Добірка наукової літератури з теми "Bio/CMOS interface"

Field-effect transistor (FET)-based biosensors have a wide range of applications, and a bio-FET odorant sensor, based on insect (Sf21) cells expressing insect odorant receptors (ORs) with sensitivity and selectivity, has emerged. To fully realize the practical application of bio-FET odorant sensors, knowledge of the cell–device interface for efficient signal transfer, and a reliable and low-cost measurement system using the commercial complementary metal-oxide semiconductor (CMOS) foundry process, will be indispensable. However, the interfaces between Sf21 cells and sensor devices are largely unknown, and electrode materials used in the commercial CMOS foundry process are generally limited to aluminium, which is reportedly toxic to cells. In this study, we investigated Sf21 cell–device interfaces by developing cross-sectional specimens. Calcium imaging of Sf21 cells expressing insect ORs was used to verify the functions of Sf21 cells as odorant sensor elements on the electrode materials. We found that the cell–device interface was approximately 10 nm wide on average, suggesting that the adhesion mechanism of Sf21 cells may differ from that of other cells. These results will help to construct accurate signal detection from expressed insect ORs using FETs.

The Complementary Metal-Oxide Semiconductor (CMOS) technology has evolved to a great extent and is being used for different applications like environmental, biomedical, radiofrequency and switching, etc. Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) based biosensors are used for detecting various enzymes, molecules, pathogens and antigens efficiently with a less time-consuming process involved in comparison to other options. Early-stage detection of disease is easily possible using Field-Effect Transistor (FET) based biosensors. In this paper, a steep subthreshold heavily doped n+ pocket asymmetrical junctionless MOSFET is designed for biomedical applications by introducing a nanogap cavity region at the gate-oxide interface. The nanogap cavity region is introduced in such a manner that it is sensitive to variation in biomolecules present in the cavity region. The analysis is based on dielectric modulation or changes due to variation in the bio-molecules present in the environment or the human body. The analysis of proposed asymmetrical junctionless MOSFET with nanogap cavity region is carried out with different dielectric materials and variations in cavity length and height inside the gate–oxide interface. Further, this device also showed significant variation for changes in different introduced charged particles or region materials, as simulated through a 2D visual Technology Computer-Aided Design (TCAD) device simulator.

The ongoing vivid advance in integration technologies is giving leverage both to computing systems as well as to sensors and sensor systems. Both conventional computing systems as well as innovative computing systems, e.g., following bio-inspiration from nervous systems or neural networks, require efficient interfacing to an increasing diversity of sensors under the constraints of metrology. The realization of sufficiently accurate, robust, and flexible analog front-ends (AFE) is decisive for the overall application system and quality and requires substantial design expertise both for cells in System-on-Chip (SoC) or chips in System-in-Package (SiP) realizations. Adding robustness and flexibility to sensory systems, e.g., for Industry 4.0., by self-X or self-* features, e.g., self-monitoring, -trimming, or -healing (AFEX) approaches the capabilities met in living beings and is pursued in our research. This paper summarizes on two chips, denoted as Universal-Sensor-Interface-with-self-X-properties (USIX) based on amplitude representation and reports on recently identified challenges and corresponding advanced solutions, e.g., on circuit assessment as well as observer robustness for classic amplitude-based AFE, and transition activities to spike domain representation spiking-analog-front-ends with self-X properties (SAFEX) based on adaptive spiking electronics as the next evolutionary step in AFE development. Key cells for AFEX and SAFEX have been designed in XFAB xh035 CMOS technology and have been subject to extrinsic optimization and/or adaptation. The submitted chip features 62,921 transistors, a total area of 10.89 mm2 (74% analog, 26% digital), and 66 bytes of the configuration memory. The prepared demonstrator will allow intrinsic optimization and/or adaptation for the developed technology agnostic concepts and chip instances. In future work, confirmed cells will be moved to complete versatile and robust AFEs, which can serve both for conventional as well as innovative computing systems, e.g., spiking neurocomputers, as well as to leading-edge technologies to serve in SOCs.

