Passivating contacts in the front-rear contacted c-Si solar cells enhances the device performance by quenching the contact recombination and enabling sufficient carrier transport. Tunneling oxide passi- vating contact (TOPCon) which consists of an interfacial oxide and a poly-SiO𝑥 layer is one of the most promising contact techniques in passivating both surfaces of the silicon wafer. However, the full area poly-SiO𝑥 layer is absorptive when it is applied at the front side which has become an efficiency limiting factor. In this project, an advanced local poly-SiO𝑥 passivating contact architecture is proposed and presented. The local poly-SiO𝑥 passivating contacts, so-called “poly-SiO𝑥 finger”, is deposited through a hard mask at predefined locations which will be only placed underneath the metal contacts. The formation of poly-SiO𝑥 finger and its application in the solar cells are discussed. First, a silicon wafer hard mask is fabricated by a lithography process followed by a KOH wet chemical etch-back process. Specified openings down to 38.7 𝜇m are established which isolate sections of the substrate for the poly-SiO𝑥 finger formation. P+ doped a-SiO𝑥 :H is deposited using PECVD with SiO𝑥 p+ diffused emitter commercial Cz wafers through the hard mask. By investigating the influence of deposition parameters during the PECVD, specifically the power and pressure, decent uniformity of the finger deposition is achieved. An optimized deposition condition of the doped a-SiO𝑥 :H is established for the poly-SiO𝑥 finger formation: an RF power source at 15 Watt, a chamber pressure of 1.5 mbar, and a substrate temperature of 180 °C. A narrow poly-SiO𝑥 finger with a width of 40.0 𝜇m has been demonstrated. Second, the passivation properties of the symmetrical samples are examined by varying the poly-SiO𝑥 and Al2O3 thickness for the hole carrier selective contacts. A full area p+ TOPCon and Al2O3 symmetric samples were coated on the textured diffused p+ surface with an n-type c-Si substrate to investigate the process parameters on the passivation properties. The optimal 𝑖𝑉𝑜𝑐 of p+ TOPCon samples is 662 𝑚𝑉 and a corresponding 𝐽0 of 156 𝑓 𝐴 𝑐𝑚2 which is obtained with a poly-SiO𝑥 thickness of 50 nm when annealed at 850 °C. The optimal thickness for the Al2O3 passivation layer is achieved by depositing a 20 nm thick layer followed by FGA, with an 𝑖𝑉𝑜𝑐 of 692 𝑚𝑉 and a 𝐽0 of 22.4 𝑓 𝐴/𝑐𝑚2. Finally, a process flowchart has been created to fabricate c-Si solar cells with the local p+ TOPCon on the front p+ diffused emitter and a full area n+ TOPCon on the rear side. A localized polished area is made by a poly-etch process for the alignment between the substrate and the pattern in the lithography mask. Using the ascertained parameters during the optimization, NAOS-SiO𝑥 combining with 50 nm-thick doped p+ a-SiO𝑥 :H fingers are applied on the p+ diffused surface and 100 nm-thick n+a-SiO𝑥 :H on the rear side. A annealing step is performed for the crystallization. A 20 nm-thick Al2O3 followed by a 75 nm SiN𝑥 layer are deposited on the front side and 100 nm SiN𝑥 layer is applied on the rear side. Lithography, wet chemical etch-back together with Al/Ag(front/rear) evaporation are utilized for solar cells metallization. The fabricated c-Si solar cells with poly-SiO𝑥 fingers has a champion efficiency with an efficiency of 7.91 %, with a 𝑖𝑉𝑜𝑐 of 562 𝑚𝑉, 𝑖𝑛𝑡 𝐽𝑠𝑐 of 34.7 𝑚𝐴 𝑐𝑚2 and fill factor of 40.5 %. The limited performance originates from the poor passivation in the poly-SiO𝑥 local contact area and severe 𝑖𝑉𝑜𝑐 drop after the metallization process. For further improvement, good performing c-Si solar cells by minimizing the recombination in the poly-SiO𝑥 local fingers contacts area and optimizing metallization steps are expected to be fabricated .

