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International audience; Due to the shrinking size of devices, the calibration process for TCAD simulators is becoming increasingly complex with time mandatory to run simulations steadily increasing. In this work, we propose a Statistical Learning approach to predicting Figures of Merit (FoMs) of a Silicon Germanium Heterojunction bipolar transistor (HBT) 1D doping and germanium profile. The surrogate model thus built allows to simulate electrical outputs with high precision much faster than traditional simulations. This model is then used to solve the inverse design problem and recover profiles that match target FoMs.

In this letter, we provide a detailed characterization of the equilibria and bifurcations of two-dimensional linear-threshold models. Using the input to the system as the bifurcation parameter, we characterize the location of the admissible equilibria, show that bifurcations can arise only when equilibria lie on the boundary of well-defined regions of the state space, and prove that (codimension-one) bifurcations can only be of three different types: persistent, non-smooth fold, and Hopf. We show how these bifurcations change the qualitative properties of the system trajectories, and how these behaviors resemble prototypical patterns of EEG activity observed before, during, and after seizure events in the human brain. Our findings suggest that low-dimensional linear threshold models can effectively be used to model, analyze, predict, and ultimately regulate the interactions of neuronal populations in the human brain.

While 5G wireless networks are currently deployed around the world, preliminary research activities have begun to look beyond 5G and conceptualize 6G standard. Although it is envisioned that 6G may bring an unprecedent transformation of the wireless networks in comparison with previous generations, the necessity to develop analog and RF specialized technologies to address new frequency spectra will remain. In this paper, we review the development of PD-SOI CMOS and SiGe BiCMOS technologies addressing 5G RF Integrated Circuits (RFICs) and their evolutions for 6G.

A concise overview on the features of the most recently released version 3.0.0 of the industry standard compact bipolar transistor model HICUM/L2 is provided. The focus here is on the development of the model over the past ten years since its detailed description in [1]. The rationales for the various extensions, their physical background and some of the code implementation peculiarities are discussed.

Education is one of the most critical areas for the development and growth of a country, for this reason it is important to innovate in teaching methodologies and leave behind traditional teaching. In this context, this work proposes the development of a dynamic game that uses the accomplishment unit of the Octalysis framework based on gamification to improve the teaching-learning process in children. As a result, the use of a dynamic game with gamification for teaching improves the learning capacity since it encourages children to pay more attention to the class.

In this paper, we present an in-situ thru-reflect-line (TRL) calibration and de-embedding kit that sets the reference plane in close proximity to the device under test. This is made possible thanks to the realization of the standards at the metal3 BEOL level, instead of the common meta1-8 solution. This novel calibration kit has been compared to classic TRL, both for parasitics assessment and by direct application on the active device (HBT) measurements.

The undesired behavior of the substrate significantly affects the output impedance of the device; hence degrades circuit performance mainly in the high frequency regime. Therefore, for high-speed and RF circuits, collector-substrate modeling has to be sufficiently accurate. In this paper, an improved collector-substrate equivalent circuit model is proposed. The circuit model elements are physics based and are calculated from technological data. The validity of the equivalent circuit has been verified by on-wafer measurements of an SiGe HBT fabricated in B55 technology up to 330 GHz, the highest frequency reported so far for collector-substrate modeling. The proposed substrate network can be considered as an extension of the latest large-signal HICUM model (L2v2.4).

Traditional Intensive Care Unit (ICU) outcome prediction models are based on the analysis of large populations, and often provide statistically rigorous results for an average patient but are also expensive, time-consuming, and prone to selection bias; moreover, these indicators usually lack the precision required for use at the individual level, since they present significant errors at patient data away from the average. In this work we present an evaluation of the impact and relevance of three different patient similarity metrics on the one-year mortality prediction when patients are related by the same diagnosis, sepsis. We use data of 16.000 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC-III) database.