Neuromorphic computing inspired by the neural network systems of the human brain enables energy efficient computing for big-data processing. A neural network is formed by thousands or even millions of neurons which are connected by even a higher number of synapses. Neurons communicate with each other through the connected synapses. The main responsibility of synapses is to transfer information from the pre-synaptic to the postsynaptic neurons. Synapses can memorize and process the information simultaneously. The plasticity of a synapse to strengthen or weaken their activity over time make it capable of learning and computing. Thus, artificial synapses which can emulate functionalities and the plasticity of bio-synapses form the backbones of neuromorphic computing. Alternative artificial synapses have been successfully demonstrated. The classical two-terminal memristor devices, like resistive random access memory (ReRAM), phase change memory (PCM) and ferroelectric tunnel junctions (FTJs) with one terminal connected to the pre-synaptic neuron and another connected with the post-synaptic neuron, own advantages of simple structure, easy processing with high density, and capability of integration with CMOS. However, signal processing and learning cannot be performed simultaneously in 2-terminal devices, thus limiting their synaptic functionalities. Ferroelectric field effect transistors (FeFET) which uses ferroelectric as the gate oxide are the most interesting three-terminal artificial synapse devices, in which the gate or the source is connected to the pre-synaptic neuron while the drain is used for the terminal of the post-synaptic neuron , thus can perform signal transmission and learning simultaneously. However, traps at the channel interface can degrade the device performance causing low endurance. Focuses of those abovementioned devices have been mainly put on the homosynaptic plasticity, which is input specific, meaning that the plasticity occurs only at the synapse with a pre-synaptic activation . The homosynaptic plasticity has a drawback of positive feedback loop: when a synapse is potentiated, the probability of the synapse to be further potentiated is increased. Similarly, when a synapse is depressed the probability of the synapse of being further depressed is higher. Therefore, synaptic weights tend to be either strengthened to the maximum value or weakened to zero, causing the system to be unstable. In contrast, heterosynaptic plasticity can be induced at any synapse at the same time after episodes of strong postsynaptic activity, avoiding the positive feedback problem and stabilize the activity of the post-synaptic neuron. To address the above challenges we proposed a very simple 4-terminal synapse structure based on gated Schottky diodes on silicon (FEMOD) with a ferroelectric layer. The conductance of the Schottky diode is modulated by the polarization of the ferroelectric layer. With this simple synapse structure we can achieve multiple hetero-synaptic functions, including excitatory/ inhibitory post-synaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), long-term potentiation/depression (LTP/LTD), as well as biological neuron-like spike-timing-dependent plasticity (STDP) characteristics. The modulatory synapse can modify the weight of another synapse with a very low voltage. Furthermore, logic gates, like AND and NAND which are highly desired for in-memory computing can be realized with such simple structure. Figure 1

As the rapid development mobile phone and mobile internet, the mobile phone is considered as the import interface between brands and consumers by managers. Mobile advertising, whichis defined as “any form of marketing, advertising or sales promotion activity aimed at consumers and conducted over a mobile channel is becoming the new promising marketing channel. Given this importance, how to conduct mobile advertising campaign to increase advertising effect is an big challenge for CMOs. To help the CMO to understand the mobile advertising, this paper reviewed recent research advances in mobile advertising researches.