In this research work, efforts have been made to successfully integrate graphene in a standard CMOS fabrication process without the need for wafer-to-wafer transferring. For this purpose, a graphene-based Pirani sensor is integrated in the BICMOS process. Two batches of graphene-based Pirani sensors are characterized, resulting in world’s first operating graphene-based Pirani pressures sensors. The maximum measured resistance change is 2.8 % which is comparable to current state-of-the-art implementations using other materials. The graphene-based implementation allowed for miniaturization of the devices with low power consumption design possibilities. The BICMOS technology used at EKL at the Delft University of Technology is characterized and effects of graphene growth are investigated. This led to an electrical read-out design in CMOS technology of which the digital circuits performed as designed. Operational digital logic gates were measured alongside grafeen, but with a reduced yield. The analog circuits proved to be challenging in the BICMOS technology and did therefore not operate as intended. From this it is concluded that it is indeed possible to integrate grafeen with a CMOS process. Future research work that focuses on the details of the fabrication process flow should result in higher fabrication yields and better reproducibility. Additional measurements on the grafeen structures will help to characterize the graphene-based Pirani better and allow for attempts to fit an analytical model to the measurement data.

The on-field performance and lifespan of PV modules are affected by the interaction of numerous parameters, among which temperature, strain, and humidity. Among these parameters, only temperature is occasionally measured in PV installations, and such monitoring is often external to the module. This limits its use and does not provide accurate information on the conditions at cell level. The integration of different sensors on PV cells can improve the performance of PV devices and enable the early detection of faults and unwanted operating conditions, examples of which are PV-module delamination and the occurrence of hotspots, respectively. This thesis reports on the design, fabrication and testing of four types of sensors, as an initial step within the PVMD group towards sensor integration on PV cells. The devices considered for this work are the following: an aluminum-based resistive temperature sensor, a Boron-doped poly-silicon piezoresistive strain sensor, and two polyimide-functionalized humidity sensors – one capacitive, the other thermoresistive. Furthermore, these devices are combined into multi-sensing platforms with the aim to simultaneously sense sets of parameters (e.g., strain & temperature) to eventually compensate for their reciprocal interference. The results of the electrothermal characterization of the non-laminated temperature and strain sensors reveal an overall linear response of the devices, with an average TCR of around +3.75*10-3 °C-1 and -3.7*10-3 °C-1, respectively; this translates into a change in resistance of approximately 22.2-22.5% over the temperature range under study (30 °C-90 °C). Meanwhile, the characterization with a climate chamber of a fabricated capacitive humidity sensor shows a significant increase in capacitance: when relative humidity changes between 20% and 80% at 30 °C, the measured capacitance rises from 6.86 nF to 7.20 nF – equivalent to a 5% increase. Furthermore, capacitance measurements performed at constant humidity ratio and varying temperatures indicate a substantial cross-sensitivity of the humidity sensor under test, resulting in an approximately linear capacitance increase between 20 °C and 40 °C with an average regression slope of 10 pF/°C. Since the tested capacitive humidity sensor and piezoresistive strain sensor show a substantial response to temperature, temperature compensation is needed to ensure reliable and accurate measurements of both humidity and strain. To this regard, a simplified compensation based on the simultaneous interrogation of the resistive temperature sensor and capacitive humidity sensor is successfully performed, which highlights the importance of integrated multi-sensing platforms.

At this time, sensor developers rely on their own circuit board designs to test new sensors. This means a lot of time and money is spent on designing the custom interface for every newly designed sensor. This report describes a way to have a configurable test bench for sensors. The system consists of a data acquisition unit and multiple circuit boards to be able to have a certain level of reconfigurability for the used pins. This system is also able to generate simple signals. It is therefore a replacement for certain instruments as well. The test bench has 9 configurable pins and a number of external connections. It is simple to use and can do its job automatically. This report focuses on the hardware part, where all the switching and amplification is done. We recommend to use the system on a trial basis, because it is not completely functional yet.

