Scholarly Works, Electrical and Computer Engineering

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Research articles, presentations, and other scholarship

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Neural Encoding Strategies for Neuromorphic Computing
Liu, Michael; Zheng, Honghao; Yi, Yang (MDPI, 2026-03-14)
Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). This paper provides a comprehensive overview of major neural encoding schemes used in neuromorphic systems, including rate and temporal encoding, as well as latency, interspike interval, phase, and multiplexed encoding. The purpose of this paper is to explore the use of encoding techniques for deep learning applications. We discussed the underlying principles of spike encoding approaches, their biological inspiration, computational efficiency, power consumption, integrated circuit design and implementation, and suitability for various neuromorphic applications. We also presented our research on a hardware-and-software co-design platform for different encoding schemes and demonstrated their performance. By comparing their strengths, limitations, and implementation challenges, we aim to provide insights that will guide the development of more efficient and application-specific neuromorphic systems. We also performed an encoder performance analysis via Python 3.12 simulations to compare classification accuracies across these spike encoders on three popular image and video datasets. The performance of neural encoders working with both deep neural networks (DNNs) and SNNs is analyzed. Our performance data is largely consistent with the benchmark data on image classification from other papers, while limited performance data on the University of Central Florida’s 101 (UCF-101) video dataset were found in comparable studies on spike encoders. Based on our encoder performance data, the Interspike Interval (ISI) encoder performs well across all three datasets, preserving continuous, detailed spike timing and richer temporal information for standard classification tasks. Further, for image classification, multiplexing encoders outperform other spike encoders as they simplify timing patterns by enforcing phase locking and improve stability and robustness to noise. Within the SNN testbenches, the ISI-Phase encoder achieved the highest accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, surpassing the Time-To-First Spike (TTFS) encoder by 1.9%. On the Canadian Institute For Advanced Research (CIFAR-10) dataset, the ISI encoder achieved the highest accuracy. This ISI encoder had 22.7% higher accuracy than the TTFS encoder on the CIFAR-10 dataset. The ISI encoder performed best on the UCF-101 dataset, achieving 12.7% better performance than the TTFS encoder.
Experiences with VITIS AI for Deep Reinforcement Learning
Chaudhury, Nabayan; Gondhalekar, Atharva; Feng, Wu-chun (IEEE, 2024-09)
Deep reinforcement learning has found use cases in many applications, such as natural language processing, self-driving cars, and spacecraft control applications. Many use cases of deep reinforcement learning seek to achieve inference with low latency and high accuracy. As such, this work articulates our experiences with the AMD Vitis AI toolchain to improve the latency and accuracy of inference in deep reinforcement learning. In particular, we evaluate the soft actor-critic (SAC) model that is trained to solve the MuJoCo humanoid environment, where the objective of the humanoid agent is to learn a policy that allows it to stay in motion for as long as possible without falling over. During the training phase, we prune the model using the weight sparsity pruner from the Vitis AI optimizer at different timesteps. Our experimental results show that pruning leads to an improvement in the evaluation of the reinforcement learning policy, where the trained agent can remain balanced in the environment and accumulate higher rewards, compared to a trained agent without pruning. Specifically, we observe that pruning the network during training can deliver up to 20% better mean episode length and 23% higher reward (better accuracy), compared to a network without any pruning. Additionally, there is an improvement in decision-making latency up to 20%, which is the time between the observation of the agent's state and a control decision.
On the Landscape of Graph Clustering at Scale
Dey, Saikat; Jha, Sonal; Wanye, Frank; Feng, Wu-chun (IEEE, 2025-06)
Graph clustering, also known as community detection, is used to partition and analyze data across a gamut of disciplines, leading to new insights in fields like bioinformatics, networking, and cybersecurity. To keep pace with the exponential growth in collected data, much of the graph clustering research has increasingly pivoted towards developing parallel and distributed clustering algorithms. However, little work has been done to rigorously characterize such algorithms with respect to each other when using the same software stack, hardware stack, and graph dataset inputs. In this manuscript, we identify three open-source, state-of-the-art graph clustering algorithms and characterize the trade-offs between their accuracy and performance on real-world graphs. We show that the ideal choice of graph clustering algorithm depends on the (1) use case, (2) runtime requirements, and (3) accuracy requirements of the user. We provide guidelines for selecting the appropriate state-of-the-practice graph clustering algorithm and conduct a performance characterization of these algorithms through which we identify opportunities for future research in scalable and accurate graph clustering algorithms.
