Infineon Technologies (Canada)

Integrated cryogenic analog multiplexers are essential for scaling up trapped ion quantum computers (TIQC). They require high-voltage devices, such as LDMOS, to operate within the required voltage range of upto ±40 V. However, at cryogenic temperatures, below 40 K, LDMOS devices exhibit a diode-like behavior, leading to nonlinear on-resistance at low V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DS</inf>. This negatively affects settling time and transfer function linearity of the required analog multiplexers. Previous work on cryogenic LDMOS focused on characterizing and modeling their behavior. This work explores the use of a field plate to improve the on-resistance performance of LDMOS devices at cryogenic temperatures. Our field plate engineered device was fabricated and measured at cryogenic temperatures alongside a baseline device. Our key findings include: 1) a field plate is essential at cryogenic temperatures; 2) it reduces on-resistance by six orders of magnitude at low V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DS</inf>; 3) it degrades the breakdown voltage; 4) device nonlinear region span decreases to 40 mV compared to the previous threshold of 400 mV. Finally, TCAD-based cryogenic simulations demonstrate that the remaining threshold stems from an energy barrier in the conduction band at STI corners. These results show for the first time that regaining the linear behavior of LDMOS devices’ on-resistance at cryogenic temperatures can be achieved, which is essential to scale up the number of qubits and hence bring quantum computers to commercial use.

<strong class="journal-contentHeaderColor">Abstract.</strong> Since June 2019, a scanning airglow camera is operated operationally every night at DLR Oberpfaffenhofen (48.09&deg; N, 11.28&deg; E), Germany. It provides nearly all-sky images (diameter 500 km) of the OH* airglow layer (height ca. 85&ndash;87 km) with an average spatial resolution of ca. 150 m and a temporal resolution of ca. 2 min. We analyse about three years (941 nights between October 2020 and September 2023) of OH* airglow all-sky images for spatially confined wave structures with horizontal wavelengths of ca. 20 km and less. Such structures are often referred to as ripples and are considered to be instability structures. However, Li et al. (2017) showed that they could also be secondary waves. While ripples move with the background wind, secondary waves do not. To identify small-scale and spatially confined structures, we adapt and train YOLOv7 (You Only Look Once, version 7), a machine learning approach, to determine their position and extent on the sky as well as their horizontal wavelength. Those wavelengths are compared to two-dimensional FFT (Fast Fourier Transform) results. We analyse the seasonal variations in the propagation direction and horizontal wavelengths of these structures and deduce that instability signatures are observed especially in summer. Finally, we introduce a concept for &ldquo;operating-on-demand&rdquo; in order to derive energy dissipation rates from our measurements.

Abstract Hand gesture recognition (HGR) has gained significant attention in human-computer interaction, enabling touchless control in various domains, such as virtual reality, automotive systems, and healthcare. While deep learning approaches achieve high accuracy in gesture classification, their lack of interpretability hinders transparency and user trust in critical applications. To address this, we extend MIRA, an interpretable rule-based HGR system, with a novel gesture onset detection method that autonomously identifies the start of a gesture before classification. Our onset detection approach achieves 90.13% accuracy on average, demonstrating its robustness across users. By integrating signal processing techniques, MIRA enhances interpretability while maintaining real-time adaptability to dynamic environments. Additionally, we introduce a background class, enabling the system to differentiate between gesture and non-gesture frames and expand the dataset with new users and recordings to improve generalization. We further analyze how feature diversity affects performance, showing that low diversity can suppress personalization due to early misclassifications. Using a foundational and personalized rule framework, our approach correctly classifies up to 94.9% of gestures, reinforcing the impact of personalization in rule-based systems. These findings demonstrate that MIRA is a robust and interpretable alternative to deep learning models, ensuring transparent decision-making for real-world radar-based gesture recognition.

Abstract Complex combinations of various materials in microelectronic packages pose challenges for sample preparation. Reliable cross-sectional sample preparation is critical for microstructural analysis, especially when utilising low-melting Sn‑Ag‑Cu (SAC) solders, which are easily deformed or damaged by mechanical grinding. Hence, this study explores the impact of femtosecond laser ablation for bulk material removal and subsequent Ar‑ion milling for final cross‑sectional polishing, comparing cooled and uncooled milling masks. We reveal that uncooled Ar-ion milling causes artefacts like increased surface roughness and fine Cu‑rich structures due to surface heating (~50 °C) combined with Cu redeposition on the preferentially milled, intruded SAC‑surface. Cryogenic cooling of the milling mask to -100 °C reduces these effects, minimising curtaining and preserving detail. However, cooled milling results in the displacement of recrystallised grains at grain boundaries due to the thermal expansion anisotropy of β-Sn. Thus, milling parameters must be carefully tailored to the desired preservation of microstructure, cracks and strain.