With the slowing down of Moore’s law, related to conventional scaling of integrated circuits, alternative technologies will require research effort for pushing the limits of new generations of electronic or photonic devices. Perovskite oxides with the ABO3 chemical formula have a very wide range of interesting intrinsic properties such as metal-insulator transition, ferroelectricity, pyroelectricity, piezoelectricity, ferromagnetic and superconductivity. For the integration of such oxides, it is of great interest to combine their properties with traditional electronic, memory and optical devices on the same silicon-based platform. In the context of high-speed chip-to-chip optical interconnects, compact high-resolution beam steering and video-rate RGB hologram generation require the integration of fast and efficient optical modulators on top of silicon CMOS devices. For these applications the integration of high quality electro-optical materials A defect-free material-stack deposition on silicon wafers is hence required. Among the possible materials options, barium titanate (BaTiO3) is one promising candidate due to its large intrinsic Pockels coefficients that can be obtained. In a first part of the talk, we will review the different options to integrate BaTiO3 on Silicon substrate though different templates to control the polarization direction and discuss the influence on the physical, electrical and optical properties. Then in the second section we will discuss the use of perovskites oxide in the field of topological based qubits which is one of the promising methods for realizing fault-tolerant computations. It is recognized that superconductor/topological insulator heterostructure interfaces may be a perfect host for the exotic “Majorana” particles. These have relevant topological protection nature as required for processing information. Therefore, the physics at the superconductor/topological insulator heterostructure interface need to be studied further, starting at the material level. In this work, a candidate material Barium Bismuthate (BBO) is studied utilizing the Oxide Molecular Beam Epitaxy (MBE) process. The perovskite structure provides opportunity for easily tailored functionality through substitutional doping. Incorporation of potassium into the lattice of BBO results in a superconducting phase with Curie temperature as high as ~ 30K. In addition, BBO is according to DFT based studies, predicted to form topological surface states when doped with Fluorine. In our work, we integrate BBO perovskite on Si(001) substrate, using an epitaxially grown strontium titanate (STO) single-crystalline buffer layer and discuss the structural and chemical properties of the heterostructure will be established by utilizing physical characterization techniques such as AFM, and TEM in later stages. This will go hand in hand with the understanding of the ARPES studies and related surface reconstruction of BBO observed by RHEED as a criterion for the high-quality films. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreements No 864483 and 742299)”.

AbstractA CMOS integrated microelectrode array (IMA) utilizing a low-inpedance transimpedance input has been developed. The design uses a three-stage amplifier, including a transimpedance input stage, buffer, and multiplexor. The input stage presents a low impedance which provides bias for the neural input and maximizes the current transfer into the circuit. The amplifiers are replicated in an 8x8 array, enabling measurement of a network of 64 neurons invitro. The chip was post-processed to provide a bio-compatible gold interface between the electrodes and the neurons. Simulations and experimental results demonstrate the functionality of the new design. Additional measurements are underway to provide experimental confirmation of the system with in-vitro neurons.

The improvement of devices provided by Nanotechnology has put forward new classes of sensors, called bio-nanosensors, which are very promising for the detection of biochemical molecules in a large variety of applications. Their use in lab-on-a-chip could gives rise to new opportunities in many fields, from health-care and bio-warfare to environmental and high-throughput screening for pharmaceutical industry. Bio-nanosensors have great advantages in terms of cost, performance, and parallelization. Indeed, they require very low quantities of reagents and improve the overall signal-to-noise-ratio due to increase of binding signal variations vs. area and reduction of stray capacitances. Additionally, they give rise to new challenges, such as the need to design high-performance low-noise integrated electronic interfaces. This thesis is related to the design of high-performance advanced CMOS interfaces for electrochemical bio-nanosensors. The main focus of the thesis is: 1) critical analysis of noise in sensing interfaces, 2) devising new techniques for noise reduction in discrete-time approaches, 3) developing new architectures for low-noise, low-power sensing interfaces. The manuscript reports a multi-project activity focusing on low-noise design and presents two developed integrated circuits (ICs) as examples of advanced CMOS interfaces for bio-nanosensors. The first project concerns low-noise current-sensing interface for DC and transient measurements of electrophysiological signals. The focus of this research activity is on the noise optimization of the electronic interface. A new noise reduction technique has been developed so as to realize an integrated CMOS interfaces with performance comparable with state-of-the-art instrumentations. The second project intends to realize a stand-alone, high-accuracy electrochemical impedance spectroscopy interface. The system is tailored for conductivity-temperature-depth sensors in environmental applications, as well as for bio-nanosensors. It is based on a band-pass delta-sigma technique and combines low-noise performance with low-power requirements.