This paper proposes a decision support system based on fuzzy clustering, fuzzy modeling and fuzzy fingerprints, to provide personalized therapy for critically ill patients. It is hypothesized that the ‘collective experience’ from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the Intensive Care Unit (ICU). The validity of the proposed systems will be tested using the case study of patients admitted to the ICU who then develop acute kidney injury (AKI); Two-thirds of patients with AKI require renal support therapy. Generalized severity scoring systems have consistently performed poorly for patients with AKI.

Up to two thirds of critically ill patients in Intensive Care Units (ICU) around the world develop Acute Kidney Injury (AKI). This complication is characterized by a reduction of kidney function and is intrinsically linked to an increased risk of mortality. AKI is also connected to higher costs in healthcare due to longer hospital stay times and ICU readmissions for affected patients. The present study developed a model capable of predicting which patients are at a larger risk of death and therefore allow for a more efficient use of hospital resources. Information regarding several physiological parameters of patients with AKI as well as data regarding their survival was used for the creation of a fuzzy model. The model achieved AUC values of up to 0.92 when using mean values for time series and analyzing a subset of patients with AKI at its most advanced stage. When analysing a general population of ICU patients, the algorithm can achieve AUC values of 0.86. This value can be compared with values obtained in similar studies of 0.85 and surpasses values obtained in traditional risk scores such as APACHE II and SOFA. A model such as this, shows the potential of using this type of predictive applications for scheduling human resources and managing monetary resources on critical patients according to their relative need for attention and treatment.

This paper presents different de-embedding methods applied in semiconductor industry, used to retrieve intrinsic device performances from high frequency S-parameters On-wafer measurement. A de-embedding method with a reduced set of dummies is proposed for conducting accurate on-wafer device measurement in the gigahertz range. The experimental results on a device characteristic up to 110GHz show that it has a comparable accuracy than a more complex one.

In this paper we study and analyze the existing techniques in literature to extract the self-heating thermal resistance from the measured DC electrical behaviour of silicon-germanium heterojunction bipolar transistors (SiGe HBTs) focusing their dependence on device junction temperature and propose a simple extraction technique that shows superior accuracy than the existing extraction methodologies. Our approach is scalable and validated with model card simulations across different emitter geometries for a wide temperature range. We also present the applicability of our approach on measured data of a SiGe HBT fabricated in STMicroelectronics B55 process.

The cut-off frequencies of silicon-germanium hetero-junction bipolar transistors (SiGe HBTs) have entered the THz range at the cost of high current density and relatively low breakdown voltages. Typically, the common-emitter breakdown voltage with open base (BVCEO) is used to indicate the allowed breakdown voltage related operation limit. However, an open base (i.e. an infinite source impedance) is rarely encountered in actual circuits, so that BVCEO may be exceeded to a certain extent, maximal up to the open-emitter breakdown voltage BVCBO. Therefore, compact HBT models need to be accurate beyond BVCEO up to BVCBO. In this paper, the enhancement of the avalanche current implemented in the latest version of HICUM/L2 is presented. The model has been validated for different types of advanced SiGe:C HBTs over a wide range of collector-base voltages and temperatures.

Intensive care treatment presents unique challenges in the medical world. When treating patients, their wide variety leave care providers with few past examples to draw on. Instead of operating in a pure knowledge discovery capacity, decision support systems can be developed to help predict short-term and long-term patient outcome, based upon available data. One area in which generalized severity scoring systems have consistently performed poorly is among patients admitted intensive care units (ICU) who then develop acute kidney injury. Urine output is used to guide fluid resuscitation and is one of the criteria for the diagnosis of acute kidney injury. This paper provides an example application for predicting short-term critical kidney function in an intensive care unit. Feature construction is performed to extract important aspects of the clinical evolution of the patient. Feature selection is performed on several patient features. Classifiers based on support vector machines and Takagi-Sugeno fuzzy models are developed to predict short-term drops in patient urine output rate. Both types of models showed comparable results, with an AUC of 78%. This shows potential in using similar classifiers to build an ICU decision support system with the goal of predicting short-term complication in the patient and augment current guidelines by anticipating treatment.