Search

With the aggressive scaling of MOSFET devices below 3nm, the role of TCAD in selecting a feasible device architecture for next node has become extremely important. There is an enormous opportunity cost for each choice, so the pros and cons of each option must be identified through seamless pre-validation using TCAD. Therefore, it is important to understand which TCAD solutions are necessary to validate the architecture candidate in rigorous way. This paper describes which TCAD solutions are important in atomic/device/standard cell/block-chip level for next generation logic pathfinding from the perspective view of a semiconductor manufacturing industry.

A novel algorithm is introduced to extract the electrical roughness at the first principle level. The autocorrelation and root-mean-squared height of roughness spectrum are extracted for 3×3 nm2 Si/SiO 2 samples. As an application, the impact of Ge-O bond on electrical roughness is demonstrated. The result concludes that electrical roughness of a given interface can be substantially larger than geometrical values.

The widespread use of CCTV and various image devices has become a primary cause of privacy invasion, because these are possible to record, share and leak privacy images without owners’ consent. The image privacy masking is one of technology for privacy prevention, and its necessity has been increased owing to need of personal information protection and social safety in these days [1]. In this paper, we propose a block-based masking region relocation and detection method to overcome the shortage of the meta-data typed privacy region information sharing method of restorable image masking service. This method is based on the histogram difference between the original image and the masked image. It analyzes the histogram feature of images, and relocates the masking region information from the coordinate system to the block system. Therefore, it set and detect the masking region without additional information such as meta-data and the original image. For demonstration the feasibility of our approach, we used the real-world database and the experimental results show the applicability of the real privacy masking service.

Robot learning by demonstration is a research paradigm that can play an important role in expanding areas where robots can be applied. An easy method to get a policy to reproduce the demonstrated behavior is to learn a model that maps directly from demonstrations to action. This is referred to as behavioral cloning (BC). In this paper, a novel robotic BC method for directly imitating a policy from trajectories generated by performing a task is proposed. The proposed method not only allows a user to obtain a demonstrated trajectory for performing a task by manipulating the robot, but also automatically generates a trajectory by having the robot perform the task on behalf of the user. Experiment results showed that the proposed method can effectively learn to make accurate behavior prediction for robot manipulators.

Recently, reinforcement learning utilizing deep learning, called deep reinforcement learning, is being developed for discontinuous action space. It is shown that deep reinforcement learning has higher playing skills than those of human in the video game as Atari. The development of deep reinforcement learning for discontinuous action space, has been extended to the continuous action space as robot control area. The area of control deals with issues closely related to real world environment. However, deep reinforcement learning has limitations to solve control problems in the real world due to numerous trial-and-error based training. To address the limitations of learning in the real world, virtual world environments are used to emulate control problem in real world. In this paper, to solve a complex robot manipulation task, the learning robot manipulation based modular reward shaping is proposed. Since it is difficult to solve the complex task using only single primitive manipulation skill with a reward, it is required to combine various primitive manipulation skills with various rewards. In simulation results, it is shown that the combination of the reward functions affects to the robot manipulation performance.

Advances in deep reinforcement learning enabled cost-efficient training of control policy of physical robot actions from robot simulators. Learning control policy in a simulated environment is cost-efficient over learning in a real environment. Reward engineering is one of the key components to train efficient control policy. For tasks with long horizons such as navigation and manipulation, a sparse reward is providing limited information. The robot simulator for a physical engine of physical robot manipulation has made it easy for researchers in the field of deep reinforcement learning to simulate complicated robot manipulation environments. In this paper, A robot manipulation simulator and a deep RL framework are utilized for implement a training control policy by utilizing previous experiences. For implementation, Recent innovation Hindsight Experience Replay (HER) algorithms with previous experiences to calculate dense rewards from a sparse reward is leveraged . Proposed implementation showed an approach to investigate the reward engineering method to formulate dense reward in robot manipulator tasks.

This paper provides an experimental study of reinforcement learning on IoT devices using distilled knowledge, whose a teacher with a well-trained model transfers to a student with a new model to be trained. The experimental results show that the distilled knowledge is effective to a new model training on IoT devices.