The improvement of devices provided by Nanotechnology has put forward new classes of sensors, called bio-nanosensors, which are very promising for the detection of biochemical molecules in a large variety of applications. Their use in lab-on-a-chip could gives rise to new opportunities in many fields, from health-care and bio-warfare to environmental and high-throughput screening for pharmaceutical industry. Bio-nanosensors have great advantages in terms of cost, performance, and parallelization. Indeed, they require very low quantities of reagents and improve the overall signal-to-noise-ratio due to increase of binding signal variations vs. area and reduction of stray capacitances. Additionally, they give rise to new challenges, such as the need to design high-performance low-noise integrated electronic interfaces. This thesis is related to the design of high-performance advanced CMOS interfaces for electrochemical bio-nanosensors. The main focus of the thesis is: 1) critical analysis of noise in sensing interfaces, 2) devising new techniques for noise reduction in discrete-time approaches, 3) developing new architectures for low-noise, low-power sensing interfaces. The manuscript reports a multi-project activity focusing on low-noise design and presents two developed integrated circuits (ICs) as examples of advanced CMOS interfaces for bio-nanosensors. The first project concerns low-noise current-sensing interface for DC and transient measurements of electrophysiological signals. The focus of this research activity is on the noise optimization of the electronic interface. A new noise reduction technique has been developed so as to realize an integrated CMOS interfaces with performance comparable with state-of-the-art instrumentations. The second project intends to realize a stand-alone, high-accuracy electrochemical impedance spectroscopy interface. The system is tailored for conductivity-temperature-depth sensors in environmental applications, as well as for bio-nanosensors. It is based on a band-pass delta-sigma technique and combines low-noise performance with low-power requirements.

Neuronal networks are at the base of information processing in the brain. They are series of interconnected neurons whose activation defines a recognizable linear pathway. The main goal of studying neural ensembles is to characterize the relationship between the stimulus and the individual or general neuronal responses and the relation amongst the electrical activities of neurons within the network, also understanding how topology and connectivity relates to their function. Many techniques have been developed to study these complex systems: single-cell approaches aim to investigate single neurons and their connections with a limited number of other nerve cells; on the opposite side, low-resolution large-scale approaches, such as functional MRI (Magnetic Resonance Imaging) or electroencephalography (EEG), record signal changes in the brain that are generated by large populations of cells. More recently, multisite recording techniques have been developed to overcome the limitations of previous approaches, allowing to record simultaneously from huge neuronal ensembles with high spatial resolution and in different brain regions, i.e. by using implantable semiconductor chips. Local Field Potentials (LFPs), the part of electrophysiological signals that has frequencies below 500 Hz, capture key integrative synaptic processes that cannot be measured by analyzing the spiking activity of few neurons alone. Several studies have used LFPs to investigate cortical network mechanisms involved in sensory processing, motor planning and higher cognitive processes, like memory and perception. LFPs are also promising signals for steering neuroprosthetic devices and for monitoring neural activity even in human beings, since they are more easily and stably recorded in chronic settings than neuronal spikes. In this work, LFP profiles recorded in the rat barrel cortex through high-resolution CMOS-based needle chips are presented and compared to those obtained by means of conventional Ag/AgCl electrodes inserted into glass micropipettes, which are widely used tools in electrophysiology. The rat barrel cortex is a well-known example of topographic mapping where each of the whiskers on the snout of the animal is mapped onto a specific cortical area, called a barrel. The barrel cortex contains the somatosensory representation of the whiskers and forms an early stage of cortical processing for tactile information, along with the trigeminal ganglion and the thalamus. It is an area of great importance for understanding how the cerebral cortex works, since the cortical columns that form the basic building blocks of the neocortex can be actually seen within the barrel. Moreover, the barrel cortex has served as a test-bed system for several new methodologies, partly because of its unique and instantly identifiable form, and partly because the species that have barrels, i.e. rodents, are the most