6 research outputs found

    Design Techniques for High-Speed ADCs in Nanoscale CMOS Technologies

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    Sub-Picosecond Jitter Clock Generation for Time Interleaved Analog to Digital Converter

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    Nowadays, Multi-GHz analog-to-digital converters (ADCs) are becoming more and more popular in radar systems, software-defined radio (SDR) and wideband communications, because they can realize much higher operation speed through using many interleaved sub-ADCs to relax ADC sampling rates. Although the time interleaved ADC has some issues such as gain mismatch, offset mismatch and timing skew between each ADC channel, these deterministic errors can be solved by previous works such as digital calibration technique. However, time-interleaved ADCs require a precise sample clock to achieve an acceptable effective-numberof-bits (ENOB) which can be degraded by jitter in the sample clock. The clock generation circuits presented in this work achieves sub-picosecond jitter performance in 180nm CMOS which is suitable for time-interleaved ADC. Two different test chips were fabricated in 180nm CMOS to investigate the low jitter design technique. The low jitter delay line in two chips were designed in two different ways, but both of them utilized the low jitter design technique. In first test chip, the measured RMS jitter is 0.1061ps for each delay stage. The second chip uses the proposed low jitter Delay-Locked Loop can work from 80MHz to 120MHz, which means it can provide the time interleaved ADC with 2.4GHz to 3.6GHz low jitter sample clock, the measured delay stage jitter performance in second test chip is 0.1085ps

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    Department of Electrical EngineeringA Sensor system is advanced along sensor technologies are developed. The performance improvement of sensor system can be expected by using the internet of things (IoT) communication technology and artificial neural network (ANN) for data processing and computation. Sensors or systems exchanged the data through this wireless connectivity, and various systems and applications are possible to implement by utilizing the advanced technologies. And the collected data is computed using by the ANN and the efficiency of system can be also improved. Gas monitoring system is widely need from the daily life to hazardous workplace. Harmful gas can cause a respiratory disease and some gas include cancer-causing component. Even though it may cause dangerous situation due to explosion. There are various kinds of hazardous gas and its characteristics that effect on human body are different each gas. The optimal design of gas monitoring system is necessary due to each gas has different criteria such as the permissible concentration and exposure time. Therefore, in this thesis, conventional sensor system configuration, operation, and limitation are described and gas monitoring system with wireless connectivity and neural network is proposed to improve the overall efficiency. As I already mentioned above, dangerous concentration and permissible exposure time are different depending on gas types. During the gas monitoring, gas concentration is lower than a permissible level in most of case. Thus, the gas monitoring is enough with low resolution for saving the power consumption in this situation. When detecting the gas, the high-resolution is required for the accurate concentration detecting. If the gas type is varied in the above situation, the amount of calculation increases exponentially. Therefore, in the conventional systems, target specifications are decided by the highest requirement in the whole situation, and it occurs increasing the cost and complexity of readout integrated circuit (ROIC) and system. In order to optimize the specification, the ANN and adaptive ROIC are utilized to compute the complex situation and huge data processing. Thus, gas monitoring system with learning-based algorithm is proposed to improve its efficiency. In order to optimize the operation depending on situation, dual-mode ROIC that monitoring mode and precision mode is implemented. If the present gas concentration is decided to safe, monitoring mode is operated with minimal detecting accuracy for saving the power consumption. The precision mode is switched when the high-resolution or hazardous situation are detected. The additional calibration circuits are necessary for the high-resolution implementation, and it has more power consumption and design complexity. A high-resolution Analog-to-digital converter (ADC) is kind of challenges to design with efficiency way. Therefore, in order to reduce the effective resolution of ADC and power consumption, zooming correlated double sampling (CDS) circuit and prediction successive approximation register (SAR) ADC are proposed for performance optimization into precision mode. A Microelectromechanical systems (MEMS) based gas sensor has high-integration and high sensitivity, but the calibration is needed to improve its low selectivity. Conventionally, principle component analysis (PCA) is used to classify the gas types, but this method has lower accuracy in some case and hard to verify in real-time. Alternatively, ANN is powerful algorithm to accurate sensing through collecting the data and training procedure and it can be verified the gas type and concentration in real-time. ROIC was fabricated in complementary metal-oxide-semiconductor (CMOS) 180-nm process and then the efficiency of the system with adaptive ROIC and ANN algorithm was experimentally verified into gas monitoring system prototype. Also, Bluetooth supports wireless connectivity to PC and mobile and pattern recognition and prediction code for SAR ADC is performed in MATLAB. Real-time gas information is monitored by Android-based application in smartphone. The dual-mode operation, optimization of performance and prediction code are adjusted with microcontroller unit (MCU). Monitoring mode is improved by x2.6 of figure-of-merits (FoM) that compared with previous resistive interface.clos
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