Reinforcement Learning-Based Fuzzer for 5G RRC Security Evaluation
Parikh, Dhairya; Dessources, Dimitri A.; Tripathi, Nishith D.; Reed, Jeffrey H.; Burger, Eric W. (IEEE, 2026-03-09)
Open Radio Access Network (O-RAN) and modern Fifth Generation Mobile Networks (5G) Standalone (SA) deployments increase protocol complexity and broaden the attack surface of cellular infrastructure. This paper introduces a reinforcement-learning-based fuzz tester designed to evaluate the Radio Resource Control (RRC) layer in 5G SA networks. The fuzzer operates as a software-defined “false” User Equipment (UE) that attaches to the target network, intercepts and mutates uplink RRC messages, and injects malformed test cases targeting RRC handlers. The system integrates Reinforcement Learning (RL)-driven test-case generation with an automated execution pipeline for message injection and packet-capture analysis, allowing the agent to iteratively learn which mutations most effectively trigger anomalous behavior. Reinforcement feedback is computed from system metrics such as Central Processing Unit (CPU) utilization, thread count, and network Input/Output (I/O) to guide learning toward high-impact inputs. Experimental results demonstrate that the proposed fuzzer uncovers previously unseen protocol-handling anomalies, malformed-message behaviors, and resource-exhaustion conditions, including reproducible RRC/NGAP inconsistencies identified through a deterministic Proof-of-Concept (PoC) evaluation. The paper presents the overall architecture, reinforcement learning formulation, and evaluation results, highlighting how feedback-driven adaptive fuzzing can prioritize high-impact mutations for stateful 5G RRC security assessment.
Benchmarking Deep Legendre-SNN for Time Series Classification – Analysis and Enhancements
Gaurav, Ramashish; Agarwal, Shrestha; Stewart, Terrence C.; Yi, Yang (IEEE, 2025-10-29)
Compute- and energy-efficient Time Series Classification (TSC) is the need of the hour-to cater the continually growing sources and applications of temporal data. State-of-the-Art (SoTA) temporal computational models, e.g., LSTMs/RNNs, HIVE-COTE, Transformers, etc., are high performing, but are also resource intensive, resulting in high energy consumption on CPUs/GPUs. On the contrary, Reservoir Computing (RC) based models are resource-efficient and perform well for simple TSC datasets; and when implemented with spiking neurons, spiking RC-based models offer the promise of high energy-efficiency on neuromorphic hardware. In this work, we analyse, enhance, and benchmark the newly introduced-spiking RC-based, “Legendre Spiking Neural Network” (Legendre-SNN or LSNN) model for TSC. We theoretically investigate the Legendre Delay Network (LDN) that acts as a reservoir in the LSNN model, and bring some useful insights into the design of the LDN-based models. In our analysis, we find that a higher order LDN is necessary for optimal performance with input signals composed of higher frequencies. We also extend the existing LSNN model to multivariate time-series signals and propose the “DeepLSNN” model. We conduct experiments with DeepLSNN on 102 benchmark TSC-datasets (comprising both univariate and multivariate signals). Via such large scale experiments, we present the first benchmark-results for spiking-TSC. Considering DeepLSNN's best results, we find that it outperforms the non-spiking LSTM-FCN on more than 31% of the 102 datasets. We note that our benchmark-results can serve as a comparison criterion for other spiking-TSC experiments.