commonly used laboratory mammal. The barrel cortex, the whiskers that activate it and the intervening neural pathways have been increasingly the subject of focus by a growing number of research groups for quite some time. Nowadays, studies (such this one) are directed not only at understanding the barrel cortex itself but also at investigating issues in related fields using the barrel cortex as a base model. In this study, LFP responses were evoked in the target barrel by repeatedly deflecting the corresponding whisker in a controlled fashion, by means of a specifically designed closed-loop piezoelectric bending system triggered by a custom LabView acquisition software. Evoked LFPs generated in the barrel cortex by many consecutive whiskers' stimulations show large variability in shapes and timings. Moreover, anesthetics can deeply affect the profile of evoked responses. This work presents preliminary results on the variability and the effect of commonly used anesthetics on these signals, by comparing the distributions of evoked responses recorded from rats anesthetized with tiletamine-xylazine, which mainly blocks the excitatory NMDA receptors, and urethane, which conversely affects both the excitatory and inhibitory system, in a complex and balanced way yet preserving the synaptic plasticity. Representative signal shape characteristics (e.g., latencies and amplitude of events) extracted from evoked responses acquired from different cortical layers are analyzed and discussed. Statistical distributions of these parameters are estimated for all the different depths, in order to assess the variability of LFPs generated by individual mechanical stimulations of single whiskers along the entire cortical column. Preliminary results showed a great variability in cortical responses, which varied both in latency and amplitude across layers. We found significant difference in the latency of the first principal peak of the responses: under tiletamine-xylazine anesthetic, the responses or events of the evoked LFPs occurred later than the ones recorded while urethane was administered. Furthermore, the distributions of this parameter in all cortical layers were narrower in case of urethane. This behavior should be attributed to the different effects of these two anesthetics on specific synaptic receptors and thus on the encoding and processing of the sensory input information along the cortical pathway. The role of the ongoing basal activity on the modulation of the evoked response was also investigated. To this aim, spontaneous activity was recorded in different cortical layers of the rat barrel cortex under the two types of anesthesia and analyzed in the statistical context of neuronal avalanches. A neuronal avalanche is a cascade of bursts of activity in neural networks, whose size distribution can be approximated by a power law. The event size distribution of neuronal avalanches in cortical networks has been reported to follow a power law of the type P(s)= s^-a, with exponent a close to 1.5, which represent a reflection of long-range spatial correlations in spontaneous neuronal activity. Since negative LFP peaks (nLFPs) originates from the sum of synchronized Action Potentials (AP) from neurons within the vicinity of the recording electrode, we wondered if it were possible to model single nLFPs recorded in the basal activity traces by means of only one electrode as the result of local neuronal avalanches, and thus we analyzed the size (i.e. the amplitude in uV) distribution of these peaks so as to identify a suitable power-law distribution that could describe also these single-electrode records. Finally, the results of the first ever measurements of evoked LFPs within an entire column of the barrel cortex obtained by means of the latest generation of CMOS-based implantable needles, having 256 recording sites arranged into two different array topologies (i.e. 16 x 16 or 4 x 64, pitches in the x- and y-direction of 15 um and 33 um respectively), are presented and discussed. A propagation dynamics of the LFP can be already recognized in these first cortical profiles. In the next future, the use of these semiconductor devices will help, among other things, to understand how degenerating syndromes like Parkinson or Alzheimer evolve, by coupling detected behaviors and symptoms of the disease to neuronal features. Implantable chips could then be used as 'electroceuticals', a newly coined term that describes one of the most promising branch of bioelectronic medicine: they could help in reverting the course of neurodegenerative diseases, by constituting the basis of neural prostheses that physically supports or even functionally trains impaired neuronal ensembles. High-resolution extraction and identification of neural signals will also help to develop complex brain-machine interfaces, which can allow intelligent prostheses to be finely controlled by their wearers and to provide sophisticated feedbacks to those who have lost part of their body or brain functions.
