IMEC

• A hybrid RBS–NRA referencing approach is developed to quantify the absolute boron content in thin films. • A self-supporting B/Si 3 N 4 film is fabricated, quantified by RBS, and then used as a reference in NRA measurements. • The approach enables full elemental analysis of an in-situ boron-doped SiGe epitaxial layer. • Detailed uncertainty analysis demonstrates the high accuracy and traceable uncertainty of this approach. In semiconductor technology, boron-doped silicon–germanium compounds have attracted significant attention due to their application in strain engineering to enhance the carrier mobility. One of the current challenges in this context is the accurate quantification of the concentration of boron, since many analytical techniques require a reference standard with a known and traceable concentration. In this study, we present a method to quantify the absolute amount of boron in thin films by combining substrate-less Rutherford backscattering spectrometry (RBS) and nuclear reaction analysis (NRA). First, a substrate-less boron pellicle is realized for which the absolute amount of boron is quantified with RBS. Then, the obtained absolute standard is used as a reference for the analysis with the 11 B(p,α 1 )2α nuclear reaction at a proton energy of 640 keV. The strength of the RBS-NRA hybrid approach is illustrated with the analysis of a boron doped SiGe epilayer, demonstrating the traceable uncertainty and an accuracy of 1.5 %. More generally, the method can be employed to certify standards of 11 B in various compounds that are needed for other analytical techniques, such as secondary ion mass spectrometry (SIMS).

We present innovative design strategies to eliminate the need for at-resolution field stitching in high-numerical aperture extreme ultraviolet lithography. For logic applications, two complementary approaches at the inter- and intra-cell levels are proposed. These include the insertion of dummy filler cells or relaxed-pitch cells, combined with metal routing blockages, which can be seamlessly implemented within a modified place-and-route flow, thereby simplifying process complexities associated with at-resolution stitching. For dynamic random access memory applications, a die aspect ratio optimization methodology is introduced to maximize mask area utilization and improve manufacturing throughput. Depending on the die arrangement in the full imaging field, two stitching scenarios are identified: an even number of dies in the Y-direction, enabling pitch-tolerant field stitching along the scribe lane, and an odd number of dies, which eliminates the need for critical-dimension-critical stitching at the stitching boundary.

Microcin B17 (MccB17) is a ribosomally synthesized and post-translationally modified peptide (RiPP) with antibacterial activity, natively produced by Escherichia coli. Despite its unique gyrase-inhibiting activity, therapeutic development has been limited, primarily due to its low solubility and narrow spectrum of activity. Here, we employed a genome mining approach to identify and functionally characterize novel MccB17-like biosynthetic gene clusters (BGCs) from uncharacterized environmental Pseudomonas isolates. Four BGCs sharing substantial sequence similarity were cloned and heterologously expressed in E. coli. Regardless of their similarity, the clusters triggered congener-dependent growth retardation and cell elongation, despite lacking detectable antibacterial activity in overlay assays. Transcription and production of modified MccB17-like peptides or their unmodified precursors were confirmed by reverse transcription-quantitative PCR (RT-qPCR) and liquid chromatography-mass spectrometry (LC-MS), respectively. Transcriptome profiling indicated differential induction of stress responses and metabolic shifts in host cells, with limited overlap among the different BGCs. Our findings suggest that structurally similar MccB17 congeners can elicit distinct physiological responses. This study expands the known range of MccB17-like RiPPs and highlights the importance of functionally exploring RiPP diversity beyond known antimicrobial activity, even among congeners. KEY POINTS: • In silico identification of uncharacterizedMccB17 congeners from Pseudomonas spp. • Functional analysis of novel variants reveals phenotypic effects on expression host • Transcriptomics reveals congener-specific effects despite similarity to E. coli MccB17.

With the rapid development of artificial intelligence (AI) and machine learning (ML) applications, wafer-level optical I/O is becoming increasingly attractive for massive and efficient data interconnects in future wafer-scale multi-processor-units (multi-XPU) compute clusters with unparalleled data bandwidth, energy efficiency, and low latency. In this paper, we present a 300 mm sized wafer reticle-stitched low-pressure chemical vapor deposition (LPCVD) silicon nitride (SiN) waveguide technology and demonstrate a multi-reticle-stitched ~56 cm long waveguides across 20 reticles with propagation loss of 0.13~0.15 dB/cm at 1310 nm wavelength, and <0.001~0.002 dB SiN waveguide stitch loss, which is because of <5 nm high-precision reticle lithography offset. These advantageous features of low-loss reticle-stitched SiN waveguides have the potential to significantly enhance future optically interconnected wafer-scale multi-chip compute systems.

Driver drowsiness and distracting behaviors are leading causes of road accidents. This paper presents a cost-effective, real-time driver monitoring system (DMS) that leverages Edge Machine Learning (EdgeML) to detect multiple risk factors: drowsiness, phone usage, smoking, and seatbelt non-compliance. The proposed solution integrates an ensemble of models, object detection (OD), facial landmark analysis, and posture estimation, deployed on a Raspberry Pi 5 (RPi 5) with a Coral USB Edge TPU (ETPU) accelerator. A dual-validation logic cross-confirms biometric anomalies with OD within a temporal window to minimize false positives. The system achieves an average F1-score of 98.66% and 90.46% for two- and five-class classification, respectively, and supports real-time processing at 40 FPS with low resource utilization (21% CPU, 500 MB RAM). This work offers a retrofittable EdgeML solution for real-time driver monitoring, democratizing access to advanced safety features for the billions of cars already on the road. The source code and model weights are publicly available<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.