Towards a Holistic CAD Platform for Nanotechnologies

Silicon-based CMOS technologies are predicted to reach their ultimate limits by the middle of the next decade. Research on nanotechnologies is actively conducted, in a world-wide effort to develop new technologies able to maintain the Moore's law. They promise revolutionizing the computing systems by integrating tremendous numbers of devices at low cost. These trends will have a profound impact on the architectures of computing systems and will require a new paradigm of CAD. The paper presents a work in progress on this direction. It is aimed at fitting requirements and constraints of nanotechnologies, in an effort to achieve efficient use of the huge computing power promised by them. To achieve this goal we are developing CAD tools able to exploit efficiently these huge computing capabilities promised by nanotechnologies in the domain of simulation of complex systems composed by huge numbers of relatively simple elements.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Characterization and Optimization of Radiation at Nano Scale: Applications in Solar Cell Design

High energy needs and environmental concerns associated with fossil fuels have raised the demand for efficient and clean alternatives of power generation. Solar cell technology is one of the most promising options of reliable renewable power sources despite high costs. Thin film solar cells offer the potential for reduction in the cost per kilowatt-hour due to the lower material usage. Nevertheless, most thin film solar cells suffer from low efficiency, though advancements in the science of near field radiation have led to substantial improvements in their optical efficiency. Many design challenges remain to be overcome for the wide-scale commercialization of thin film solar cells. In this dissertation, a numerical study is conducted for optical, optoelectrical and scattering performance enhancement of subwavelength optical devices (i.e., thin film solar cells and light trapping nanoparticles). The proposed design framework of thin film solar cells is based on learning based optimization and characterization methods, which utilize approximations of time consuming simulations. Additionally, a free form nanoparticle design procedure using evolutionary shape optimization is detailed. The background of thin film solar cells and a comprehensive literature review of the thin film solar cell design approaches are provided in Chapters 2 and 3, respectively. The optical enhancement of thin film solar cells using nanoparticles with different shapes is studied in Chapter 4. In Chapter 5, an approximate formulation for optoelectrical efficiency of thin film solar cells is developed to accelerate the design optimization. The learning based design methodology that is introduced in Chapter 5 is further improved in Chapter 6 using a knowledge transfer concept (also known as transfer learning). In this chapter, multiple sets of material combinations are optimized and compared with each other in terms of their optoelectrical efficiencies. In Chapter 7, nanoparticles are designed for maximum scattering, which is desired for enhanced optical performance, using a nonparametric evolutionary design method. In Chapter 8, a predictive model for scattering of arbitrarily shaped nanoparticles using descriptive geometric features is proposed. Overall, this dissertation has led to significant contributions in the field of thin film solar cell design. The results show that the computational burden of the thin film solar cell design can be overcome significantly without sacrificing accuracy. Furthermore, the design methods developed for this dissertation can easily be transferred to other engineering areas involving repetitive, time consuming simulations for design optimization, such as other photonic design problems and integrated circuit design

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations

Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nanophotonic structures, electrodynamic simulations are essential. These simulations enable us to model electromagnetic fields over time and calculate optical properties. In this work, we introduce frameworks and benchmarks to evaluate nanophotonic structures in the context of parametric structure design problems. The benchmarks are instrumental in assessing the performance of optimization algorithms and identifying an optimal structure based on target optical properties. Moreover, we explore the impact of varying grid sizes in electrodynamic simulations, shedding light on how evaluation fidelity can be strategically leveraged in enhancing structure designs.Comment: 31 pages, 31 figures, 4 tables. Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), Datasets and Benchmarks Trac