• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.74-82
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• 2015

As the complexity of an electronic device and the reduction of its operating voltage is progressing, susceptibility test of the chip and module for internal or external noises is essential. Although the immunity compliance of the chip was served with IEC 62132-4 Direct Power Injection method as an industry standard, in fact, EM immunity of the chip is influenced by their Power Domain Network (PDN). This paper evaluates the EM noise tolerance of a PLL and compares their noise transfer characteristics to the PLL on various PCB boards. To make differences of the PDNs of PCBs, various PCBs with or without LDO and with several types of capacitors are tested. For evaluation of discrepancies between EM characteristics of an IC only and the IC on real boards, the analysis of the noise transfer characteristics according to the PDNs shows that it gives important information for the design having robust EM characteristics. DPI measurement results show that greatly improved immunity of the PLL in the low-frequency region according to using the LDO and a frequency change of the PLL according to the DPI could also check with TEM cell measurement spectrum.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• ETRI Journal
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• v.42 no.4
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• pp.491-504
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• 2020

We present AB9, a neural processor for inference acceleration. AB9 consists of a systolic tensor core (STC) neural network accelerator designed to accelerate artificial intelligence applications by exploiting the data reuse and parallelism characteristics inherent in neural networks while providing fast access to large on-chip memory. Complementing the hardware is an intuitive and user-friendly development environment that includes a simulator and an implementation flow that provides a high degree of programmability with a short development time. Along with a 40-TFLOP STC that includes 32k arithmetic units and over 36 MB of on-chip SRAM, our baseline implementation of AB9 consists of a 1-GHz quad-core setup with other various industry-standard peripheral intellectual properties. The acceleration performance and power efficiency were evaluated using YOLOv2, and the results show that AB9 has superior performance and power efficiency to that of a general-purpose graphics processing unit implementation. AB9 has been taped out in the TSMC 28-nm process with a chip size of 17 × 23 ㎟. Delivery is expected later this year.

• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.5
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• pp.455-461
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• 2006

In this study, we have described a method for the fabrication of a protein chip on silicon substrate using hydrophobic thin film and microfluidic channels, for the simultaneous detection of multiple targets in samples. The use of hydrophobic thin film provides for a physical, chemical, and biological barrier for protein patterning. The microfluidic channels create four protein patterned strips on the silicon surfaces with a high signal-to-noise ratio. The feasibility of the protein chips was determined in order to discriminate between each protein interaction in a mixture sample that included biotin, ovalbumin, hepatitis B antigen, and hepatitis C antigen. In the fabrication of the multiplexed assay system, the utilization of the hydrophobic thin film and the microfluidic networks constitutes a more convenient method for the development of biosensors or biochips. This technique may be applicable to the simultaneous evaluation of multiple protein-protein interactions.

• Smart Structures and Systems
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• v.8 no.1
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• pp.39-52
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• 2011

We are developing a biomimetic robot based on the Sea Lamprey. The robot consists of a cylindrical electronics bay propelled by an undulatory body axis. Shape memory alloy (SMA) actuators generate propagating flexion waves in five undulatory segments of a polyurethane strip. The behavior of the robot is controlled by an electronic nervous system (ENS) composed of networks of discrete-time map-based neurons and synapses that execute on a digital signal processing chip. Motor neuron action potentials gate power transistors that apply current to the SMA actuators. The ENS consists of a set of segmental central pattern generators (CPGs), modulated by layered command and coordinating neuron networks, that integrate input from exteroceptive sensors including a compass, accelerometers, inclinometers and a short baseline sonar array (SBA). The CPGs instantiate the 3-element hemi-segmental network model established from physiological studies. Anterior and posterior propagating pathways between CPGs mediate intersegmental coordination to generate flexion waves for forward and backward swimming. The command network mediates layered exteroceptive reflexes for homing, primary orientation, and impediment compensation. The SBA allows homing on a sonar beacon by indicating deviations in azimuth and inclination. Inclinometers actuate a bending segment between the hull and undulator to allow climb and dive. Accelerometers can distinguish collisions from impediment to allow compensatory reflexes. Modulatory commands mediate speed control and turning. A SBA communications interface is being developed to allow supervised reactive autonomy.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.293-300
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• 2020

