Partial Discharge Mitigation in Power Modules using an Automation-Driven Design Rule Development Method

Power modules used for the conversion and conditioning of electrical power for applications like electric vehicles, more-electric aircraft, the power grid, etc., are largely designed manually by engineers. Design automation of power modules is starting to gain recognition as a timely and necessary alternative to intuitive manual design and fabrication. With increasing need for wide bandgap materials that can operate at higher voltages, and the need to make modules more compact, hazards like electrical breakdown are more likely. Partial discharge (PD) is a silent and invisible precursor to electrical breakdown. It is compounded with compaction, creating a potential for electrical breakdown and catastrophic failure of the module package. Instead of being the limiting factor, or even a hazard, power module packages need to keep pace with the advancements being made in wide bandgap technology. While the automation of power module design is still new, and research and standards on PD in power modules are limited, this dissertation is a significant step in designing for high voltage operation while assessing tradeoffs against module compaction in an electronic design automation tool. This dissertation describes a method of systematically accounting for partial discharge in power modules using a unique approach where improvements to a module layout are determined in terms of design rules. Trace gaps, in this method, are designed to be functions of operating voltage, substrate and encapsulant material choice, and layer thicknesses of the substrate. These design rules are based on simulations that are validated by physical PD experiments. Furthermore, filleting is performed on the final layouts to further reduce PD by reducing the E-field concentrations by a third. This methodology has been implemented in PowerSynth, an in-house hardware-validated electronic design automation tool that performs electro-thermal and mechanical layout optimization. Before the implementation of this work, layouts were agnostic to PD. From the contribution of this work, the layouts now generated by the tool are PD-mitigated, with a maximum operating voltage for each layer stack. Below the rated voltage, the user can choose multiple voltage-trace gap trade off options for the layout. Demonstrating this implementation in this work shows that the user can achieve either a 24% improvement in voltage level, or a 20% improvement in area reduction, or a trade-off combination of the two. As layouts increase in complexity, these improvements will likely grow. The implementation of this work allows room for growth by allowing customized PD data libraries from various manufacturing lines to inform design rules much like a process design kit in the field of integrated circuit design. The designer using PowerSynth can: 1.) Use default libraries for design rules, or 2.) Perform their own simulations to augment the existing PD data library according to the method presented here, or 3.) Fabricate their own test structures and design corresponding simulations to develop their own complete PD data library and import it to PowerSynth. The manufacturable modules resulting from this tool are thus designed to be practical and reliable for high voltage operation

Partial Discharge Mitigation in Power Modules using an Automation-Driven Design Rule Development Method

Power modules used for the conversion and conditioning of electrical power for applications like electric vehicles, more-electric aircraft, the power grid, etc., are largely designed manually by engineers. Design automation of power modules is starting to gain recognition as a timely and necessary alternative to intuitive manual design and fabrication. With increasing need for wide bandgap materials that can operate at higher voltages, and the need to make modules more compact, hazards like electrical breakdown are more likely. Partial discharge (PD) is a silent and invisible precursor to electrical breakdown. It is compounded with compaction, creating a potential for electrical breakdown and catastrophic failure of the module package. Instead of being the limiting factor, or even a hazard, power module packages need to keep pace with the advancements being made in wide bandgap technology. While the automation of power module design is still new, and research and standards on PD in power modules are limited, this dissertation is a significant step in designing for high voltage operation while assessing tradeoffs against module compaction in an electronic design automation tool. This dissertation describes a method of systematically accounting for partial discharge in power modules using a unique approach where improvements to a module layout are determined in terms of design rules. Trace gaps, in this method, are designed to be functions of operating voltage, substrate and encapsulant material choice, and layer thicknesses of the substrate. These design rules are based on simulations that are validated by physical PD experiments. Furthermore, filleting is performed on the final layouts to further reduce PD by reducing the E-field concentrations by a third. This methodology has been implemented in PowerSynth, an in-house hardware-validated electronic design automation tool that performs electro-thermal and mechanical layout optimization. Before the implementation of this work, layouts were agnostic to PD. From the contribution of this work, the layouts now generated by the tool are PD-mitigated, with a maximum operating voltage for each layer stack. Below the rated voltage, the user can choose multiple voltage-trace gap trade off options for the layout. Demonstrating this implementation in this work shows that the user can achieve either a 24% improvement in voltage level, or a 20% improvement in area reduction, or a trade-off combination of the two. As layouts increase in complexity, these improvements will likely grow. The implementation of this work allows room for growth by allowing customized PD data libraries from various manufacturing lines to inform design rules much like a process design kit in the field of integrated circuit design. The designer using PowerSynth can: 1.) Use default libraries for design rules, or 2.) Perform their own simulations to augment the existing PD data library according to the method presented here, or 3.) Fabricate their own test structures and design corresponding simulations to develop their own complete PD data library and import it to PowerSynth. The manufacturable modules resulting from this tool are thus designed to be practical and reliable for high voltage operation

Machine allocation problems in manufacturing networks

In this paper we discuss two server (machine) allocation problems that occur in manufacturing networks. The manufacturing network is modelled as an open network of queues. The server allocation problems are solved by means of a marginal analysis scheme. We show that for the first problem our algorithm generates undominated allocations. Furthermore, the algorithm provides us with bounds to check how close the allocation generated is to the optimal allocation. In the second problem the algorithm presented generates optimal allocations within time bounded by a polynomial function in the size of the network

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE

Integration of resource efficiency and waste management criteria in European product policies – Second phase. Report n° 1. Analysis of Durability

The present report aims at: 1) identifying key issues concerning the durability of products; 2) analysing methods and standards for the assessment of durability; 3) identifying potential product’s policy criteria for durability. The report is subdivided in 3 Chapters: Chapter 1 analyses scientific publications and standards to identify potential methods for the assessment of the durability of products. Also potential approaches to extend the operating time of products have been illustrated. Chapter 2 applies the method for the environmental assessment of durability to two exemplary washing machines. Chapter 3 illustrates hot spots for durability of washing machines, meaning those key components/parts that are functionally critical for the lifetime of the product. The analysis has been based on researches published in scientific literature and feedback from stakeholders. Potential environmental benefits for the washing machine product group due to extension of product’s lifetime have been also estimatedJRC.H.8-Sustainability Assessmen