493 research outputs found

    Design of agile signal conditioning circuits for microelectromechanical sensors

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    Microelectromechanical systems (MEMS) are used in many applications to detect physical parameters and convert them to an electrical signal. The output of MEMS-based transducers is usually not suitable to be directly processed in the digital or the analog domain, and they could be as small as femto farads in capacitive sensing and micro volts in resistive sensing. Consequently, high sensitivity signal conditioning circuits are essential. In this thesis, it is shown that both the noise and input capacitance are important parameters to ensure optimal capacitive sensing. The dominant noise source in MEMS conditioning circuits is flicker noise, and one of the best methods to mitigate flicker noise is the chopping technique. Three different chopping techniques are considered: single chopper amplifier (SCA), dual chopper amplifier (DCA), and two-stage single chopper amplifier (TCA). Also, their sensitivity and power consumption based on the total gain and sensing capacitance are extracted. It is shown that the distribution of gain between the two stages in the DCA and TCA has a significant effect on the sensitivity, and, based on this distribution, the sensitivity and power consumption change significantly. For small sensor capacitances, the highest sensitivity could be achieved by a DCA because of its ability to decrease the noise floor and the input sensor capacitance simultaneously. A novel DCA is proposed to reach higher sensitivity and reduced power consumption. In this DCA, two supply voltages are utilized, and the second stage is composed of two parallel paths that improve the SNR and provide two gain settings. This circuit is fabricated in the GlobalFoundries 0.13 μm CMOS technology. The measurement results show a power consumption of 2.66 μW for the supply voltage of 0.7 V and of 3.26 μW for the supply voltage of 1.2 V. The single path DCA has a gain of 34 dB with bandwidth of 4 kHz and input noise floor of 25 nV/√Hz. The dual path DCA has a gain of 38 dB with bandwidth of 3 kHz and input noise floor of 40 nV/√Hz. To be able to detect the signal near DC frequencies, another circuit is proposed which has a configurable bandwidth and a sub-μHz noise corner frequency. This circuit is composed of three stages, and three chopping frequencies are used to mitigate the flicker noise of the three stages. The simulated circuit is designed in the GlobalFoundries 0.13 μm CMOS technology with supply voltages of 0.4 V and 1.2 V. The total power consumption is of 6.7 μW. A gain of 68 dB and bandwidths of 1, 10, 100 and 1000 Hz are achieved. The input referred noise floor is of 20.5 nV/√Hz and the design attains a good power efficiency factor of 4.0. In the capacitive mode, the noise floor is of 3.6 zF for a 100 fF capacitance sensor

    Analog/RF Circuit Design Techniques for Nanometerscale IC Technologies

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    CMOS evolution introduces several problems in analog design. Gate-leakage mismatch exceeds conventional matching tolerances requiring active cancellation techniques or alternative architectures. One strategy to deal with the use of lower supply voltages is to operate critical parts at higher supply voltages, by exploiting combinations of thin- and thick-oxide transistors. Alternatively, low voltage circuit techniques are successfully developed. In order to benefit from nanometer scale CMOS technology, more functionality is shifted to the digital domain, including parts of the RF circuits. At the same time, analog control for digital and digital control for analog emerges to deal with current and upcoming imperfections

    Interface Circuits for Microsensor Integrated Systems

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    ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [Recent advances in sensing technologies, especially those for Microsensor Integrated Systems, have led to several new commercial applications. Among these, low voltage and low power circuit architectures have gained growing attention, being suitable for portable long battery life devices. The aim is to improve the performances of actual interface circuits and systems, both in terms of voltage mode and current mode, in order to overcome the potential problems due to technology scaling and different technology integrations. Related problems, especially those concerning parasitics, lead to a severe interface design attention, especially concerning the analog front-end and novel and smart architecture must be explored and tested, both at simulation and prototype level. Moreover, the growing demand for autonomous systems gets even harder the interface design due to the need of energy-aware cost-effective circuit interfaces integrating, where possible, energy harvesting solutions. The objective of this Special Issue is to explore the potential solutions to overcome actual limitations in sensor interface circuits and systems, especially those for low voltage and low power Microsensor Integrated Systems. The present Special Issue aims to present and highlight the advances and the latest novel and emergent results on this topic, showing best practices, implementations and applications. The Guest Editors invite to submit original research contributions dealing with sensor interfacing related to this specific topic. Additionally, application oriented and review papers are encouraged.

    Integrated circuits for wearable systems based on flexible electronics

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    Integrated circuits for wearable systems based on flexible electronics

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    Accurate magnetic sensor system integrated design

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    Inductive measurement of magnetic fields is a diagnostic technique widely used in several scientific fields, such as magnetically confined fusion, plasma thrusters and particle accelerators, where real time control and detailed characterization of physics phenomena are required. The accuracy of the measured data strongly influences the machine controllability and the scientific results. In the framework of the assembly modifications of the RFX-mod experiment, a complete renew and improvement of the magnetic diagnostic system, from the probes moved inside the vacuum vessel to the integrator modules, has been carried out. In this paper, the whole system making up the magnetic diagnostics is described, following the acquisition chain from the probe to the streamed data and illustrating the requirements and conflicting limitations which affect the different components, in order to provide a comprehensive overview useful for an integrated design of any new systems. The characterization of a prototypical implementation of the whole acquisition chain is presented, focusing on the flexible ADC architecture adopted for providing a purely numerical signal integration, highlighting the advantages that this technology offers in terms of flexibility, compactness and cost effectiveness, along with the limitations found in existing implementation in terms of ADC noise characteristics and their possible solutions

    Low Power Circuits for Smart Flexible ECG Sensors

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    Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W

    Low Power CMOS Interface Circuitry for Sensors and Actuators

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