674 research outputs found

    Ancient and historical systems

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    Indirect interactions between magnets

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    Modeling & Simulation of High Performance Nanoscale MOSFETs

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    Silicon-on-insulator (SOI) has been the forerunner of the CMOS technology in the last few decades offering superior CMOS devices with higher speed, higher density and reduced second order effects for submicron VLSI applications.A new type of transistor without junctions and no doping concentration gradients is analysed and demonstrated. These device structures address the challenge of short channel effects (SCEs) resulting with scaling of transistor dimensions and higher performance for deep submicron VLSI integration. Recent experimental studies have invigorated interest in partially depleted (PD) SOI devices because of their potentially superior scalability relative to bulk silicon CMOS devices. SELBOX structure offer an alternative way of suppressing kink effect and self heating effects in PD-SOI devices with a proper selection of oxide gap length. Also in order to mitigate the difficulties in fabrication of ultra thin devices for the semiconductor industry, resulting from scaling of gate length in MOSFET, a new device structure called junctionless (JL) transistors have recently been reported as an alternative device. In conclusion, extensive numerical simulation studies were used to explore and compare the electrical characteristics of SELBOX SOI MOSFET with a conventional single-material gate (SMG) bulk MOSFET. The proposed work investigates the DC and AC characteristics of the junctionless transistors. Also the performance analysis of JL transistors is compared and presented with the conventional DG MOSFET structure. The results presented in this work are expected to provide incentive for further experimental exploration

    Towards Oxide Electronics:a Roadmap

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    At the end of a rush lasting over half a century, in which CMOS technology has been experiencing a constant and breathtaking increase of device speed and density, Moore's law is approaching the insurmountable barrier given by the ultimate atomic nature of matter. A major challenge for 21st century scientists is finding novel strategies, concepts and materials for replacing silicon-based CMOS semiconductor technologies and guaranteeing a continued and steady technological progress in next decades. Among the materials classes candidate to contribute to this momentous challenge, oxide films and heterostructures are a particularly appealing hunting ground. The vastity, intended in pure chemical terms, of this class of compounds, the complexity of their correlated behaviour, and the wealth of functional properties they display, has already made these systems the subject of choice, worldwide, of a strongly networked, dynamic and interdisciplinary research community. Oxide science and technology has been the target of a wide four-year project, named Towards Oxide-Based Electronics (TO-BE), that has been recently running in Europe and has involved as participants several hundred scientists from 29 EU countries. In this review and perspective paper, published as a final deliverable of the TO-BE Action, the opportunities of oxides as future electronic materials for Information and Communication Technologies ICT and Energy are discussed. The paper is organized as a set of contributions, all selected and ordered as individual building blocks of a wider general scheme. After a brief preface by the editors and an introductory contribution, two sections follow. The first is mainly devoted to providing a perspective on the latest theoretical and experimental methods that are employed to investigate oxides and to produce oxide-based films, heterostructures and devices. In the second, all contributions are dedicated to different specific fields of applications of oxide thin films and heterostructures, in sectors as data storage and computing, optics and plasmonics, magnonics, energy conversion and harvesting, and power electronics

    Ultra-low Power Circuits and Architectures for Neuromorphic Computing Accelerators with Emerging TFETs and ReRAMs

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    Neuromorphic computing using post-CMOS technologies is gaining increasing popularity due to its promising potential to resolve the power constraints in Von-Neumann machine and its similarity to the operation of the real human brain. To design the ultra-low voltage and ultra-low power analog-to-digital converters (ADCs) for the neuromorphic computing systems, we explore advantages of tunnel field effect transistor (TFET) analog-to-digital converters (ADCs) on energy efficiency and temperature stability. A fully-differential SAR ADC is designed using 20 nm TFET technology with doubled input swing and controlled comparator input common-mode voltage. To further increase the resolution of the ADC, we design an energy efficient 12-bit noise shaping (NS) successive-approximation register (SAR) ADC. The 2nd-order noise shaping architecture with multiple feed-forward paths is adopted and analyzed to optimize system design parameters. By utilizing tunnel field effect transistors (TFETs), the Delta-Sigma SAR is realized under an ultra-low supply voltage VDD with high energy efficiency. The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM\u27s conducting filament with built-in stochasticity is used to mimic the neuron\u27s membrane capacitor, which temporally integrates input spikes. A capacitor-less neuron circuit is designed, laid out, and simulated. The output spiking train of the neuron obeys the Poisson distribution. Based on the ReRAM based neuron, we propose a scalable and reconfigurable architecture that exploits the ReRAM-based neurons for deep Spiking Neural Networks (SNNs). In prior publications, neurons were implemented using dedicated analog or digital circuits that are not area and energy efficient. In our work, for the first time, we address the scaling and power bottlenecks of neuromorphic architecture by utilizing a single one-transistor-one-ReRAM (1T1R) cell to emulate the neuron. We show that the ReRAM-based neurons can be integrated within the synaptic crossbar to build extremely dense Process Element (PE)–spiking neural network in memory array–with high throughput. We provide microarchitecture and circuit designs to enable the deep spiking neural network computing in memory with an insignificant area overhead

    Integrating ultrafast all-optical switching with magnetic tunnel junctions

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    The 2017 Terahertz Science and Technology Roadmap

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    Science and technologies based on terahertz frequency electromagnetic radiation (100GHz-30THz) have developed rapidly over the last 30 years. For most of the 20th century, terahertz radiation, then referred to as sub-millimeter wave or far-infrared radiation, was mainly utilized by astronomers and some spectroscopists. Following the development of laser based terahertz time-domain spectroscopy in the 1980s and 1990s the field of THz science and technology expanded rapidly, to the extent that it now touches many areas from fundamental science to “real world” applications. For example THz radiation is being used to optimize materials for new solar cells, and may also be a key technology for the next generation of airport security scanners. While the field was emerging it was possible to keep track of all new developments, however now the field has grown so much that it is increasingly difficult to follow the diverse range of new discoveries and applications that are appearing. At this point in time, when the field of THz science and technology is moving from an emerging to a more established and interdisciplinary field, it is apt to present a roadmap to help identify the breadth and future directions of the field. The aim of this roadmap is to present a snapshot of the present state of THz science and technology in 2016, and provide an opinion on the challenges and opportunities that the future holds. To be able to achieve this aim, we have invited a group of international experts to write 17 sections that cover most of the key areas of THz Science and Technology. We hope that The 2016 Roadmap on THz Science and Technology will prove to be a useful resource by providing a wide ranging introduction to the capabilities of THz radiation for those outside or just entering the field as well as providing perspective and breadth for those who are well established. We also feel that this review should serve as a useful guide for government and funding agencies

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations
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