Multidimensional device architectures for efficient power electronics

Zhang, Yuhao; Udrea, F.; Wang, Han

doi:10.1038/s41928-022-00860-5

Cited by 103 publications

(41 citation statements)

References 115 publications

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“…As expected, gate length scaling improves I D,max for the ON-state characteristics, but slightly worsens the gate control in the OFF-state breakdown measurements. The destructive breakdown was ≈50 V. It should be noted that, none of the transistors in this work feature any electric field management structures (e.g., field plates and charge balancing [42]), which are expected to push the boundary of the R ON A × BV trade-off.…”

Section: B Results and Discussionmentioning

confidence: 99%

Highly Scaled GaN Complementary Technology on a Silicon Substrate

Xie

Yuan

Niroula

et al. 2023

IEEE Trans. Electron Devices

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This article reports on the scaling of GaN complementary technology (CT) on a silicon substrate to push its performance limits for circuit-level applications. The highly scaled self-aligned (SA) p-channel FinFET (a fin width of 20 nm) achieved an I D,max of −300 mA/mm and an R ON of 27 •mm, a record for metal organic chemical vapor deposition (MOCVD)-grown III-nitride p-FETs. A systematic study on impact of fin width scaling and recess depth in these transistors was conducted. A new SA scaled n-channel p-GaN-gate FET (n-FET) process, compatible with the p-FinFET, demonstrated enhancement-mode (E-mode) n-FETs (L G = 200 nm, I D,max = 525 mA/mm, and R ON = 2.9 •mm) on the same epitaxial platform. The p-FETs and n-FETs feature competitive performance in their respective categories and, when taken together, offer a leading solution for GaN CT on a silicon substrate.

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Section: B Results and Discussionmentioning

confidence: 99%

Highly Scaled GaN Complementary Technology on a Silicon Substrate

Xie

Yuan

Niroula

et al. 2023

IEEE Trans. Electron Devices

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“…Even though the pseudorandom binary sequence (PRBS) signal method [20], containing multiple frequency components, can be used to expedite the frequency sweep process to some extent, the process of experimental measurements can still be very tedious when considering multiple OPs. Future power electronics converters may operate at higher frequencies with sophisticated switching actions based on Silicon Carbide (SiC) or Gallium Nitride (GaN) power devices [21], making impedance data collections even more challenging.…”

Section: Few-shot Experiments and Model Extrapolation Using Transfer ...mentioning

confidence: 99%

Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters

Li¹,

Liao²,

Chen³

et al. 2023

Preprint

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<p>The future electric grid will be pervasively supported by a large number of smart inverters distributed at the grid edge, whose dynamics are critical for grid stability and resiliency. The operating conditions of these inverters may vary across a wide range, leading to various impedance patterns and complicated grid-inverter interaction behaviors. Existing analytical impedance models require thorough and precise understandings of system parameters and make numerous assumptions to reduce the system complexity. They can hardly capture complete electrical behaviors of physical systems when inverters are controlled with sophisticated algorithms or performing complex functions. Real-world impedance acquisitions across multiple operating points through simulations or measurements are expensive and impractical. Leveraging the recent advances in artificial intelligence and machine learning, we present the InvNet, a few-shot machine learning framework that is capable of characterizing inverter impedance patterns across a wide operation range when only limited impedance data for each inverter is available. The InvNet is capable of extrapolating from physics-based models to real-world models and from inverters to inverters. Comprehensive evaluations were conducted to verify the effectiveness of the proposed approach in various application scenarios. All data and models were open-sourced. We showcase machine learning and neural networks as powerful tools for modeling black-box characteristics of sophisticated grid-edge energy systems and their capabilities of analyzing behaviors of larger-scale systems that cannot be described via traditional analytical methods.</p>

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“…Even though the pseudorandom binary sequence (PRBS) signal method [21], containing multiple frequency components can be Few-Shot Machine Learning at the Grid Edge used to expedite the frequency sweep process to some extent, the process of experimental measurements can still be very tedious when considering multiple OPs. Future power electronics converters may operate at higher frequencies with sophisticated switching actions based on Silicon Carbide (SiC) or Gallium Nitride (GaN) power devices [22], making impedance data collections even more challenging.…”

Section: Few-shot Experiments and Model Extrapolation Using Transfer ...mentioning

confidence: 99%

Few-Shot Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Smart Inverters

Liao

Chen

et al. 2023

Preprint

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The future electric grid will be supported by a vast number of smart inverters distributed at the edge of the grid. These inverters' dynamics are commonly characterized as impedances under small-signal perturbations and are critical for ensuring grid stability and resiliency. However, the operating conditions of these inverters can vary widely, resulting in various impedance patterns and complicating grid-inverter interaction behaviors. Current analytical impedance models necessitate a comprehensive and accurate comprehension of system parameters while also relying on multiple assumptions to simplify system complexities. They can hardly capture the complete electrical behaviors of physical systems when inverters are controlled with sophisticated algorithms or performing complex functions. Real-world impedance acquisitions across multiple operating points through simulations or measurements are expensive and impractical. Leveraging the recent advances in artificial intelligence and machine learning, we present InvNet, a few-shot machine learning framework capable of characterizing inverter impedance patterns across a wide operation range, even with limited impedance data for each inverter. The InvNet can extrapolate from physics-based models to real-world models and from one inverter to another. Comprehensive evaluations were conducted to verify the effectiveness of our proposed approach in various application scenarios, and all data and models were open-sourced. Our study demonstrates the effectiveness of machine learning and neural networks as powerful techniques for modeling the intricate black-box characteristics of grid-edge energy systems and analyzing the behaviors of such systems with larger-scales that cannot be accurately described using traditional analytical methods.

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Multidimensional device architectures for efficient power electronics

Cited by 103 publications

References 115 publications

Highly Scaled GaN Complementary Technology on a Silicon Substrate

Highly Scaled GaN Complementary Technology on a Silicon Substrate

Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters

Few-Shot Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Smart Inverters

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