Neuroscientists use neural electrodes to explore the working mechanisms of the nervous system. Therefore, ideal electrodes should have a small size and the ability to record and stimulate at a single cell resolution with low noise. Materials used for fabrication should be flexible and stable for a long period in the biological media. However, conventional recording and stimulation techniques do not have sufficient spatiotemporal resolution for neuroscience research. Combining electrical and optical modalities into one device helps overcome the resolution limits and record more detailed information. For this application, transparent conductive materials are needed. Graphene is a potential solution due to its advantageous combination of properties, such as high conductivity, transparency, and flexibility. However, important characteristics of recording and stimulation electrodes, such as the impedance and charge injection capacity of graphene electrodes, do not reach the levels of conventional materials. The electrical characteristics of graphene could be improved further with surface modification, chemical doping, or stacking. Each method has been shown to improve the conductivity of graphene, although some affect the transparency of the layer. In this work, three methods were used to improve the electrical characteristics of multilayer graphene neural electrode without losing transparency or flexibility. These methods include growing a thicker layer of graphene, adding metal nanoparticles to the surface of the electrode, and nitric acid doping of graphene. For that purpose, graphene electrodes were fabricated on a silicon wafer. The electrical characteristics of these electrodes were assessed with electrochemical impedance spectroscopy, cyclic voltammetry and 4-point probe measurements. Furthermore, the optical transmittance was measured. The improvement methods were then tested on these electrodes, and the performance was evaluated. Adding metal nanoparticles to the surface of the electrode showed the most promising results. With gold nanoparticles, the impedance at 1 kHz was lowered 82%, and charge storage capacity increased 529%. However, at the same time, 30% of the optical transmittance was lost. With lower nanoparticle density, 6% of transmittance was lost, and 7% of impedance gained. Nitric acid doping did not improve the impedance, but the charge storage capacity was increased up to 66%. Thicker layers of graphene displayed a lower sheet resistance. However, impedance or charge storage capacity were not improved.

The COVID-19 pandemic caused a shortage of Personal Protective Equipment for Healthcare personnel. This project aims to aid in this shortage by extending the lifetime of the filter material used in a mask. This is done in the form of an SPPE, a Smart Personal Protective Equipment. This face mask has two smart filter heads that are modular and contain UVC LEDs to disinfect the filter, a control system to control the LED's radiative power and an on-board power management system. The latter is the focus of this thesis. The implementation of an on-board power management system for a smart personal protection face mask was designed in three stages: (1) researching existing theory about battery management, (2) implementing and verifying a system design in Simulink and (3) making a PCB design and selecting off-the-shelf components. The goal of this thesis is to make a complete design of a functional battery management system, that supplies required power to the rest of the system, ensuring safe battery operation and aiming to maximize battery life. In this, the design has succeeded as almost all requirements are met. The result is a PCB design that can be made and combined with two other subgroups to create a Smart Personal Protection face mask. The main findings were a different and possibly new approach to estimating the State of Charge of a battery and designing a Battery management system for low power applications in a small form factor as opposed to battery management systems for electrical vehicles, which are common today.

This project is to design and implement a reconfigurable measurement interface for Internet of Things sensors, for the Microelectronics Department of the Delft University of Technology. This thesis will discuss the functionality and design process taken in designing such a reconfigurable measurement interface, focusing on generating signals and control control signals for the hard- ware. The important choices will be highlighted and the strengths and weaknesses of each design choice will be weighed in order to produce a fully functional measurement interface. In the span of 10 weeks this group was able to successfully generate sinusoidal, square, and triangular waves as well as DC voltages at +12V and +24V and in the voltage range of -10V to +10V on different pins of the dip-48 socket. Sensor output voltages can also be measured and observed using an external computer.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia occurring in around 0.5% of the world population. AF is characterized by the rapid and irregular beating of the atrial chambers of the heart, which can cause lead to strokes and other heart-failures. To prevent these consequences the early detection of AF is paramount. Using photoplethysmography (PPG) heart activity can be measured from which the inter-beat-interval (IBI), the time between heart beats, can be estimated. Using data collected by a PPG sensor the aim is to classify the heart activity as either AF or Normal Sinus Rhythm in real time using machine learning and collect the outcomes for further analysis by medical professionals. For this a classification method is suggested which is able to be implemented on an ARM based processor. Using a Support Vector Machine and 10 features derived from the IBI's and the PPG signal this algorithm achieves the following accuracy metrics: balanced accuracy = 0.853, sensitivity = 0.850, specificity = 0.856 and Matthews Correlation Coefficient (MCC) = 0.643. Compared to similar studies these results are substandard and should be improved.