Fault detection of Photovoltaic (PV) is one of the important issues in PV Farm. There is a way to monitor power for PV failure detection, but it is necessary to connect a device for measuring power at all PV. Therefore, PV Farm owners have to more spend cost for monitoring as number of PV. In this paper, we propose a monitoring system that does not require connection of power sensors, communication modules and processors to each PV. For this purpose, generated power is modulated depending on specified frequency band.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

Today, a paradigm in the manufacturing industry has been shifted from mass production to small and custom production. To adapt to today’s industrial expectations, this paper presents position-based visual servoing of multiple robotic manipulators. The presented visual servoing consists of two modules: task planning and motion planning. The task planning module manipulates each robotic arm not to collide with each other by distributing a task to them in a systematic manner. The motion planning module employs the MoveIt framework to solve inverse kinematics for each robotic arm so that it moves each end-effector’s pose to the desired pose to complete the task. In addition, the module controls a robotic hand to grasp or release a target object. The presented visual servoing has been tested and verified in the Gazebo simulator.

Applying deep learning to symbolic planning is not straightforward because they differently internalize knowledge. One represents knowledge numerically while the other does symbolically. Hybrid methods that take strengths from both techniques will deliver better performance on automated planning. This paper presents a method to improve the performance of symbolic planning by applying LSTM recurrent networks to inferring the guidance knowledge for search tree node expansion. The networks learn sequential patterns across previous node expansions and generalizes the learned knowledge to predict promising actions and their probability scores at the present expansion step. The prediction consists with node cost estimation for heuristic search. A simple experiment vindicates the method of this research.

This paper addresses a continual lifelong learning problem that learns incremental multiple tasks in real-world environments. We overview and summarize representative approaches and categorization of the state-of-the-art in continual learning. Comparable scenarios, benchmark datasets, and baseline approaches for different continual scenarios introduced in this paper. We suggested a comparison of the differences and similarities with other machine learning methods. We also report real-world applications, especially robots and healthcare fields. We summarize current states and suggest future direction of continual learning problems.

This paper proposes a system design for automation in multi-agent-based manufacturing systems to conduct a given complex task automatically by controlling multiple robotic manipulators in a systematic manner. To this end, the proposed system is designed with three-module configurations: environmental perception, task planning, and motion planning. The environmental perception module utilizes a vision sensor to recognize all objects placed on the workspace and extract their unique ID, size, and pose. The task planning module divides a given task into primitive skill levels and distributes each primitive skill to the associated robotic manipulator with the relevant object information in a systematic manner for robotic manipulators not to collide with each other. The motion planning module determines the motion of a robotic arm and a robotic hand by solving inverse kinematics for the robotic arm and by opening or closing two fingers. The proposed system has been tested and verified in real robot environments through a complex task "peg in hole" that requires at least two robotic manipulators.

Peak shaving is an important technology in power grid. Many studies on peak shaving are being conducted, using ESS, shiftable load, and V2G. However, they do not consider of the ESS SoC and supply power reduction because they are only for peak shaving. ESS SoC is related to battery life which is directly linked to costs. Reduction supplying power causes problems that uncertainty and inconvenience to customers. Therefore, it is necessary to consider the problems of peak shaving. This paper presents ESS SoC optimization by controlling that charging and discharging of EV. It can reduce supplying power and peak shaving.

This paper designs and analyzes radar antenna for automobiles that can operate at 24 GHz and 77 GHz. This radar antenna is designed on LTCC substrate for highly integrated module. The LTCC substrate has a reflector at an arbitrary position to improve the antenna patterns. The pattern of the reflector antenna has dual beam characteristics that the central portion of the pattern is reduced. Therefore, this paper adds an air layer to the LTCC substrate to improve this disadvantage. As a result, the pattern has been improved to have a single beam characteristic. This paper designs and analyzes a single beam offset parabolic reflector(OPR) antenna for a highly integrated module that can operate in the millimeter wave. This paper also suggests the possibility of integration of monopole antennas for short and long distance radar systems.

This paper investigates the impact of BEOL design on device and backend reliability – HCI, BTI, EM – due to dependence of self-heating on BEOL in highly-scaled FinFETs. Our analysis indicates that due to poor thermal coupling to substrate – in the thin fin body devices – a large part of heat flows out of BEOL. This makes self-heating, and thus device (FEOL) temperature, very sensitive to BEOL design. The heat flow through BEOL also significantly increases the metal and via temperatures. The increased temperature negatively affects the overall reliability, and one of the ways to mitigate device degradation is optimization of BEOL design.

This paper proposes a new method to detect mechanical faults of permanent magnet synchronous motors (PMSMs) under variable speed conditions. Several prior studies have proposed motor current signature analysis (MCSA) based methods for transient conditions; however, these methods have limitations because they require the characteristic frequency of the motor or they only verify the performance of the methods for a restricted time-varying region. Thus, the research outlined in this paper suggests a method for detecting motor faults using stator currents. The proposed method uses two techniques, continuous wavelet transform (CWT) and distance approach. In this method, after the influence of the non-stationary condition is reduced in the wavelet coefficients, the distance of the residual signal from the distribution of normal state is calculated. The performance of the proposed method is confirmed with the simulation result examining unbalance. From the results, the proposed method demonstrates better performance in small-load under non-stationary conditions.

Non-Hermitian singularities known as exceptional-points (EPs) have been shown to exhibit increased sensitivities but the observation of EPs has so far been limited to wavelength scaled systems subject to diffraction limit. We propose a novel approach to EPs and report their first observation in plasmonics at room temperature. The plasmonic EPs are based on the hybridization of resonances in multilayered plasmonic crystals to reach a critical complex coupling rate between nanoantennas arrays, and, resulting in the simultaneous coalescence of the resonances and loss rates. Because plasmons shrink the wavelength of light to make it compatible with biological relevant substances, enhanced sensing of anti-Immunoglobulin G, the most common antibody found in blood circulation, is observed at attomolar concentration. Our work opens the way to novel class of nanoscale devices, sensors, and imagers based on topological polaritonic effects.

This paper proposes a method for detecting inter-turn short circuit faults of surface-mounted permanent magnet synchronous motors (SPMSMs) based on reference voltage. The proposed method consists of three steps. First, from the mathematical model of faulty PMSM, a reference voltage vector in the dq-frame is separated into a healthy voltage and a faulty voltage. Second, the faulty voltage is further decomposed into a DC component and a 2nd harmonic component. Third, the relationship between these components and the inter-turn fault severity is identified. Based on the relationship, this paper proposes two fault indicators; DC bias and 2nd harmonic amplitude. Simulation results verify the effectiveness of the proposed method. The proposed method can detect inter-turn short circuit faults under low sampling rate and without the need for additional sensors. This strength can facilitate on-line fault detection.

As demanding the ultrashort bunches for the pump & probe experiments to investigate ultrafast phenomena occurring at atomic size level, the diagnostic tools should be developed to measure. by measuring the electron diffraction pattern directly. We have developed an S-band RF deflecting cavity working on $\mathrm{TM}_{120}$ mode to measure the femto-second bunch duration of electron beam in UED (Ultrafast Electron Diffraction) system at KAERI. In this conference, we will present on design, fabrication and experimental performance of the deflecting cavity. The estimated temporal resolution of RF deflector, operating with the deflecting voltage of 1.5 MV and the drift length of 1.7 m, is 50 fs. The electron beam with 3 MeV and 1.88 pC, electron bunch duration has 67 fs in rms, which is well agreed with the simulation results.

The current network access control solution focuses only on the setup for the connection control of each gateway or VPN equipment and the status monitoring function of each equipment. In addition, each secure connection configuration between the corresponding network access nodes making it easy for the target node to be protected is likely to be exposed to malicious attacks. Therefore, in this paper, we propose a trust-based network access control and protect system (TAPS) that enables the software-driven definition and control of a network tunnel for protection between devices and services. The proposed system was designed to only grant access to specified services to mutually authorized devices by predefining and mapping a list of devices and services. In addition, data traffic between each device and service has been developed to be resistant to a variety of cyber attacks by encrypting it according to its purpose.

In this paper, the accelerated thermal deterioration test was carried out in order to evaluate the characteristics of the insulation breakdown of the insulating paper used in the oil type transformer. Characteristics of the insulation breakdown due to the thermal stress of the insulating oil-impregnated insulating paper were analyzed. The insulation materials used in tests were three types of aramid papers. Which was widely used as the main coil insulation of the transformer. And silicone oil, which was synthetic insulating oil was used. Accelerated thermal deterioration test was conducted by IEEE/ANSI Standard C57.91-based on the life expectancy curve of 1981, the H class insulation system was used as a reference in order to comply with main transformer of AC electric multiple unit. The thermal stress did at 40°C ∼180°C and the insulation breakdown characteristics of the insulating paper were test. For the analysis, the strength of insulation breakdown depends on the temperature.

In most major cities, there are certain areas with insufficient sewage systems that are incapable of moving drainage water efficiently, especially in heavy rain scenarios. Using CCTV (Closed-circuit television) cameras in order to detect such water pooling requires human focus and attention, however, immediate action has to be taken to minimize the environmental impact of such incidents. Detecting flooding not only might help save people’s and city’s money and time, but also human lives as well. In this paper, we propose a deep learning approach to detect flooding. Using the method of image segmentation and state-of-the-art architecture, system is capable of detecting flooding early on.

Intelligent design technology co-optimization (iDTCO) methodology for next generation logic architecture pathfinding and its application results are presented in this paper. There are 2 major steps in our iDTCO framework; standard cell (STC)-level iDTCO and block-level iDTCO. STC-level iDTCO, the main focus of this paper, consists of 4 major components; (1) full 3D process emulation with litho contour of standard cell (STC) layout, (2) auto-extraction of transistor compact model & parasitic RC extraction (PEX) in 3D, (3) performance-power-yield (PPY) analyzer, (4) multi-objective optimization of layout & process assumption (PA) to get best PPY. Applying our STC-level iDTCO flow to logic arch pathfinding, we could speed up our PPY analysis TAT by 5$\sim$10 times with good accuracy of >95%.

Extending tungsten contact for the most advanced nodes (≤ 7 nm) is challenging due to the growing impact of contact resistance on the overall resistance of a device and to the increasing difficulty of gapfill in features with < 20 nm critical dimensions. The paper presents a gapfill material using metalorganic chemical vapor deposition (MO-CVD) cobalt for contact plug. Highlights of new gapfill material include proven seamless, voidless gapfill and contact resistance reduction. CVD Cobalt anneal parameters are discussed that can be optimized in combination with the deposition process to achieve desired gapfill. A proprietary electron-beam imaging technology was used to qualify the cobalt fill for void-free performance. Various process flows are discussed that lead to the best-known fill and resistance reduction values.

This paper presents an architecture for cyber-physical systems resilient against external attacks and internal faults. The target CPS consists of multiple physical systems equipped with local embedded systems, a supervision module that oversees operations of the physical systems, and communication network connecting each physical system to the supervision module. We give an instantiation of the architecture on a radio-based train control system to demonstrate the resiliency under various safety critical scenarios. The testbed entitled KRS-DGIST includes a commercial train control and supervision software deployed in the Philippines. The railway installed sensors and other mechanisms are implemented reflecting the actual train control and supervision system, and the dynamics of the trains is computer simulated. Demonstrations are given with attacks on sensors, communication network, and embedded systems.

A data stream is a sequence of data generated continuously over time. A data stream is too big to be saved in memory and its underlying data distribution may change over time. Outlier detection aims to find data instances which significantly deviate from the underlying data distribution. While outlier detection is performed at an individual instance level, anomalous pattern detection involves detecting a point in time where the behavior of the data becomes unusual and differs from normal behavior. Most outlier detection methods work in unsupervised mode, where the class labels of the data samples are not known. Alternatively, concept drift detection methods find a drifting point in the streaming data and try to adapt the model to the new emerging pattern. In this paper, we provide a review of outlier detection, anomaly pattern detection and concept drift detection approaches for streaming data.

The aim of this study is to investigate the cognitive workload characteristics which can be applied to the human factors that are applied to the switching of operation control in autonomous vehicles. For this purpose, we analyze test driver's EEG and driving data measured while driving on real roads to find out the difference of the cognitive workload state according to driving behaviors in the Urban Road. We performed a paired sample t-test using the preprocessed data to investigate the difference between normal workload ratio and overload workload ratio according to driving behavior. We also performed k-means clustering to see if drivers could be divided into groups using the overload status ratio according to the driving behavior of the driver. For this, we divided the collected data into simple and complex driving types, to see if there is any difference in the cognitive workload when the driver drives straight ahead or does in combination with other driving behaviors. We found that the overload occurrence rate is significantly different according to the driving behavior. We found that female drivers are more likely to be overloaded than male drivers and middle-aged drivers are likely to have more overloaded than young drivers when they did in complex driving. These cognitive workload characteristics can be reflected in the function of switching the operation control right in the autonomous driving system.

Inline detection of embedded voids within Middle-Of-Line (MOL) cobalt metal lines is a major industry gap at 7nm technology node and below, for both developing the new metallization solutions, as well as for monitoring during ramp and production. We present a new non-destructive electron beam cobalt void detection method, leveraging an improved scanning electron microscope (SEM) imaging technique, which enable an accurate detection of voids embedded inside MOL metal trenches. After explaining the potential process mechanisms causing void formation, we introduce the e-beam technique, and demonstrate by simulation and experiments the correlation between the electron signal and the volume and depth of the voids. We conclude this paper by discussing how a defect inspection strategy using a massive metrology approach can lead to a faster and more efficient development of the Cobalt metallization.