International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

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Design Of Analog Cmos Integrated Circuits

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion. Such design leverages the strengths of FCN for pixel-level prediction and the strengths of LSTM for learning complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as learning residual functions with reference to the sum of densities in each frame, which significantly accelerates the training of networks. To preserve feature map resolution, we propose a Hyper-Atrous combination to integrate atrous convolution in FCN and combine feature maps of different convolution layers. FCN-rLSTM enables refined feature representation and a novel end-to-end trainable mapping from pixels to vehicle count. We extensively evaluated the proposed method on different counting tasks with three datasets, with experimental results demonstrating their effectiveness and robustness. In particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21 on TRANCOS; and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process is accelerated by 5 times on average.

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

Vehicle speed is a key variable for the calibration, validation, and improvement of traffic emission and air quality models. Lidar technologies have significant potential in vehicle tracking by scanning the surroundings in 3-D frequently, hence can be used as traffic flow monitoring sensors for accurate vehicle counting and speed estimation. However, the characteristics of lidar-based vehicle tracking and speed estimation, such as attainable accuracy, remain as open questions. This research therefore proposes a tracking framework from roadside lidar to detect and track vehicles with the aim of accurate vehicle speed estimation. Within this framework, on-road vehicles are first detected from the observed point clouds, after which a centroid-based tracking flow is implemented to obtain initial vehicle transformations. A tracker, utilizing the unscented Kalman Filter and joint probabilistic data association filter, is adopted in the tracking flow. Finally, vehicle tracking is refined through an image matching process to improve the accuracy of estimated vehicle speeds. The effectiveness of the proposed approach has been evaluated using lidar data obtained from two different panoramic 3-D lidar sensors, a RoboSense RS-LiDAR-32 and a Velodyne VLP-16, at a traffic light and a road intersection, respectively, in order to account for real-world scenarios. Validation against reference data obtained by a test vehicle equipped with accurate positioning systems shows that more than 94% of vehicles could be detected and tracked, with a mean speed accuracy of 0.22 m/s.

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Vehicle Tracking and Speed Estimation From Roadside Lidar

In this communication, compact dual-bandgap two via double-slot-type electromagnetic bandgap (TVDS-EBG) structure is presented. The analysis of the TVDS-EBG structure is done by using the $LC$ model. Two resonance parallel $LC$ circuits per unit cell result dual-bandgap in the $x$ - and $y$ -directions. Compactness is achieved because of two via per unit cell, square slot, and inverse C-type slot on an EBG patch. The simulation of the unit cell TVDS-EBG is carried out using an eigenmode solution of Ansys high-frequency structure simulator. Simulated results are validated by experimental results. Simulated and experimental results prove that compared with a two-layer edge located via interdigital-EBG, edge located via-EBG, and Minkowski EBG, TVDS-EBG gives size reduction of 20.48%, 29.03%, and 31.25%, respectively, with a simple structure. The application of the proposed dual-bandgap TVDS-EBG for ultrawideband monopole antenna is also demonstrated. Simulated and measured results show that dual-band-notched can be obtained by using a unit cell of TVDS-EBG at fed line of an elliptical monopole antenna.

A Compact Dual Band Gap Electromagnetic Band Gap Structure

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Image Processing Based Intelligent Traffic Controller

A novel two via slot-type electromagnetic bandgap (TVS-EBG) structure is presented in this letter. Design and analysis of the TVS-EBG structure are done by using the $LC$ analytical model. In the central located via EBG (CLV-EBG) and the edge located via EBG (ELV-EBG) structure, current flowing through via gives the inductor ( $L$ ) and the gap between the adjacent cells gives the capacitance ( $C$ ). From the $LC$ analytical model, in order to design compact EBG structure, the product of $LC$ should be high. TVS-EBG structure increases the value of $C$ and $L$ per unit EBG cell by making a slot on the square patch of the cell and two via between square patch of the cell and ground plane. TVS-EBG structure simulated using Ansoft HFSS and tested. Simulated and experimental results prove that, compared with CLV-EBG, complementary split ring resonator-based EBG, and ELV-EBG, a size reduction of 35.29%, 17.20%, and 13.48% has been achieved, respectively.

A Compact Two Via Slot-Type Electromagnetic Bandgap Structure

In this brief, we have analyzed the response of silicon-nanowire (Si-NW) gate-all-around (GAA) field-effect transistor to total ionizing dose (TID) effects and assessed the impact of single-event effects (SEEs) in simple inverter circuit built from such devices. The analysis of radiation effects is carried out with 3-D technology computer-aided design simulations. Reliability of n-channel and p-channel Si-NW MOSFET is investigated for TID effects with gamma ray exposure. The transient effects at the device level are studied for alpha particle and heavy-ion strikes. It is found that Si-NW MOSFET is inherently hardened to TID effects. This result is in concordance with the earlier reported experimental results. However, we found that Si-NW CMOS inverter is not as tolerant to SEE, as Si-NW MOSFET is to TID. This study highlights the need for radiation-hardened Si-NW FET circuits against SEE.

Radiation Effects in Si-NW GAA FET and CMOS Inverter: A TCAD Simulation Study

Radiation effects in microelectronic components have been studied for several decades for military and space applications. Understanding radiation effects in electronics is a complex and continually evolving challenge. In this paper, we present an extensive literature review pertaining to the state-of-the-art issues in radiation effects and radiation-hardened circuit design. Various research papers, books and application notes were referred, which take care of various aspects of understanding of radiation effects. In particular, this review gives a bibliographical survey of the current research efforts in the area of radiation effects in metal-oxide-semiconductor based devices and circuits. The paper also presents some of the challenges anticipated in the years to come and enlists further scope for research. A full description of the various methods is beyond the scope of this article; instead, the focus is on providing primary developments that have taken place in the area of radiation effects.

Surendra S. Rathod

Papers

A Compact Dual Band Gap Electromagnetic Band Gap Structure

Image Processing Based Intelligent Traffic Controller

A Compact Two Via Slot-Type Electromagnetic Bandgap Structure

Radiation Effects in Si-NW GAA FET and CMOS Inverter: A TCAD Simulation Study

Radiation Effects in MOS-based Devices and Circuits: A Review