194 research outputs found

    Wearable, low-power CMOS ISFETs and compensation circuits for on-body sweat analysis

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    Complementary metal-oxide-semiconductor (CMOS) technology has been a key driver behind the trend of reduced power consumption and increased integration of electronics in consumer devices and sensors. In the late 1990s, the integration of ion-sensitive field-effect transistors (ISFETs) into unmodified CMOS helped to create advancements in lab-on-chip technology through highly parallelised and low-cost designs. Using CMOS techniques to reduce power and size in chemical sensing applications has already aided the realisation of portable, battery-powered analysis platforms, however the possibility of integrating these sensors into wearable devices has until recently remained unexplored. This thesis investigates the use of CMOS ISFETs as wearable electrochemical sensors, specifically for on-body sweat analysis. The investigation begins by evaluating the ISFET sensor for wearable applications, identifying the key advantages and challenges that arise in this pursuit. A key requirement for wearable devices is a low power consumption, to enable a suitable operational life and small form factor. From this perspective, ISFETs are investigated for low power operation, to determine the limitations when trying to push down the consumption of individual sensors. Batteryless ISFET operation is explored through the design and implementation of a 0.35 \si{\micro\metre} CMOS ISFET sensing array, operating in weak-inversion and consuming 6 \si{\micro\watt}. Using this application-specific integrated circuit (ASIC), the first ISFET array powered by body heat is demonstrated and the feasibility of using near-field communication (NFC) for wireless powering and data transfer is shown. The thesis also presents circuits and systems for combatting three key non-ideal effects experienced by CMOS ISFETs, namely temperature variation, threshold voltage offset and drift. An improvement in temperature sensitivity by a factor of three compared to an uncompensated design is shown through measured results, while adding less than 70 \si{\nano\watt} to the design. A method of automatically biasing the sensors is presented and an approach to using spatial separation of sensors in arrays in applications with flowing fluids is proposed for distinguishing between signal and sensor drift. A wearable device using the ISFET-based system is designed and tested with both artificial and natural sweat, identifying the remaining challenges that exist with both the sensors themselves and accompanying components such as microfluidics and reference electrode. A new ASIC is designed based on the discoveries of this work and aimed at detecting multiple analytes on a single chip. %Removed In the latter half of the thesis, Finally, the future directions of wearable electrochemical sensors is discussed with a look towards embedded machine learning to aid the interpretation of complex fluid with time-domain sensor arrays. The contributions of this thesis aim to form a foundation for the use of ISFETs in wearable devices to enable non-invasive physiological monitoring.Open Acces

    Digital CMOS ISFET architectures and algorithmic methods for point-of-care diagnostics

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    Over the past decade, the surge of infectious diseases outbreaks across the globe is redefining how healthcare is provided and delivered to patients, with a clear trend towards distributed diagnosis at the Point-of-Care (PoC). In this context, Ion-Sensitive Field Effect Transistors (ISFETs) fabricated on standard CMOS technology have emerged as a promising solution to achieve a precise, deliverable and inexpensive platform that could be deployed worldwide to provide a rapid diagnosis of infectious diseases. This thesis presents advancements for the future of ISFET-based PoC diagnostic platforms, proposing and implementing a set of hardware and software methodologies to overcome its main challenges and enhance its sensing capabilities. The first part of this thesis focuses on novel hardware architectures that enable direct integration with computational capabilities while providing pixel programmability and adaptability required to overcome pressing challenges on ISFET-based PoC platforms. This section explores oscillator-based ISFET architectures, a set of sensing front-ends that encodes the chemical information on the duty cycle of a PWM signal. Two initial architectures are proposed and fabricated in AMS 0.35um, confirming multiple degrees of programmability and potential for multi-sensing. One of these architectures is optimised to create a dual-sensing pixel capable of sensing both temperature and chemical information on the same spatial point while modulating this information simultaneously on a single waveform. This dual-sensing capability, verified in silico using TSMC 0.18um process, is vital for DNA-based diagnosis where protocols such as LAMP or PCR require precise thermal control. The COVID-19 pandemic highlighted the need for a deliverable diagnosis that perform nucleic acid amplification tests at the PoC, requiring minimal footprint by integrating sensing and computational capabilities. In response to this challenge, a paradigm shift is proposed, advocating for integrating all elements of the portable diagnostic platform under a single piece of silicon, realising a ``Diagnosis-on-a-Chip". This approach is enabled by a novel Digital ISFET Pixel that integrates both ADC and memory with sensing elements on each pixel, enhancing its parallelism. Furthermore, this architecture removes the need for external instrumentation or memories and facilitates its integration with computational capabilities on-chip, such as the proposed ARM Cortex M3 system. These computational capabilities need to be complemented with software methods that enable sensing enhancement and new applications using ISFET arrays. The second part of this thesis is devoted to these methods. Leveraging the programmability capabilities available on oscillator-based architectures, various digital signal processing algorithms are implemented to overcome the most urgent ISFET non-idealities, such as trapped charge, drift and chemical noise. These methods enable fast trapped charge cancellation and enhanced dynamic range through real-time drift compensation, achieving over 36 hours of continuous monitoring without pixel saturation. Furthermore, the recent development of data-driven models and software methods open a wide range of opportunities for ISFET sensing and beyond. In the last section of this thesis, two examples of these opportunities are explored: the optimisation of image compression algorithms on chemical images generated by an ultra-high frame-rate ISFET array; and a proposed paradigm shift on surface Electromyography (sEMG) signals, moving from data-harvesting to information-focused sensing. These examples represent an initial step forward on a journey towards a new generation of miniaturised, precise and efficient sensors for PoC diagnostics.Open Acces