Multivariate Legendre-SNN on Loihi-2 for Time Series Classification and 5G Jamming Detection
Gaurav, Ramashish; Sinha, Sujata; Lin, Chunxiao; Stewart, Terrence C.; Liu, Lingjia; Yi, Yang (IEEE, 2026)
5G-&-Beyond technologies offer the promise of improved speed and bandwidth, ultra low latency, high network reliability, and have the potential to enable new applications and services. It only seems fitting to complement the transformative future of 5G-&-Beyond with the low energy offering of Spiking Neural Networks (SNNs) on neuromorphic chips. In this work, we develop Loihi-2 (Intel’s neuromorphic chip) -compatible versions of our previously proposed Legendre-SNN model for univariate and multivariate Time-Series Classification (TSC), as well as for 5G wireless applications. The Legendre-SNN is a reservoir-based SNN, where, the non-spiking Legendre Delay Network (LDN) is used as a static reservoir, followed by a trainable spiking network. Deploying such an SNN model (mix of non-spiking and spiking components) entirely on Loihi-2 is nontrivial - this is due to the scarcity of related approaches and technical documentations. In this work, we present our approach and the technicalities of implementing the non-spiking LDN on the rarely used “Lakemont core” (embedded on Loihi-2); thereby, adding to the scarce technical documentation to program on-chip Lakemont cores. Thus, our presented approach can be leveraged by other researchers as well - to implement their non-spiking components right on-chip. Our proposed hardware-friendly versions of Legendre-SNN when evaluated on Loihi-2, outperform LSTM-based models (- executed on GPU) on 7 of 24 TSC datasets. Here, we also emphasize on the applications of our Legendre-SNN versions for 5G Jamming Detection on Loihi-2, and complement it with a real-time video demonstration of Jamming Detection (with simulated signals) on our physical Kapoho-Point Single Chip Loihi-2 board, followed by detailed energy-analysis. Overall, this work is directed towards the (comparatively) understudied technical side of neuromorphic computing to enable researchers leverage the Lakemont cores and deploy their SNNs entirely on Loihi-2, with a push towards the cause for neuromorphics in Wireless Communications.
Modular Coaxial Power Converter for High-Density Integration into Medium-Voltage Cables
Cairnie, Mark A. Jr.; Kamalapur, Aakash; Nassar, Rajaie; Knoll, Jack; Yuchi, Qingrui; Bae, Jung-Soo; DiMarino, Christina M.; Ngo, Khai; Lu, Guo-Quan; Li, Qiang; Boroyevich, Dushan; Zhou, Jierui; Cao, Yang; DeVoto, Douglas; Kekelia, Bidzina (2024-06)
This work proposes to combine the functionality benefits of power electronics with the power density benefits of medium-voltage cables to create a streamlined, high-density power electronics solution that seamlessly integrates with medium-voltage cables. Located at the ends of a medium- or high-voltage line, the proposed converter uses a cascade of coaxial power conversion cells to gradually step down the voltage, and excels in high step-down applications. By mimicking the coaxial geometry of medium-voltage cables, the converter preserves the axisymmetric electric field of the cable which, when combined with a solid insulating dielectric, provides a voltage scaling advantage over conventional planar and PCB-based converter solutions. Similar to medium voltage cables, the converter is fully passively cooled. A passive cooling strategy allows for combined installation with existing medium voltage cable systems without the added cost, maintenance needs, infrastructure, and reliability concerns associated with active cooling systems. The scalability of the modular structure in combination with the integration benefits provide a flexible power electronics system that can adapt to the evolving demands of the grid.
Coaxial Gate/Kelvin Interconnects for Reduced Gate Loop Inductance and Common Source Mutual Inductance in Power Semiconductor Packages
Knoll, Jack; Cairnie, Mark A. Jr.; DiMarino, Christina M. (IEEE, 2026-02-18)
A coaxial gate/kelvin interconnect is proposed to limit gate loop inductance in vertical package structures. While low-inductance solutions have been demonstrated for planar packages, existing interconnects for vertical package structures result in high inductance per unit length. The coaxial gate/kelvin interconnect proposed here has been shown to reduce per-unit length gate-loop inductances in vertical package structures by 50% or more, enabling more advanced vertical package structures. The proposed on-chip coaxial interconnect also results in significant decoupling between the gate and power loops, enabling faster switching and better control of parallel die. This paper explores several materials and techniques for implementing coaxial interconnects. The resultant electrical characteristics, bond strengths, and viability of these approaches were evaluated. A solution for limiting mechanical stress in the bond between the die and the coaxial interconnects due to mechanical loading on the unbonded end was developed. To demonstrate the viability of the proposed interconnect solution, a package prototype which includes coaxial gate/kelvin interconnects was fabricated and characterized.
SCALED: Substation in a cable for adaptable low-cost electrical distribution
Cairnie, Mark A. Jr.; DiMarino, Christina M. (Adjacent Digital Politics, 2025-10-17)
Modern power electronics meets medium voltage cables to create a high-density inline power conversion system for the grid of tomorrow. Global electricity networks face a mounting dual challenge: increased demand and higher risk. Rapid electrification, the accelerated rollout of renewables, and the surging energy appetite of data centers are all straining grid capacity. At the same time, climate change and geopolitical instability raise the stakes for security and resilience. Recent conflicts have highlighted the vulnerability of energy infrastructure during crises, while market volatility has underscored the urgency of building systems that can withstand disruptions. Together, these forces are driving unprecedented stress across transmission and distribution networks.
Thermal Modeling and Limitations for Power Electronics Embedded in Medium-Voltage Cables
Cairnie, Mark A. Jr.; DiMarino, Christina M.; DeVoto, Douglas; Kekelia, Bidzina; Moreno, Gilberto; Chaudhary, Rajneesh (IEEE, 2025-09-11)
As next-generation energy technologies gain traction and power demand increases, the existing electrical infrastructure faces significant stress, prompting innovative solutions to enhance the grid’s capacity and lifespan. This work explores the possibility of embedding medium-voltage (MV) power electronics directly inline with the cable, and the resulting thermal challenges. Since the majority of power distribution cables installed in the U.S. are passively cooled, the work focuses primarily on passive cooling, with an emphasis on the limitations of axial heat spreading within the cable. To date, literature on axial spreading of high incident heat loads on cables and cable environments is limited, typically reporting cases with <10 W of incident heat load. This work will explore the considerations, limits, and tradeoffs of cable-embedded heat loads significantly larger than the cable losses. Both external and internal effects are modeled analytically in nondimensional terms via a Biot number analysis, allowing fundamental limits and tradeoffs to be derived. The work culminates in the design and experimental validation of a cable-embedded thermal system capable of passively dissipating 300 W of heat from a coaxial SiC MOSFET switch module over a length of 20 cm, thus validating the possibility of MV cable-embedded power electronics from a thermal standpoint.
Reservoir Computing: Foundations, Advances, and Challenges Toward Neuromorphic Intelligence
Liu, Andrew; Azmine, Muhammad Farhan; Lin, Chunxiao; Yi, Yang (MDPI, 2026-02-13)
Reservoir computing (RC) has emerged as an energy-efficient paradigm for temporal information processing, offering reduced training complexity by fixing recurrent dynamics and training only a simple readout layer. Among RC models, Echo State Networks (ESNs) and Liquid State Machines (LSMs) represent two distinct approaches based on continuous-valued and spiking neural dynamics, respectively. In this work, we present a comparative evaluation of ESNs and LSMs on the Mackey–Glass chaotic time-series prediction task, with emphasis on scalability, overfitting behavior, and robustness to reduced numerical error precision. Experimental results show that ESNs achieve lower prediction error with relatively small reservoirs but exhibit early performance saturation and signs of overfitting as reservoir size increases. In contrast, LSMs demonstrate more consistent generalization with increasing reservoir size and maintain stable performance under aggressive reservoir quantization. These findings highlight fundamental trade-offs between accuracy and hardware efficiency, and suggest that spiking RC models are well suited for energy-constrained and neuromorphic computing applications.
Privacy Risks of Cybersquatting Attacks
Kolenbrander, Jack; Rheault, Elliott; Michaels, Alan J. (MDPI, 2026-02-19)
Cybersquatting is a collection of methods commonly used by malicious actors to mislead or trick internet users into accessing fraudulent or malicious content. Much of the current research has concentrated on the specific techniques used by attackers in this domain, such as typosquatting, combosquatting, and sound squatting. Some research has explored the financial and time impacts of cybersquatting; however, an understanding of user privacy impacts is limited. Prior research into privacy implications has primarily relied on passive techniques such as analyzing DNS records, HTML content, and domain registrations. These passive approaches limit the ability to interact with these domains and track the downstream impact of sharing personally identifiable information (PII). This research develops an active open-source intelligence (OSINT) collection system capable of rapidly collecting and analyzing squatting domains through both passive and active techniques, with a particular emphasis on identifying those that solicit user information. Synthetic identities are then registered with these domains, and their associated communications are collected and analyzed to identify privacy-related risks and determine whether shared PII propagates.
Network intracellular recording and synaptic connection mapping with a microhole electrode array
Wang, Jun; Jung, Woo-Bin; Gertner, Rona S.; Park, Hongkun; Ham, Donhee (2025-11-15)
Patch-clamp electrodes capture intracellular signals from few neurons, while microelectrode arrays (MEAs) record many neurons extracellularly with limited sensitivity. Bridging these approaches has been a long-standing goal. We report a 4,096-channel semiconductor microhole electrode array that records intracellularly from thousands of neurons in parallel. From over 2,000 neurons, more than 70,000 plausible synaptic connections were identified and classified as electrical, inhibitory, or excitatory, with about 5% error. This platform enables large-scale mapping of neuronal network connectivity with single-cell resolution.