Since the communication networks like the Internet collapses at disaster and calamity sites, a maintenance system that can be operated offline is required for the maintenance of various facilities. In this paper, we propose a system that memory tags attached on the facilities may transmit the emergency manual to a smart-phone, and the smart phone displays it off-line. The main issue is to design low energy mode memory tags. This study presents two kinds of methods and analyzes each's energy consumption mode. The first one is to develop memory tags by using one chip, and the next one is to design memory tags by forming multi-modules. Both ways show proper application fields under the low energy mode. This research selects the off-line maintenance system by using one chip design, and proposes the direction of contents for enhancing the effectiveness of the system. And we expect that this memory tags will be valuable for disaster scenes as well as battle fields.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.284-288
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• 2019

A calibration-free analog neuron circuit is proposed as a viable alternative to the power hungry digital neuron in implementing a deep neural network. The conventional analog neuron requires calibrations because a voltage-mode link is used between the soma and the synapse, which results in significant uncertainty in terms of current mapping. In this work, a current-mode link is used to establish a robust link between the soma and the synapse against the variations in the process and interconnection impedances. The increased hardware owing to the adoption of the current-mode link is estimated to be manageable because the number of neurons in each layer of the neural network is typically bounded. To demonstrate the utility of the proposed analog neuron, a simple neural network with $4{\times}7{\times}3$ architecture has been designed for classifying iris flowers. The chip is now under fabrication in 0.35 mm CMOS technology. Thus, the proposed true current-mode analog neuron can be a practical option in realizing power-efficient neural networks for edge computing.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.139-149
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• 2012

A 3D stacked IC is made by multiple dies (possibly) with heterogeneous process technologies. Therefore, die-to-die variation in 2D chips renders on-package variation (OPV) in a 3D chip. In spite of the different variation effect in 3D chips, generally, 3D die stacking can produce high yield due to the smaller individual die area and the averaging effect of variation on data path. However, 3D clock network can experience unintended huge clock skew due to the different clock propagation routes on multiple stacked dies. In this paper, we analyze the on-package variation effect on 3D clock networks and show the necessity of a post silicon management method such as body biasing technique for the OPV induced 3D clock skew control in 3D stacked IC designs. Then, we present a parametric yield improvement method to mitigate the OPV induced 3D clock skew.

• Smart Structures and Systems
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• v.12 no.1
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• pp.23-39
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• 2013

In this article, a smart multifunctional optical system-on-a-chip (SOC) sensor platform is presented and its application for fiber Bragg grating (FBG) sensor interrogation in optical sensor networks is investigated. The smart SOC sensor platform consists of a superluminescent diode as a broadband source, a tunable microelectromechanical system (MEMS) based Fabry-P$\acute{e}$rot filter, photodetectors, and an integrated microcontroller for data acquisition, processing, and communication. Integrated with a wireless sensor network (WSN) module in a compact package, a smart optical sensor node is developed. The smart multifunctional sensor platform has the capability of interrogating different types of optical fiber sensors, including Fabry-P$\acute{e}$rot sensors and Bragg grating sensors. As a case study, the smart optical sensor platform is demonstrated to interrogate multiplexed FBG strain sensors. A time domain signal processing method is used to obtain the Bragg wavelength shift of two FBG strain sensors through sweeping the MEMS tunable Fabry-P$\acute{e}$rot filter. A tuning range of 46 nm and a tuning speed of 10 Hz are achieved. The smart optical sensor platform will open doors to many applications that require high performance optical WSNs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1687-1693
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• 2007

Advances in technologies of networking, chip integration, and embedded system have enabled sensor networks applicable to a wide range of areas. Sharing some common characteristics, sensor networks are thus diversified in features depending on their applications. An intelligent and digital home can be one area to establish a particular feature of sensor network. This paper proposes a composite sensor and control network, and discusses its applying to the next generation intelligent and digital home. Development results of the network and a test-bed as a virtual test environment are also presented. The proposed network can not only be efficiently applying to achieve new home intelligences but also provide a sound solution to maintenance and operations of home network or devices.