    Nonlinear characterisation of power ultrasonic devices used in bone surgery

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    Ultrasonic cutting has existed in surgery since the 1950s. However, it was not until the end of the 20th century that advances in ultrasonic tool design, transduction and control allowed commercially viable ultrasonic cutting devices to enter the market. Ultrasonic surgical devices, like those in other power ultrasonic applications such as drilling and welding, require devices to be driven at high power to ensure sufficient output motion is produced to fulfil the application it is designed to perform. With the advent of novel surgical techniques surgeons require tuned ultrasonic tools which can reduce invasiveness while giving access to increasingly difficult to reach surgical sites. To fulfil the requirements of novel surgical procedures new tuned tools need to be designed. Meanwhile, it is well documented that power ultrasonic devices, whilst driven at high power, are inherently nonlinear and, if no attempt is made to understand and subsequently control these behaviours, it is likely that these devices will suffer from poor performance or even failure. The behaviour of the commercial ultrasonic transducer used in bone surgery (Piezosurgery® Device) is dynamically characterised through finite element and experimental methods whilst operating in conjunction with a variety of tuned inserts. Finite element analysis was used to predict modal parameters as well as stress levels within the tuned devices whilst operating at elevated amplitudes of vibration, while experimental modal analysis validated predicted resonant frequencies and mode shapes between 0-80kHz. To investigate the behaviour of tuned devices at elevated vibrational amplitudes near resonance, responses were measured whilst the device was excited via the burst sine sweep method. In an attempt to provide an understanding of the effects that geometry, material selection and wavelength of tuned assemblies have on the behaviour of an ultrasonic device, tuned inserts consisting of a simple rod horn design were characterised alongside more complex cutting inserts which are used in maxillofacial and craniofacial surgery. From these results the aim will be to develop guidelines for design of tuned inserts. Meanwhile, Langevin transducers, commonly known as sandwich or stack transducers, in their most basic form generally consist of four parts; a front mass, a back mass, a piezoceramic stack and a stud or bolt holding the parts together under a compressive pre-load. It is traditionally proposed that the piezoceramic stack is positioned at or close to the vibrational nodal point of the longitudinal mode, however, this also corresponds with the position of highest dynamic stress. It is also well documented that piezoceramic materials possess a low linear stress threshold, therefore this research, in part, investigates whether locating the piezoceramic stack away from a position of intrinsic high stress will affect the behaviour of the device. Through experimental characterisation it has been observed that the tuned devices under investigation exhibited; resonant frequency shifts, jump amplitudes, hysteretic behaviour as well as autoparametric vibration. The source of these behaviours have been found to stem from device geometry, but also from heating within the piezoceramic elements as well as joints with different joining torques

    Reliability prediction of single crystal silicon MEMS using dynamic Raman spectroscopy

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    Phd ThesisThe work investigates an extension and improvement to reliability prediction in single crystal silicon MEMS by utilising dynamic Raman spectroscopy to allow fracture test data collected directly from devices thereby taking account of actual geometrical tolerances, dynamic load conditions and effects from the microfabrication process. Micro-cantilever beam MEMS devices microfabricated from (100) crystalline silicon wafers having [110] beam direction were used in this experiment. A piezo actuator was used to vibrate the devices. The fracture data was taken by increasing the supply voltage to bring each device to rupture whilst a continuous beam HeNe laser was directed from the [100] direction to particular positions on the sample allowing for capture of the photons. The resulting Raman profile, broadened by the vibration of the device, was fit using a Voigt profile and compared to the no load condition. A calibration step was used to convert the Raman signal to volumetric μstrain. The reliability prediction methodology used in this work was developed under the Weibull distribution function that is based on the concept of weakest link theory describing distribution of flaws in brittle material. As each device was fabricated from semiconductor grade single crystalline silicon, it can be considered to have no mechanical defects with the flaws on the device only existing as surface flaws induced from the microfabrication process during manufacturing. Differently processed surfaces each have their own Weibull parameters. The failure prediction of a particular MEMS device is calculated from these parameters with the simulated structure responses due to applied load being predicted from the finite element package ANSYS. The failure prediction method shows a good agreement with the experimental results with accuracy of 10%. However, visual observations were necessary as a number of ruptured specimens started to fail from the bottom side of the clamped end and propagated through [111] direction so from the upper side the failure looks like it started from a distance from the clamped end. The experimental work was carried out utilising [100] Raman scattering. This limits the ability of the system to only capture the strain condition in one direction; the other strain directions were approximated using silicon orthotropic material properties and assuming that the load is a uniaxial load. This limitation forces the failure prediction distribution function to treat silicon as an isotropic material with the strength ii characteristic scale parameter in the Weibull distribution being the same value for all directions. This limitation together with extending the work towards implementing an “off-axis” Raman characterisation able to characterise all the strain directions is discussed for future work

    MME2010 21st Micromechanics and Micro systems Europe Workshop : Abstracts

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