Solar flare-induced gradient drift instability observed by SuperDARN HF radars
Chakraborty, Shibaji; Nishitani, Nozomu; Shi, Xueling; Ponomarenko, Pavlo; Ruohoniemi, Mike; Baker, Joseph; Coster, Anthea; Haggstrom, I. (2025-10-23)
Solar flares are a rapid increase in solar irradiance, specifically in X‐ray and Extreme Ultraviolet spectra, which enhances the ionization in the dayside ionosphere and creates Sudden Ionospheric Disturbances (SIDs). SIDs are known to create space weather impacts on traveling high frequency (HF: 3–30 MHz) radio waves, by disrupting the communication channels. In this study, we examine ionospheric scatters at dawn terminator, which stems from a severe X9.3 flare on 6 September 2017 peaked at 12:02 UT, utilizing SuperDARN HF coherent scatter radars and Global Navigation Satellite System (GNSS) Total Electron Content (TEC) observations. Specifically, we are interested in the transients in the ionospheric electrodynamics at the sub‐auroral latitude near the terminator stemming from the flare effect. Observations suggest that flare‐induced density gradient likely favors the formation of gradient‐drift instability near the dawn terminator, leading to the irregularities observed by the SuperDARN radars with line‐of‐sight (LoS) Doppler velocity reaching nearly 300 m/s. The flare amplifies the eastward TEC gradient near the dawn terminator by approximately 2–3 times compared to a geomagnetically quiet and non‐flare day. The observed irregularities, attributed to flare‐driven instabilities, exhibit a velocity consistent with the equatorial return flow of ionospheric Hall convection. In contrast to prior studies indicating decreased cross‐polar‐cap potential and associated ionospheric convection flow, our findings show the flare is followed by an increase in localized electric field near the dawn terminator, as depicted in radar LoS velocity.
Statistical characterization of joule heating associated with ionospheric ULF perturbations using SuperDARN data
Shi, Xueling; Chakraborty, Shibaji; Baker, Joseph B. H.; Hartinger, Michael D.; Wang, Wenbin; Ruohoniemi, J. Michael; Lin, Dong; Lotko, William; Sterne, Kevin; McWilliams, Kathryn A. (2025-03-18)
Ultra low frequency (ULF; 1 mHz ‐ several Hz) waves are key to energy transport within the geospace system, yet their contribution to Joule heating in the upper atmosphere remains poorly quantified. This study statistically examines Joule heating associated with ionospheric ULF perturbations using Super Dual Auroral Radar Network (SuperDARN) data spanning middle to polar latitudes. Our analysis utilizes high‐timeresolution measurements from SuperDARN high‐frequency coherent scatter radars operating in a special mode, sampling three “camping beams” approximately every 18 s. We focus on ULF perturbations within the Pc5 frequency range (1.6–6.7 mHz), estimating Joule heating rates from ionospheric electric fields derived from SuperDARN data and height‐integrated Pedersen conductance from empirical models. The analysis includes statistical characterization of Pc5 wave occurrence, electric fields, Joule heating rates, and azimuthal wave numbers. Our results reveal enhanced electric fields and Joule heating rates in the morning and pre‐midnight sectors, even though Pc5 wave occurrences peak in the afternoon. Joule heating is more pronounced in the highlatitude morning sector during northward interplanetary magnetic field conditions, attributed to local time asymmetry in Pedersen conductance and Pc5 waves driven by Kelvin‐Helmholtz instability. Pc5 waves observed by multiple camping beams predominantly propagate westward at low azimuthal wave numbers (|m|<50), while high‐m waves propagate mainly eastward. Although Joule heating estimates may be underestimated due to assumptions about empirical conductance models and the underestimation of electric fields resulting from SuperDARN line‐of‐sight velocity measurements, these findings offer valuable insights into ULF wave‐related energy dissipation in the geospace system.
