Large Scale Manufacturing Challenges of Silicon Carbide and Gallium Nitride Based Power Devices and Systems

The potential of wide band gap (WBG) semiconductors for manufacturing ultra-high performance power devices and systems is well known [1]. Historically, silicon carbide research is as old as is the discovery of transistors [2]. In recent years, significant progress has been made in reducing the bulk crystal defects. As a result, for niche applications some SiC and GaN products are currently in the market. However, silicon dominates the power devices and systems market. In an earlier publication [3] we pointed out that bulk crystal defects and process induced defects are still the major road blocks in creating a SiC and GaN semiconductor based power devices and power systems market. This is due to the fact that best values of performance, reliability and yield of semiconductor products can be obtained only when the microstructure is homogenous and minimum defect density is observed [4]. It is a well-known fact in semiconductor manufacturing that other than feature size reduction, the prime reason for the success of silicon manufacturing for low power and power devices has been the reduction of defect density. Currently the defect density of SiC and GaN based products is several orders of magnitude higher than the defect density of Si based semiconductor products. The general notion in the WBG semiconductor based power devices and power system community has been that older generation of silicon manufacturing is good enough to manufacturer WBG based power devices [5]. The prime reason for the success of silicon manufacturing is that the process control of semiconductor manufacturing has evolved from classical statistical process control (SPC) to advanced process control (APC). In other words, batch processing has been replaced by single wafer processing [6]. The key objective of this paper is to demonstrate that without making major process control changes in SiC and GaN processing, these materials will have only niche markets in power semiconductor devices and systems manufacturing

Correlation filters for detection of cellular nuclei in histopathology images

Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. Availability: A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist