47 research outputs found

    High speed – energy efficient successive approximation analog to digital converter using tri-level switching

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    This thesis reports issues and design methods used to achieve high-speed and high-resolution Successive Approximation Register analog to digital converters (SAR ADCs). A major drawback of this technique relates to the mismatch in the binary ratios of capacitors which causes nonlinearity. Another issue is the use of large capacitors due to nonlinear effect of parasitic capacitance. Nonlinear effect of capacitor mismatch is investigated in this thesis. Based on the analysis, a new Tri-level switching algorithm is proposed to reduce the matching requirement for capacitors in SAR ADCs. The integral non-linearity (INL) and the differential non-linearity (DNL) of the proposed scheme are reduced by factor of two over conventional SAR ADC, which is the lowest compared to the previously reported schemes. In addition, the switching energy of the proposed scheme is reduced by 98.02% compared with the conventional SAR architecture. A new correction method to solve metastability error of comparator based on a novel design approach is proposed which reduces the required settling time about 1.1τ for each conversion cycle. Based on the above proposed methods two SAR ADCs: an 8-bit SAR ADC with 50MS/sec sampling rate, and a 10-bit SAR split ADC with 70 MS/sec sampling rate have been designed in 0.18μm Silterra complementary metal oxide semiconductor (CMOS) technology process which works at 1.2V supply voltage and input voltage of 2.4Vp-p. The 8-bit ADC digitizes 25MHz input signal with 48.16dB signal to noise and distortion ratio (SNDR) and 52.41dB spurious free dynamic range (SFDR) while consuming about 589μW. The figure of merit (FOM) of this ADC is 56.65 fJ/conv-step. The post layout of the 10-bit ADC with 1MHz input frequency produces SNDR, SFDR and effective number of bits (ENOB) of 57.1dB, 64.05dB and 9.17Bit, respectively, while its DNL and INL are -0.9/+2.8 least significant bit (LSB) and -2.5/+2.7 LSB, respectively. The total power consumption, including digital, analog and reference power, is 1.6mW. The FOM is 71.75fJ/conv. step

    Low-Power Slew-Rate Boosting Based 12-Bit Pipeline ADC Utilizing Forecasting Technique in the Sub-ADCS

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    The dissertation presents architecture and circuit solutions to improve the power efficiency of high-speed 12-bit pipelined ADCs in advanced CMOS technologies. First, the 4.5bit algorithmic pipelined front-end stage is proposed. It is shown that the algorithmic pipelined ADC requires a simpler sub-ADC and shows lower sensitivity to the Multiplying DAC (MDAC) errors and smaller area and power dissipation in comparison to the conventional multi-bit per stage pipelined ADC. Also, it is shown that the algorithmic pipelined architecture is more tolerant to capacitive mismatch for the same input-referred thermal noise than the conventional multi-bit per stage architecture. To take full advantage of these properties, a modified residue curve for the pipelined ADC is proposed. This concept introduces better linearity compared with the conventional residue curve of the pipelined ADC; this approach is particularly attractive for the digitization of signals with large peak to average ratio such as OFDM coded signals. Moreover, the minimum total required transconductance for the different architectures of the 12-bit pipelined ADC are computed. This helps the pipelined ADC designers to find the most power-efficient architecture between different topologies based on the same input-referred thermal noise. By employing this calculation, the most power efficient architecture for realizing the 12-bit pipelined ADC is selected. Then, a technique for slew-rate (SR) boosting in switched-capacitor circuits is proposed in the order to be utilized in the proposed 12-bit pipelined ADC. This technique makes use of a class-B auxiliary amplifier that generates a compensating current only when high slew-rate is demanded by large input signal. The proposed architecture employs simple circuitry to detect the need of injecting current at the output load by implementing a Pre-Amp followed by a class-B amplifier, embedded with a pre-defined hysteresis, in parallel with the main amplifier to boost its slew phase. The proposed solution requires small static power since it does not need high dc-current at the output stage of the main amplifier. The proposed technique is suitable for high-speed low-power multi-bit/stage pipelined ADC applications. Both transistor-level simulations and experimental results in TSMC 40nm technology reduces the slew-time for more than 45% and shorts the 1% settling time by 28% when used in a 4.5bit/stage pipelined ADC; power consumption increases by 20%. In addition, the technique of inactivating and disconnecting of the sub-ADC’s comparators by forecasting the sign of the sampled input voltage is proposed in the order to reduce the dynamic power consumption of the sub-ADCs in the proposed 12-bit pipelined ADC. This technique reduces the total dynamic power consumption more than 46%. The implemented 12-bit pipelined ADC achieves an SNDR/SFDR of 65.9/82.3 dB at low input frequencies and a 64.1/75.5 dB near Nyquist frequency while running at 500 MS/s. The pipelined ADC prototype occupies an active area of 0.9 mm^2 and consumes 18.16 mW from a 1.1 V supply, resulting in a figure of merit (FOM) of 22.4 and a 27.7 fJ/conversion-step at low-frequency and Nyquist frequency, respectively

    Low-Power Slew-Rate Boosting Based 12-Bit Pipeline ADC Utilizing Forecasting Technique in the Sub-ADCS

