英飞凌

This work presents a systematic study of silicon (Si) and magnesium (Mg) doping effects on the structural and electrical properties of non-polar a-plane (11̅20) GaN grown on r-plane (1̅102) sapphire by metal–organic chemical vapor deposition. Compared with conventional c-plane GaN, a-plane GaN eliminates internal polarization fields, making it attractive for optoelectronic applications. N-type and p-type layers were doped using disilane (Si₂H₆) and bis(cyclopentadienyl)magnesium (Cp₂Mg), respectively. Atomic force microscopy (AFM) revealed increased surface roughness with dopant incorporation, while high-resolution X-ray diffraction (XRD) indicated higher basal stacking fault densities at elevated doping levels. Hall Effect measurements showed maximum carrier concentrations of 4.94 × 1019 cm−3 for electrons and 2.13 × 1018 cm−3 for holes. Correspondingly, the specific contact resistivity decreased to 8.88 × 10−5 Ω·cm2 for n-type and 2.28 × 10−3 Ω·cm2 for p-type GaN. These results highlight the trade-off between electrical enhancement and structural degradation, providing guidelines for optimizing non-polar GaN epitaxy.

The growing adoption of battery powered systems demands advanced Battery Management System (BMS) solutions for accurate State of Charge (SoC) and State of Health (SoH) estimation, ensuring safe and efficient operation. To address this need, the authors propose a practical and scalable algorithm based on state-of-the-art TinyML models capable of handling variable-length input sequences without padding, for the SoH estimation. These models enable embedded SoC and SoH estimation at the module level, effectively functioning as smart sensors within a BMS. Four deep learning architectures were developed for battery state estimation, based on CNN, GRU, LSTM, and Dense layers. The models were converted into TinyML compatible formats using an innovative compression technique based on neural network projection. After projection, the models were fine-tuned for a few epochs to restore their accuracy, and then evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), model size, and inference latency. Latency measurements were obtained on an RTX 3060 GPU and are reported only for relative model comparison. For SoC estimation, the models utilized a time window of 60 samples, each containing voltage, current, and temperature measurements, to produce accurate predictions throughout the first life phase of the battery (up to 70% SoH). The projected Pr_LSTM TinyML model was selected for deployment due to its minimal memory footprint (4.0 kB), low latency (0.391 ms), and low error rates (MAE: 0.0114, RMSE: 0.0286). For SoH estimation, the models were trained and tested using partially charging data of varying lengths, reflecting practical real-world conditions. Two input feature sets were considered: the Incremental Capacity Analysis (ICA) curve alone, and ICA combined with voltage data. The projected Pr_CNN_GRU TinyML model, using only ICA features, was selected for its high accuracy and compact size (3.4 kB), achieving an MAE of 0.0060, RMSE of 0.0130, and a latency of 1.607 ms. Finally, an embedded SoC/SoH estimation strategy was proposed to scale predictions from the cell to the module level, enabling potential real-time implementation on the Infineon Mobile Robot (IMR) BMS.

Automotive perception systems are obligated to meet high requirements. While optical sensors such as Camera and Lidar struggle in adverse weather conditions, Radar provides a more robust perception performance, effectively penetrating fog, rain, and snow. Since full Radar tensors have large data sizes and very few datasets provide them, most Radar-based approaches work with sparse point clouds or 2D projections, which can result in information loss. Additionally, deep learning methods show potential to extract richer and more dense features from low level Radar data and therefore significantly increase the perception performance. Therefore, we propose a 3D projection method for fast-Fourier-transformed 4D Range-Azimuth-Doppler-Elevation (RADE) tensors. Our method preserves rich Doppler and Elevation features while reducing the required data size for a single frame by 91.9

Constraint handling is a central challenge for quantum algorithms applied to combinatorial optimization. Standard penalty-based approaches increase problem size, distort energy landscapes, and often degrade performance. Constraint-preserving mixers, such as XY-mixers, restrict quantum evolution to feasible subspaces, but their implementation on gate-based hardware requires Trotterization, which introduces approximation errors. In this work, we systematically investigate the interplay between constraint-preserving XY-mixers and Trotterized Adiabatic Evolution (TAE). We present a theoretical analyses of the origin and scaling of Trotter errors in XY-mixers and show that the dominant contribution depends on the size and structure of individual constraints rather than on the total problem size. Our findings are validated through extensive numerical simulations on three representative problems: Portfolio Optimization, the Multi-Car Paint Shop problem, and a Multi-Commodity Flow problem. For problems with a single global equality constraint spanning all variables, Trotter errors significantly impair XY-mixer performance, making standard Pauli-X mixers more robust under realistic implementations. In contrast, for problems whose constraints decompose into multiple disjoint local blocks, XY-mixers outperform X-mixers by several orders of magnitude even under Trotterized evolution. These results identify constraint locality as the key criterion for the effective use of XY-mixers and demonstrate that TAE combined with structure-aware mixer design provides a robust and theoretically grounded alternative to variational quantum optimization methods. We further present a dedicated mixer Hamiltonian for TSP-like 2-way-1-hot constraints.

We develop a new benchmarking scheme for the Decoded Quantum Interferometry (DQI) algorithm quantifying the number of quantum gates required to obtain an optimal solution to a problem amenable to DQI. We apply the benchmarking scheme to the Binary Paint Shop Problem (BPSP) in order to benchmark the performance of DQI against a state of the art classical solver. To do so, we provide an explicit construction of a quantum circuit implementation of a greedy decoder for low-density parity check codes arising from max-2-XORSAT problems.