Resistive RAM, or RRAM, is one of the emerging non-volatile memory (NVM) technologies, which could be used in the near future to fill the gap in the memory hierarchy between dynamic RAM (DRAM) and Flash, or even completely replace Flash. RRAM operates faster than Flash, but is still non-volatile, which enables it to be used in a dense 3D NVM array. It is also a suitable candidate for computation-in-memory, neuromorphic computing and reconfigurable computing. However, the show stopping problem of RRAM is that it suffers from unique defects, which is the reason why RRAM is still not widely commercially adopted. These defects differ from those that appear in CMOS technology, due to the arbitrary nature of the forming process. They can not be detected by conventional tests and cause defective devices to go unnoticed. Therefore, new tests need to be developed that properly include the physics of a defective device in a RRAM model. Device-aware testing (DAT) is the state-of-the-art solution to this problem. By accounting for the unique physics of an RRAM device, DAT is able to detect unique RRAM defects. However, DAT bases its results on relatively compact electrical models, which do not account for randomness present in e.g. the forming of the filament and local temperature fluctuations. Meanwhile, many low-level physical models exist already that can model this randomness and provide accurate insights into the physical specifics of RRAM. These models do, however, hardly ever analyze the effects of defects. The contribution of this work is to expand and improve one of the state-of-the-art physical models to analyse manufacturing defects on a low, near atomic-level scale. For the first time, the characteristics of a defect can be described in the physical shape of the defect, rather than only the electrical consequences of a black box device. This enables deep level analysis and characterization of defects, the results of which improves DAT to detect even more unique defects. The model is applied to four types of RRAM-related defects: oxygen vacancy density fluctuation, oxide thickness variation, electrode roughness, and contamination by impurities. The effect of the defects on the conductivity of the device are observed and explained, and their unique non-linear behavior is confirmed by simulation. Dynamic defects are not yet included, but the model does provide an extensive static characterization of unique RRAM defects, providing insights into their behavior and improving the quality of DAT. Finally, a discussion is presented which criticizes the reproducability of the referenced defect-free model, but also shows the potential of this work's model to be improved.

In the past few decades, there has been a growing demand for low-cost disposable sensors to be utilized in hazardous and toxic environments such as high temperature and chemically aggressive environments, which can lead to irreversible damage to the sensor. Moreover, by cost reduction, they become more attractive for utilization in developing countries, where the research of new biomedical technologies has been restricted due to a decentralized healthcare system, lack of infrastructure and shortage of investment. This research focuses on developing a low-cost, disposable and easy to use mass sensor utilizing diamagnetic levitation. Diamagnetic levitation is the only stable form of levitation at room temperature and does not require external sources of energy, feedback loops or cooling for stable levitation. It is a low cost, easy to use, disposable sensor that makes it suitable for potential point-of-care applications. The sensor consists of a pyrolytic graphite plate that levitates above a checkerboard arrangement of permanent magnets with alternating magnetization. The system is actuated using electromagnetic excitation and the material properties of the sample are extracted by conducting a multi-modal analysis of the resonance frequencies and mode shapes; measured using a laser Doppler vibrometer (LDV). An opto-electronic phase lock loop utilizing the LDV is used to bring the levitating pyrolytic graphite into sustained mechanical oscillation controlled by a PID controller. The experimental mass sensitivity is measured by analysing the frequency shift associated with the placement of glass beads. The frequency stability is measured by computing the Allan deviation and the experimental precision of mass sensor is then computed. The results show that the precision of a diamagnetically levitated resonant mass sensors decreases with the decrease of the size of the levitating oscillator. The precision of a levitating oscillator of side length 2mm and thickness 0.08mm is found to be 14.4pg at a gate time of 0.1s for an instantaneous change in frequency.