Le reti neuronali sono alla base della codifica dell'informazione cerebrale. L'obiettivo principale dello studio delle popolazioni neuronali è quello di caratterizzare la relazione tra uno stimolo e la risposta individuale o globale dei neuroni e di studiare il rapporto tra le varie attività elettriche dei neuroni appartenenti ad una particolare rete, comprendendo anche come la topologia e la connettività della rete neuronale influiscano sulla loro funzionalità. Fino ad oggi, molte tecniche sono state sviluppate per studiare questi sistemi complessi: studi a singola cellula mirano a studiare singoli neuroni e le loro connessioni con un numero limitato di altre cellule; sul lato opposto, approcci su larga scala e a bassa risoluzione, come la risonanza magnetica funzionale o l'elettroencefalogramma, registrano segnali elettrofisiologici generati nel cervello da vaste popolazioni di cellule. Più recentemente, sono state sviluppate tecniche di registrazione multisito che mirano ad abbattere le limitazioni dei precedenti approcci, rendendo possibile la misurazione ad alta risoluzione di segnali generati da grandi ensamble neuronali e da diverse regioni del cervello simultaneamente, ad esempio mediante l'uso di chip impiantabili a semiconduttore. I potenziali di campo locali (LFP) catturano processi sinaptici chiave che non possono essere estratti dall'attività di spiking di qualche neurone isolato. Numerosi studi hanno utilizzato gli LFP per studiare i meccanismi corticali coinvolti nei processi sensoriali, motori e cognitivi, come la memoria e la percezione. Gli LFP rappresentano anche dei segnali interessanti nell'ambito delle applicazioni neuroprotesiche e per monitorare l'attività cerebrale negli esseri umani, dal momento che possono essere registrati più stabilmente e facilmente in impianti cronici rispetto agli spike neuronali. In questo studio, sono riportati dei profili LFP registrati dalla barrel cortex di ratto tramite chip ad ago ad alta risoluzione basati su tecnologia CMOS e confrontati con quelli ottenuti tramite elettrodi convenzionali in Ag/AgCl inseriti in micropipette di vetro, strumenti comunemente usati in elettrofisiologia. La barrel cortex di ratto è un esempio ben noto di mapping topografico, nel quale ogni baffo sul muso dell'animale è mappato in una specifica area corticale, chiamata barrel. La barrel cortex contiene la rappresentazione sensoriale dei baffi dell'animale e rappresenta uno dei primi stadi di elaborazione dell'informazione tattile, insieme al ganglio del trigemino e al talamo. Essa è un'area di primaria importanza per lo studio del funzionamento della corteccia cerebrale, visto che le colonne corticali che formano i blocchi di base della neocorteccia possono essere visualizzati facilmente all'interno della barrel cortex. La barrel cortex inoltre è utilizzata come sistema di test in numerose metodologie innovative, grazie alla sua struttura unica ed istantaneamente identificabile, e grazie anche al fatto che le specie dotate di barrel, i roditori, sono gli animali da laboratorio più comuni. La barrel cortex e le sue interconnessioni neuronali sono stati oggetto delle ricerche più disparate in questi ultimi decenni. Attualmente, alcuni studi (come questo) non mirano solamente a comprendere meglio la barrel cortex, ma anche ad analizzare problematiche in campi scientifici collegati, utilizzando la barrel cortex come modello base. In questo lavoro, sono stati evocati segnali LFP nella barrel cortex tramite deflessioni ripetute dei baffi dell'animale, realizzate in modo controllato tramite un sistema di deflessione piezoelettrica a closed-loop innescato da un sistema di acquisizione LabView. Le risposte evocate generate nella barrel dalla stimolazione ripetuta dei baffi presentano elevata variabilità nella forma e nelle latenze temporali. Inoltre, il tipo di anestesia utilizzata può influenzare profondamente il profilo della risposta evocata. Questo studio riporta i risultati preliminari sulla variabilità della risposta neuronale e sull'effetto di due anestetici di uso comune su questi segnali, confrontando le distribuzioni delle risposte evocate in ratti anestetizzati con tiletamina-xylazina (il quale agisce