Graph-Model-Based Generative Layout Optimization for Heterogeneous SiC Multichip Power Modules With Reduced and Balanced Parasitic Inductance

Zhou, Yurong; Jin, Yuting; Chen, Yu; Luo, Haoze; He, Xiangning

doi:10.1109/tpel.2022.3157873

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…The minimum cut represents the border between upper and lower control levels. In power electronics, graph theory has been used for dynamic analysis [18], [19] (using switching flow graphs), cooperative control [20] (using directed communication graphs), converter topology simplification [21] (using node merging and path elimination), converter topology derivation [22] (using graph search), and chip layout optimization [23] (using constraint graphs). Another prominent example of using the minimum cut -maximum flow theorem is image segmentation.…”

Section: Graph Partitioning Methodsmentioning

confidence: 99%

On the Partitioning of MMC Control Systems Using Graph Theory

Jahn

Chaffey

Prieto‐Araujo³

et al. 2022

IEEE Open J. Power Electron.

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Control systems of modular multilevel converters can be partitioned into several levels. Such partitioning is becoming relevant in multi-vendor high-voltage direct-current systems where one party may deliver the converter hardware and associated control, whereas another party may deliver the systemrelated control. However, the exact control system partitioning has not been subject to research. This paper presents a method for partitioning control systems into two levels with minimum dependencies between the two levels. Furthermore, the impact of specific control designs on the partitioning and integration effort is discussed using an example. The presented method is useful for the design of multi-vendor control systems for modular multilevel converters whose partitioning otherwise would be based on engineering intuition.

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Section: Graph Partitioning Methodsmentioning

confidence: 99%

On the Partitioning of MMC Control Systems Using Graph Theory

Jahn

Chaffey

Prieto‐Araujo³

et al. 2022

IEEE Open J. Power Electron.

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“…(i) Component-level: In [20], a graph model is constructed to represent the complex, heterogeneous layouts of multichip silicon carbide power modules, capturing interconnectivity and design constraints, and using this model in conjunction with integer programming and genetic algorithms for systematic and efficient optimization of module layouts. In the latest framework of design automation technique PowerSynth 2 [59], constraint graphs are constructed to enable bottom-up constraint propagation for synthesizing layouts that respond to various design constraints, such as minimum width, enclosure, and spacing between components, leading to optimized and constraint-aware solutions.…”

Section: B Recent Developmentsmentioning

confidence: 99%

“…In this context, Graph Theory has emerged as a promising tool for addressing these challenges, especially in recent years [2]. Serving as a common language among various disciplines, graph theory can be leveraged as a powerful tool to enable systematic modelling and analysis [3][4][5][6][7], design new converters [8][9][10][11][12], control their operations [13,14], estimate and identify potential issues [15][16][17], optimize the systems [18][19][20], or even facilitate the understanding of the interconnections and interactions between components in power-electronics-based systems [21][22][23]. Especially in recent years, innovative research keeps emerging, covering component-level, converter-level and system-level of power electronics [24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Li,

Xue,

Zargari

et al. 2023

IEEE Access

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Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graphstructured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs' general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNNbased case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs' unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs' latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

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“…53,54 In the literature, many works can be found in which attempts to solve the aforementioned problems are described through solutions that are mainly based on reducing the negative effects of parasite inductances in discrete devices [55][56][57][58] or power modules (embedded bare dies). [59][60][61][62] Taking into account the new trends and technical requirements on this topic (in addition to others such as efficiency, losses, power density, cost, and size-weight ratio), the main features of power modules and discrete devices are explained in this review. At this point, the benefits of using power modules compared to discrete devices are highlighted, summarizing the main solutions for the electric vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, the leap from silicon technology to other semiconductors derived from WBG materials can cause some problems such as oscillations and electromagnetic interferences (EMI), 49,50 higher harmonic content, 51 cross‐talk effects, 52 and the effect of overvoltages on semiconductor shutdown 53,54 . In the literature, many works can be found in which attempts to solve the aforementioned problems are described through solutions that are mainly based on reducing the negative effects of parasite inductances in discrete devices 55‐58 or power modules (embedded bare dies) 59‐62 . Taking into account the new trends and technical requirements on this topic (in addition to others such as efficiency, losses, power density, cost, and size‐weight ratio), the main features of power modules and discrete devices are explained in this review.…”

Section: Introductionmentioning

confidence: 99%

The role of power device technology in the electric vehicle powertrain

Robles

Matallana

Aretxabaleta

et al. 2022

Intl J of Energy Research

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Summary In the automotive industry, the design and implementation of power converters and especially inverters, are at a turning point. Silicon (Si) IGBTs are at present the most widely used power semiconductors in most commercial vehicles. However, this trend is beginning to change with the appearance of wide‐bandgap (WBG) devices, particularly silicon carbide (SiC) and gallium nitride (GaN). It is therefore advisable to review their main features and advantages, to update the degree of their market penetration, and to identify the most commonly used alternatives in automotive inverters. In this paper, the aim is therefore to summarize the most relevant characteristics of power inverters, reviewing and providing a global overview of the most outstanding aspects (packages, semiconductor internal structure, stack‐ups, thermal considerations, etc.) of Si, SiC, and GaN power semiconductor technologies, and the degree of their use in electric vehicle powertrains. In addition, the paper also points out the trends that semiconductor technology and next‐generation inverters will be likely to follow, especially when future prospects point to the use of “800 V" battery systems and increased switching frequencies. The internal structure and the characteristics of the power modules are disaggregated, highlighting their thermal and electrical characteristics. In addition, aspects relating to reliability are considered, at both the discrete device and power module level, as well as more general issues that involve the entire propulsion system, such as common‐mode voltage.

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Graph-Model-Based Generative Layout Optimization for Heterogeneous SiC Multichip Power Modules With Reduced and Balanced Parasitic Inductance

Cited by 17 publications

References 39 publications

On the Partitioning of MMC Control Systems Using Graph Theory

On the Partitioning of MMC Control Systems Using Graph Theory

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

The role of power device technology in the electric vehicle powertrain

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