Dissertations / Theses on the topic 'Chip-based sensor'

This thesis focuses on inductive base sensor design at MHz range frequency. The background theory, design, experiments and results for a new magnetic particles sensor is presented. A new magnetic sensor based on a planar coil was investigated for DNA pathogen detection. Change in inductance of the planar coil due to the presence of magnetic particles with varying mass was measured. The experimental set-up consisted of different sized planar coil with associated electronics for inductance measurements. The best sensor performance was accomplished using two different inductors while oscillating at frequencies 2.4MHz using 9.5μH inductor and 7.2MHz with 85μH inductor. The sensor has very large signal to noise ratio (580×103), while the average amount of frequency drift was 0.58. This sensor was tested with various types of magnetic particles. In addition, iron-oxide nanoparticles were synthesized through water in oil microemulsion method and with an average size of 25nm. The best sensitivity achieved for detection of 50μg iron-oxide particles was with the bead size of 10nm. 81Hz frequency shift was attained in regard to that amount of particles. This research shows that increasing the resonance frequency to 7.2MHz can cause the larger output signal difference (frequency shift) in the presence of magnetic particles; however, the sensor stability is the most important factor for determining the detection resolution and sensitivity. The sensitivity is better if the sensor can detect smaller amount of magnetic sample. The results of this research demonstrate that while the sample consists of smaller size particles, the sensor can detect the lower amount of sample. This is due to the heating effect of nanoparticles. On the other hand the sample distance from the sensor has a major impact on the sensitivity too; the shorter the distance, the higher the sensitivity. This technique can potentially be extended to detect several different types of bacterial pathogens and can be modified for multiplex quantitative detection. This sensing technique will be incorporated into a handheld, disposable microfluidic chip for point-of-care diagnostics for sexually transmitted diseases.

Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode.<br>Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.

In this thesis, gas sensors based on on-chip hydrothermally grown 1-D zinc oxide (ZnO) nanostructures are presented, to improve the sensitivity, selectivity, and stability of the gas sensors. Metal-oxide-semiconductor (MOS) gas sensors are well-established tools for the monitoring of air quality indoors and outdoors. In recent years, the use of 1-D metal oxide nanostructures for sensing toxic gases, such as nitrogen dioxide, ammonia, and hydrogen, has gained significant attention. However, low-dimensional nanorod (NR) gas sensors can be enhanced further. Most works synthesize the NRs first and then transfer them onto electrodes to produce gas sensors, thereby resulting in large batch-to-batch difference. Therefore, in this thesis six studies on 1-D ZnO NR gas sensors were carried out. First, ultrathin secondary ZnO nanowires (NWs) were successfully grown on a silicon substrate. Second, an on-chip hydrothermally grown ZnO NR gas sensor was developed on a glass substrate. Its performance with regard to sensing nitrogen dioxide and three reductive gases, namely, ethanol, hydrogen, and ammonia, was tested. Third, three 1-D ZnO nanostructures, namely, ZnO NRs, dense ZnO NWs, and sparse ZnO NWs, were synthesized and tested toward nitrogen dioxide. Fourth, hydrothermally grown ZnO NRs, chemical vapor deposited ZnO NWs, and thermal deposited ZnO nanoparticles (NPs) were tested toward ethanol. Fifth, the effect of annealing on the sensitivity and stability of ZnO NR gas sensors was examined. Sixth, ZnO NRs were decorated with palladium oxide NPs and tested toward hydrogen at high temperature. The following conclusions can be drawn from the work in this thesis: 1) ZnO NWs can be obtained by using a precursor at low concentration, temperature of 90 °C, and long reaction time. 2) ZnO NR gas sensors have better selectivity to nitrogen dioxide compared with ethanol, ammonia, and hydrogen. 3) Sparse ZnO NWs are highly sensitive to nitrogen dioxide compared with dense ZnO NWs and ZnO NRs. 4) ZnO NPs have the highest sensitivity to ethanol compared with dense ZnO NWs and ZnO NRs. The sensitivity of the NPs is due to their small grain sizes and large surface areas. 5) ZnO NRs annealed at 600 °C have lower sensitivity toward nitrogen dioxide but higher long-term stability compared with those annealed at 400 °C. 6) When decorated with palladium oxide, both materials form alloy at a temperature higher than 350 °C and decrease the amount of ZnO, which is the sensing material toward hydrogen. Thus, controlling the amount of palladium oxide on ZnO NRs is necessary.