prevalentemente sui recettori eccitatori di tipo NMDA) e uretano (che agisce in modo più bilanciato e complesso su entrambi i sistemi eccitatori ed inibitori, preservando la plasticità sinaptica). Sono state analizzate e discusse alcune caratteristiche rappresentative del segnale evocato (ad esempio, le latenze temporali e l'ampiezza degli eventi), registrato a varie profondità corticali. Per tutte le prondità corticali acquisite, sono state stimate le distribuzioni statistiche di tali parametri, in modo da valutare la variabilità degli LFP evocati dalle stimolazioni meccaniche individuali delle vibrisse del ratto lungo l'intera colonna corticale. I primi risultati presentano una grande variabilità nelle risposte corticali, sia in latenza che in ampiezza. Inoltre, è stata riscontrata una differenza significativa nella latenza del primo picco principale delle risposte evocate: gli LFP evocati in animali anestetizzati con tiletamina-xylazina presentavano una latenza più lunga di quelli registrati in ratti anestetizzati con uretano. Inoltre, le distribuzioni dei parametri analizzati erano più strette e piccate in uretano, in corrispondenza di tutte le profondità corticali. Questo comportamento è sicuramente da attribuire al differente meccanismo d'azione dei due anestetici su specifici recettori sinaptici, e quindi nell'elaborazione e nella trasmissione dell'informazione sensoriale lungo tutto il percorso corticale. E' stato inoltre discusso il ruolo della attività basale nella modulazione della risposta evocata. A questo proposito, è stata registrata l'attività spontanea in corrispondenza dei vari layer corticali ed analizzata nel contesto statistico delle 'valanghe neuronali'. Una valanga neuronale è una cascata di attività elettrica in una rete neuronale, la cui distribuzione statistica dei parametri principali (dimensione e vita media) può essere approssimata da una legge di potenza. La distribuzione delle dimensioni di una valanga in una rete neuronale segue una legge di potenza del tipo P(s)=s^-a, con a=1.5. Tale esponente è un riflesso delle correlazioni spaziali a lungo raggio nell'attività neuronale spontanea. Dal momento che i picchi negativi (nLFPs) nelle tracce elettrofisiologiche originano dalla somma di potenziali d'azione sincronizzati generati da neuroni posti nelle vicinanze dell'elettrodo di registrazione, ci siamo chiesti se fosse possibile modellizare i singoli nLFP registrati nell'attività basale tramite un singolo elettrodo come il risultato di valanghe neuronali locali. Pertanto, abbiamo analizzato la distribuzione della dimensione (cioè l'ampiezza in uV) di tali picchi, in modo da identificare una distribuzione power-law appropriata, che potesse descrivere anche le registrazioni a singolo elettrodo. Infine, sono presentate e discusse le prime registrazioni in assoluto degli LFP evocati lungo un'intera colonna corticale ottenute tramite l'ultima generazione di chip impiantabili a tecnologia CMOS. Questi ultimi presentano una matrice di 256 siti di registrazione, organizzata secondo due possibili topologie, 16 x 16 o 4 x 64, e avente una distanza tra gli elettrodi pari a 15 um o 33 um rispettivamente. Una precisa dinamica di propagazione dei potenziali evocati può già essere riconosciuta in questi primissimi profili corticali. Nel prossimo futuro, l'uso di questi dispositivi a semiconduttore potrà aiutare a comprendere il decorso di sindromi neurodegerative come il Parkinson o l'Alzheimer, associando sintomi e comportamenti tipo della malattia a specifiche caratteristiche neuronali. I chip impiantabili potranno anche essere utilizzati come 'electroceuticals', ossia potranno aiutare a rallentare (o addirittura a capovolgere) il decorso delle malattie neurogenerative, costituendo le basi di protesi neuronali in grado di sostenere fisicamente o allenare funzionalmente le popolazioni neuronali danneggiate. L'identificazione e il rilevamento di segnali neuronali ad alta risoluzione aiuterà anche a sviluppare complesse interfacce cervello-macchina, che consentiranno il controllo di protesi intelligenti e che forniranno sofisticati meccanismi di feedback a chi ha perso l'uso di alcune parti del proprio corpo o determinate funzioni cerebrali.