A Novel 500-kHz LLC-T Resonant Converter with Wide Output Range
Hou, Zhengming; Jiao, Dong; Lai, Jih-Sheng (IEEE, 2025)
The LLC resonant converter is attractive for the electric vehicle charging application due to its high efficiency and high power-density. Nevertheless, the efficiency of the conventional LLC converter suffers from the wide switching frequency for the wide output range applications. In this paper, a novel LLC-T resonant converter, an extra auxiliary transformer in parallel with the L-C resonant tank, is proposed to narrow the switching frequency range for the EV charging application. A 500-kHz, 1.35-kW, 400-V/250-450-V prototype is built to verify the merits of the proposed converter and demonstrates 97.82% peak efficiency.
A multilevel voltage-source inverter with separate DC sources for static var generation
Peng, Fang Zheng; Lai, Jih-Sheng; McKeever, John; VanCoevering, James (IEEE, 1995)
A new multilevel voltage-source inverter with separate dc sources is proposed for high-voltage, high-power applications, such as flexible ac transmission systems (FACTS) including static var generation (SVG), power line conditioning, series compensation, phase shifting, voltage balancing, fuel cell and photovoltaic utility systems interfacing, etc. The new M-level inverter consists of (M-1)/2 single phase full bridges in which each bridge has its own separate dc source. This inverter can generate almost sinusoidal waveform voltage with only one time switching per cycle as the number of levels increases. It can solve the problems of conventional transformer-based multipulse inverters and the problems of the multilevel diode-clamped inverter and the multilevel flying capacitor inverter. To demonstrate the superiority of the new inverter, a SVG system using the new inverter topology is discussed through analysis, simulation and experiment.
Practical Current Derivation Method for a Highly Accurate Variable Switching Frequency ZVS regulation in TCM operated Bidirectional Buck/Boost Converters
Gutierrez, Bryan; Hou, Zhengming; Jiao, Dong; Lai, Jih-Sheng (IEEE, 2024)
Triangular conduction mode (TCM) is a technique to operate non-isolated buck/boost converters under zero voltage switching (ZVS) turn-on. In TCM, a negative current is required for the active device to turn on under ZVS condition. The magnitude of this current is well-known and established. However, the derivation of the variable switching frequency according to the operating condition is usually simplified with the linear character of the magnetization and demagnetization of the inductors in the converter. The utilization of this simplification can increase the peak-to-peak current ripple in the inductor and burden the duty cycle loss in the controller. Additionally, it can increase the turn-off loss in the devices. This paper presents a highly accurate computation for the variable switching frequency to reduce the negative effects of the simplified method. Derivations are presented for charging and discharging modes, and they were tested in a 15-kW rated bidirectional dual-phase buck-boost converter.
Dynamic Current Sharing Issues with Paralleling SiC Power MOSFETs
Liu, Ching-Yao; Lee, Chen-Chan; Lai, Jih-Sheng (IEEE, 2025)
This work comprehensively evaluates the key factors that impact the dynamic current sharing of paralleling silicon carbide (SiC) MOSFETs at the phase-leg circuit level. The power device matching is necessary and is well-known method to improve current balance. Stray inductance differences in the power loop, gate drive loop, and printed circuit board (PCB) layout are also well-known key factors. In addition to the above conventional sorting and passive matching methods, this paper proposes additional active matching approach by using the negative gate-off voltage, which can not only eliminate switching noise induced false turn on, but also help current sharing with gating signal delay matching similar to adjusting gate resistance. Impact of all key factors have been verified through experimental results.
Direct Drive D-Mode GaN HEMT Switching Characteristics and Turn-Off Loss Reductions
Lai, Jih-Sheng; Hsieh, Hsin-Che; Liu, Ching-Yao; Chieng, Wei-Hua; Yang, Chih-Yi; Hsu, Chang-Shun; Chang, Edward Yi (IEEE, 2025-04)
This article aims to evaluate the depletion-mode gallium nitride high electron mobility transistor (d-mode GaN HEMT) using direct-drive gating and double pulse test to assess switching energy. The gate driving circuit features a modified cascode structure for "normally off"operation and a charge-pump circuit to supply a negative gate voltage for turn-off operation. This article described these features theoretically and validated with experimental results. Similar to enhancement-mode power mosfets or HEMTs, adjusting the gate drive resistance can affect the switching speed and associated losses, but the d-mode GaN HEMTs present an additional feature with turn-off loss reduction through gate voltage control. Thus, the main contribution of this article is to propose and demonstrate significant turn-off loss reduction using the direct-drive approach for d-mode GaN HEMTs.

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