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    The dissertation presents architecture and circuit solutions to improve the power efficiency of high-speed 12-bit pipelined ADCs in advanced CMOS technologies. First, the 4.5bit algorithmic pipelined front-end stage is proposed. It is shown that the algorithmic pipelined ADC requires a simpler sub-ADC and shows lower sensitivity to the Multiplying DAC (MDAC) errors and smaller area and power dissipation in comparison to the conventional multi-bit per stage pipelined ADC. Also, it is shown that the algorithmic pipelined architecture is more tolerant to capacitive mismatch for the same input-referred thermal noise than the conventional multi-bit per stage architecture. To take full advantage of these properties, a modified residue curve for the pipelined ADC is proposed. This concept introduces better linearity compared with the conventional residue curve of the pipelined ADC; this approach is particularly attractive for the digitization of signals with large peak to average ratio such as OFDM coded signals. Moreover, the minimum total required transconductance for the different architectures of the 12-bit pipelined ADC are computed. This helps the pipelined ADC designers to find the most power-efficient architecture between different topologies based on the same input-referred thermal noise. By employing this calculation, the most power efficient architecture for realizing the 12-bit pipelined ADC is selected. Then, a technique for slew-rate (SR) boosting in switched-capacitor circuits is proposed in the order to be utilized in the proposed 12-bit pipelined ADC. This technique makes use of a class-B auxiliary amplifier that generates a compensating current only when high slew-rate is demanded by large input signal. The proposed architecture employs simple circuitry to detect the need of injecting current at the output load by implementing a Pre-Amp followed by a class-B amplifier, embedded with a pre-defined hysteresis, in parallel with the main amplifier to boost its slew phase. The proposed solution requires small static power since it does not need high dc-current at the output stage of the main amplifier. The proposed technique is suitable for high-speed low-power multi-bit/stage pipelined ADC applications. Both transistor-level simulations and experimental results in TSMC 40nm technology reduces the slew-time for more than 45% and shorts the 1% settling time by 28% when used in a 4.5bit/stage pipelined ADC; power consumption increases by 20%. In addition, the technique of inactivating and disconnecting of the sub-ADC’s comparators by forecasting the sign of the sampled input voltage is proposed in the order to reduce the dynamic power consumption of the sub-ADCs in the proposed 12-bit pipelined ADC. This technique reduces the total dynamic power consumption more than 46%. The implemented 12-bit pipelined ADC achieves an SNDR/SFDR of 65.9/82.3 dB at low input frequencies and a 64.1/75.5 dB near Nyquist frequency while running at 500 MS/s. The pipelined ADC prototype occupies an active area of 0.9 mm^2 and consumes 18.16 mW from a 1.1 V supply, resulting in a figure of merit (FOM) of 22.4 and a 27.7 fJ/conversion-step at low-frequency and Nyquist frequency, respectively

    LOW-VOLTAGE LOW-POWER ANALOG-TO-DIGITAL CONVERTERS

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    Ph.DDOCTOR OF PHILOSOPH

    Analog and Mixed Signal Design towards a Miniaturized Sleep Apnea Monitoring Device

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    Sleep apnea is a sleep-induced breathing disorder with symptoms of momentary and often repetitive cessations in breathing rhythm or sustained reductions in breathing amplitude. The phenomenon is known to occur with varying degrees of severity in literally millions of people around the world and cause a range of chronicle health issues. In spite of its high prevalence and serious consequences, nearly 80% of people with sleep apnea condition remain undiagnosed. The current standard diagnosis technique, termed polysomnography or PSG, requires the patient to schedule and undergo a complex full-night sleep study in a specially-equipped sleep lab. Due to both high cost and substantial inconvenience, millions of apnea patients are still undiagnosed and thus untreated. This research work aims at a simple, reliable, and miniaturized solution for in-home sleep apnea diagnosis purposes. The proposed solution bears high-level integration and minimal interference with sleeping patients, allowing them to monitor their apnea conditions at the comfort of their homes. Based on a MEMS sensor and an effective apnea detection algorithm, a low-cost single-channel apnea screening solution is proposed. A custom designed IC chip implements the apnea detection algorithm using time-domain signal processing techniques. The chip performs autonomous apnea detection and scoring based on the patient’s airflow signals detected by the MEMS sensor. Variable sensitivity is enabled to accommodate different breathing signal amplitudes. The IC chip was fabricated in standard 0.5-μm CMOS technology. A prototype device was designed and assembled including a MEMS sensor, the apnea detection IC chip, a PSoC platform, and wireless transceiver for data transmission. The prototype device demonstrates a valuable screening solution with great potential to reach the broader public with undiagnosed apnea conditions. In a battery-operated miniaturized medical device, an energy-efficient analog-to-digital converter is an integral part linking the analog world of biomedical signals and the digital domain with powerful signal processing capabilities. This dissertation includes the detailed design of a successive approximation register (SAR) ADC for ultra-low power applications. The ADC adopts an asynchronous 2b/step scheme that halves both conversion time and DAC/digital circuit’s switching activities to reduce static and dynamic energy consumption. A low-power sleep mode is engaged at the end of all conversion steps during each clock period. The technical contributions of this ADC design include an innovative 2b/step reference scheme based on a hybrid R-2R/C-3C DAC, an interpolation-assisted time-domain 2b comparison scheme, and a TDC with dual-edge-comparison mechanism. The prototype ADC was fabricated in 0.18μm CMOS process with an active area of 0.103 mm^(2), and achieves an ENoB of 9.2 bits and an FoM of 6.7 fJ/conversion-step at 100-kS/s

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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

    A high resolution data conversion and digital processing for high energy physics calorimeter detectors readout

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