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Item type:Publication, Access status: without files , Modeling and improving transmission of high-speed vias on PCBs up to 200 Ghz(IEEE, 2026-06-14); ; ; ; Sismanis, NikolaosAs high-speed interconnects on printed circuit boards (PCBs) advance into the beyond 400 Gbps domain, accurate characterization of vias becomes critical. Via arrays are investigated at frequencies up to 200 GHz using different full-wave (FW) simulation tools, yielding good correlation. Scattering phenomena play a more significant role than losses in the behavior of these arrays. Considerations essential for improved interconnect performance in next-generation highspeed communication systems, increasing bandwidth by 20 GHz, are outlined. Coax vias and interstitial ground vias improving transmission are investigated.Publicationtype: Conference Paper3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Relational SI/PI-database for a data-driven approach to PCB design automation and performance predictionThe introduction of machine learning (ML) methods into the design process of printed circuit boards drives the need for large quantities of readily available data. This paper addresses the problems of engineers to find ML-ready data that can be easily reused and combined to enhance printed circuit board (PCB) design by storing the defining parameters in a normalized format within a relational database It implements search and filter functions to obtain relevant data quickly. The database contains data that was used to address a variety of different signal integrity (SI) and power integrity (PI) related problems. Details of the database structure, necessary data conversion steps, currently stored datasets, and a statistical analysis there of are described. This database is capable to be automated to a degree that ML agents can interact with it.Publicationtype: Journal Article25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Unified pre-processing steps reducing the PCB design space to enable ML applications for signal and power integrity analysisMachine Learning (ML) applications need large quantities of data spanning the complete Printed Circuit Board (PCB) design space. This design space needs to be reduced to the subset of relevant PCB configurations, i.e. those yielding network parameters which are different from each other. For this purpose a preliminary design space analysis is carried out and pre-processing steps are presented to unify the application of ML in the Signal and Power Integrity domains and pave the way to a standardized PCB representation format.Publicationtype: Conference Paper17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Generation and application of a very large dataset for signal integrity via array and link analysis(IEEE, 2024-09-18); ; ; ; In this article, a physics inspired and data-driven analysis is performed using a very large dataset of printed circuit board (PCB) based high-speed interconnects. Three different subsets are defined having approximately 15000 PCB material and geometry variations. To the authors' knowledge, this dataset is the largest ever generated and applied dataset for signal integrity (SI) purposes. The dataset is generated using accurate physics based (PB) modeling up to 60 ,mathrm{G}mathrm{Hz} and provided for download in the SI/PI-Database. The first two subsets consist of a single-ended and a differential via array with up to 116 ports with arbitrary positions for signal, power, and ground vias. The third subset represents a link over differential transmission lines between two via arrays. A data and machine learning (ML) analysis is performed showing the dependencies between the PCB parameters and clock frequency of PCI Gen 6. The large and realistic defined design spaces reflect the high complexity and dimensionality of the SI design problems and allow multipurpose ML-based analysis.Publicationtype: Journal Article21