The path towards the application of new microelectronic technologies in the AIDA community

The Workpackage 3 of the AIDA project has the goal of facilitating the access of the high energy physics community to the most advanced semiconductor technologies, from nanoscale CMOS to innovative interconnection processes. The AIDA network is studying 3D integration with the main goal of designing novel tracking and vertexing detector systems based on high-granularity pixel sensors, with aggressive and intelligent architectures for sensing, analogue and digital processing and storage, and data transmission. This talk reviews the ongoing efforts and discusses the challenges that are being tackled in this framework to qualify technologies and devices for actual applications

Data Driven Disaggregation Method for Electricity Based Energy Consumption for Smart Homes

Sustainable energy systems must be capable of ensuring sustainable development by providing affordable and reliable energy to consumers. Hence, knowledge and understanding of energy consumption in the residential sector are indispensable for energy preservation and energy efficiency which can only be possible with the help of consumer participation. New energy efficiency methods are developed due to the global adoption of smart meters that monitor and communicate residential energy consumption. Moreover, energy monitoring of each appliance is not feasible, as it is a costly solution. Therefore, energy consumption disaggregation is an answer for cost-cutting and energy saving. Contrary to the non-intrusive load monitoring (NILM) approaches, which are based on high-frequency power signals, we propose a data- driven algorithm that requires only a time-series energy meter dataset, a few appliances’ data, and energy consumption data from a consumer-based online questionnaire. Afterward, the proposed algorithm disaggregates whole house energy consumption into nine different energy consumption sectors such as lighting, kitchen, cooling, heating, etc. The energy consumption disaggregation algorithm is applied to datasets of 10 homes under experimentation. One of the homes provides us with the knowledge of 96.8% energy consumption, where only 28% knowledge is reported by monitoring plugs and 68% knowledge obtained by unmonitored means. Finally, the energy consumption obtained by the algorithm is compared with actual energy consumption, which shows the excellent functioning of the developed method

Gut dysbiosis and adaptive immune response in diet-induced obesity vs. Systemic inflammation

A mutual interplay exists between adaptive immune system and gut microbiota. Altered gut microbial ecosystems are associated with the metabolic syndrome, occurring in most obese individuals. However, it is unknown why 10-25% of obese individuals are metabolically healthy, while normal weight individuals can develop inflammation and atherosclerosis. We modeled these specific metabolic conditions in mice fed with a chow diet, an obesogenic but not inflammatory diet-mimicking healthy obesity, or Paigen diet-mimicking inflammation in the lean subjects. We analyzed a range of markers and cytokines in the aorta, heart, abdominal fat, liver and spleen, and metagenomics analyses were performed on stool samples. T lymphocytes infiltration was found in the aorta and in the liver upon both diets, however a significant increase in CD4+ and CD8+ cells was found only in the heart of Paigen-fed animals, paralleled by increased expression of IL-1, IL-4, IL-6, IL-17, and IFN-\u3b3. Bacteroidia, Deltaproteobacteria, and Verrucomicrobia dominated in mice fed Paigen diet, while Gammaproteobacteria, Delataproteobacteria, and Erysipelotrichia were more abundant in obese mice. Mice reproducing human metabolic exceptions displayed gut microbiota phylogenetically distinct from normal diet-fed mice, and correlated with specific adaptive immune responses. Diet composition thus has a pervasive role in co-regulating adaptive immunity and the diversity of microbiota

Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854

The SuperB Silicon Vertex Tracker and 3D vertical integration

The construction of the SuperB high luminosity collider was approved and funded by the Italian government in 2011. The performance specifications set by the target luminosity of this machine (> 10^36 cm^-2 s^-1) ask for the development of a Silicon Vertex Tracker with high resolution, high tolerance to radiation and excellent capability of handling high data rates. This paper reviews the R&D activity that is being carried out for the SuperB SVT. Special emphasis is given to the option of exploiting 3D vertical integration to build advanced pixel sensors and readout electronics that are able to comply with SuperB vertexing requirements

PixFEL: development of an X-ray diffraction imager for future FEL applications

A readout chip for diffraction imaging applications at new generation X-ray FELs (Free Electron Lasers) has been designed in a 65 nm CMOS technology. It consists of a 32 × 32 matrix, with square pixels and a pixel pitch of 110 µm. Each cell includes a low-noise charge sensitive amplifier (CSA) with dynamic signal compression, covering an input dynamic range from 1 to 104 photons and featuring single photon resolution at small signals at energies from 1 to 10 keV. The CSA output is processed by a time-variant shaper performing gated integration and correlated double sampling. Each pixel includes also a small area, low power 10-bit time-interleaved Successive Approximation Register (SAR) ADC for in-pixel digitization of the amplitude measurement. The channel can be operated at rates up to 4.5 MHz, to be compliant with the rates foreseen for future X-ray FEL machines. The ASIC has been designed in order to be bump bonded to a slim/active edge pixel sensor, in order to build the first demonstrator for the PixFEL (advanced X-ray PIXel cameras at FELs) imager

A new calibration method for charm jet identification validated with proton-proton collision events at √s = 13 TeV

Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb-1 at √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses

Intelligent systems for particle detectors in environmental applications and High-Energy Physics

Over the last decades, improvements in microelectronics technology have fostered signicant progress in all fields of engineering, science and also in radiation detection. The main challenge in designing radiation detectors is to develop systems based on front-end electronics that is able to cope with high radioactive environment, satisfy very high resolution requirements and comply with high particle rates. This thesis work focuses on the analysis and development of novel and intelligent solutions for electronics system, especially suited for radiation detectors. In particular, two different applications are considered here. The first one concerns the design of a portable and affordable detector system for continuous indoor Radon detection, based on SiPM technology. A simple analog front-end with optimized low-noise performances and reduced power consumption has been designed for counting each alpha particle that occurs in the detector after Radon decay. The readout electronics is integrated with a suite of environmental sensors on a full-custom Printed Circuit Board. Compared to all the commercial Radon detector nowadays available, the developed system is able to detect reliable value of indoor Radon concentration within few hours. The system also exploits the recent capabilities of microelectronic devices by including advanced functions such as Bluetooth data transmission and energy harvesting. In high-energy physics experiments, with particular emphasis on the HL-LHC environment, pixel detectors have to satisfy aggressive requirements concerning high granularity, high rate capability and low power consumption. With the advent of accessible modern technology such as 65 nm CMOS, the processing speed and reduced power consumption can be achieved. In order to meet such specications, a new pixel mixed signal ASIC has been designed as a prototype front-end for the HL-HLC pixel readout system, within the framework of RD53 collaboration. The ASIC front-end includes signal processing and synchronous analog-to-digital conversion within one Bunch Crossing period. Thus, the emphasis of the work is on the feasibility of a synchronous ADC within the HL-LHC environment, able to ensure high performances in terms of low noise, power dissipation and high speed. Finally, a novel and intelligent digital architecture has been proposed, in order to focus the eorts of the front-end on the implementations of three main features: a novel data sparsication method, a clusterization scheme at the hardware level itself and fast Region-Of-Interest (ROI) trigger capability