Indoor positioning and asset tracking have become popular and essential for different applications and use cases. Many systems use Bluetooth Low Energy (BLE) wireless personal area network technology for communication and ranging purposes. Unfortunately, due to limitations of the ISM radio band, other communication technologies such as Z-Wave, ZigBee, and Wi-Fi also use the same frequency bandwidth. This overlap often leads to interference that affects the performance of BLE systems. This work evaluates the effect of Wi-Fi interference on the phasebased ranging distance estimate for different BLE to Wi-Fi signal power ratios. We show the random distance error increasing more than 3 times for both Inverse Fourier Transform and Multiple Signal Classification algorithms at short distances. Based on simulation results and infield experiments, we identified that the interference becomes marginal for distances more than 10m, and the device can’t identify the location correctly in case of similar Wi- Fi and BLE Tx power. In the case of long-distance ranging, ignoring interfered frequencies improves the situation dramatically, but this results in worse resolution and sometimes may identify the distance incorrectly due to false peaks.

This paper discusses a new design for a new pseudo-differential electrode arrangement that is employed to develop a highly sensitive gravimeter. Unlike the earlier reported micromachined gravimeters, this device utilizes a set of linear springs. A dicing-free fabrication process with a high-yield is developed that allows for the retention of a portion of the handle wafer letting the device have a large proofmass in a relatively small chip area of 1cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. The gravimeter has undergone extensive characterization and has a sensitivity of 86pF/g when measuring 1g, a resonance frequency of 445Hz, and a noise floor of better than $350ng/\sqrt {Hz} $. The device was used to track the variations in gravitational forces over a period of two weeks and could detect the effect of earth tides after removing the temperature interferences.

Knowledge of land is of central importance to manage the impact of mankind upon the environment. The understanding and treatment of land vary greatly across different regions and communities, making the description of land highly specific to each locality. To address the larger global issues, such as world food production or climate change mitigation, one needs to have a common standardized understanding of the biosphere cover and use of land. Different governments and institutions established national systems to describe thematic content of land within their jurisdictions. These systems are all valid and tuned to address various national needs. However, their integration at regional or global levels is lacking. Integrating data from widely divergent sources to create world datasets not only requires standards, but also an approach to integrate national and regional land cover classification systems. The ISO 19144 series, developed through the collaboration between the Food and Agriculture Organization of the United Nations (FAO) and the International Organization for Standardization (ISO), offers a meta-language for the integration of disparate land classification systems, enhancing interoperability, data sharing, and national to global data integration and comparison. This paper provides an overview of classification system concepts, different stages for the development of standards in ISO and the status of different standards in the ISO 19144 series. It also explores the critical role of the ISO 19144 series in standardizing land cover and land use classification systems. Drawing on practical case studies, the paper underscores the series’ potential to support global sustainable development goals and lays out a path for its future development and application. Using these standards, we gain not only a tool for harmonizing land classification, but also a critical level for advancing sustainable development and environmental stewardship worldwide.

Abstract Reliable connections of electrical components embody a crucial topic in the microelectronics and power semiconductor industry. This study utilises 3D nondestructive Xray tomography and specifically developed machine learning (ML) algorithms to statistically investigate crack initiation and propagation in SAC305Bi solder balls upon thermal cycling on board (TCoB). We quantitatively segment fatigue cracks and flux pores from 3D Xray tomography data utilising a multilevel MLworkflow incorporating a 3D U-Net model. The data reveals that intergranular fatigue cracking is the predominant failure mechanism during TCoB and that dynamic recrystallisation precedes crack initiation. Moreover, we find that fatigue cracks are initiated at surface notches, flux pores and printed circuit boardmetallisation intrusions. The work provides important insights regarding the underlying microstructural and mechanical mechanisms for recrystallisation and cracking, uniting the aspects of bigdata analysis with MLalgorithms and indepth understanding about the underlying materials science.

Abstract This chapter assesses the benefits of using a solid immersion lens (SIL) to detect faults in ICs via optical imaging and laser-stimulation techniques. It discusses the advantages and limitations of different types of SILs and their effect on spatial resolution, spot size, focus depth, and collection efficiency. It also provides a brief overview of technical challenges at the die level.

Out-of-distribution (OOD) detection recently has drawn attention due to its critical role in the safe deployment of modern neural network architectures in real-world applications. The OOD detectors aim to distinguish samples that lie outside the training distribution in order to avoid the overconfident predictions of machine learning models on OOD data. Existing detectors, which mainly rely on the logit, intermediate feature space, softmax score, or reconstruction loss, manage to produce promising results. However, most of these methods are developed for the image domain. In this study, we propose a novel reconstruction-based OOD detector to operate on the radar domain. Our method exploits an autoencoder (AE) and its latent representation to detect the OOD samples. We propose two scores incorporating the patch-based reconstruction loss and the energy value calculated from the latent representations of each patch. We achieve an AUROC of 90.72% on our dataset collected by using 60 GHz short-range FMCW Radar. The experiments demonstrate that, in terms of AUROC and AUPR, our method outperforms the baseline (AE) and the other state-of-the-art methods. Also, thanks to its model size of 641 kB, our detector is suitable for embedded usage.