Internet of Things is expanding to the areas such as healthcare, home management, industrial, agriculture, and becoming pervasive in our life, resulting in improved efficiency, accuracy and economic benefits. Smart sensors with embedded interfacing integrated circuits (ICs) are important enablers, hence, variety of smart sensors are required. However, each type of sensor requires specific interfacing chips, which divides the huge market of sensors’ interface chips into lots of niche markets, resulting in high develop cost and long time-to-market period for each type. Software defined sensor is regarded as a promising solution, which is expected to use a flexible interface platform to cover different sensors, deliver specificity through software programming, and integrate easily into the Internet of Things. In this work, research is carried out on the design and implementations of ultra low power analog interface circuits toward software defined sensors for healthcare services based on Internet of Things.    This thesis first explores architectures and circuit techniques for energy-efficient and flexible analog to digital conversion. A time-spreading digital calibration, to calibrate the errors due to finite gain and capacitor mismatch in multi-bit/stage pipelined converters, is developed with short convergence time. The effectiveness of the proposed technique is demonstrated with intensive simulations. Two novel circuit level techniques, which can be combined with digital calibration techniques to further improve the energy efficiency of the converters, are also presented. One is the Common-Mode-Sensing-and-Input-Interchanging (CSII) operational-transconductance-amplifier (OTA) sharing technique to enable eliminating potential memory effects. The other is a workload-balanced multiplying digital-to-analog converter (MDAC) architecture to improve the settling efficiency of a high linear multi-bit stage. Two prototype converters have been designed and fabricated in 0.13 μm CMOS technology. The first one is a 14 bit 50 MS/s digital calibrated pipelined analog to digital converter that employs the workload-balanced MDAC architecture and time-spreading digital calibration technique to achieve improved power-linearity tradeoff. The second one is a 1.2 V 12 bit 5~45 MS/s speed and power-scalable ADC incorporating the CSII OTA-sharing technique, sample-and-hold-amplifier-free topology and adjustable current bias of the building blocks to minimize the power consumption. The detailed measurement results of both converters are reported and deliver the experimental verification of the proposed techniques.     Secondly, this research investigates ultra-low-power analog front-end circuits providing programmability and being suitable for different types of sensors. A pulse-width- -modulation-based architecture with a folded reference is proposed and proven in a 0.18 μm technology to achieve high sensitivity and enlarged dynamic range when sensing the weak current signals. A 8-channel bio-electric sensing front-end, fabricated in a 0.35 μm CMOS technology is also presented that achieves an input impedance of 1 GΩ, input referred noise of 0.97 Vrms and common mode rejection ratio of 114 dB. With the programmable gain and cut-off frequency, the front-end can be configured to monitor for long-term a variety of bio-electric signals, such as electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG) and electrocardiogram (ECG) signals. The proposed front-end is integrated with dry electrodes, a microprocessor and wireless link to build a battery powered E-patch for long-term and continuous monitoring. In-vivo test results with dry electrodes in the field trials of sitting, standing, walking and running slowly, show that the quality of ECG signal sensed by the E-patch satisfies the requirements for preventive cardiac care.    Finally, a wireless multimodal bio-electric sensor system is presented. Enabled by a customized flexible mixed-signal system on chip (SoC), this bio-electric sensor system is able to be configured for ECG/EMG/EEG recording, bio-impedance sensing, weak current stimulation, and other promising functions with biofeedback. The customized SoC, fabricated in a 0.18 μm CMOS technology, integrates a tunable analog front-end, a 10 bit ADC, a 14 bit sigma-delta digital to current converter, a 12 bit digital to voltage converter, a digital accelerator for wavelet transformation and data compression, and a serial communication protocol. Measurement results indicate that the SoC could support the versatile bio-electric sensor to operate in various applications according to specific requirements.

QC 20151221

The miniaturization of electronics is a technique that holds a lot of potential in improving system performance in a variety of applications. The simultaneous miniaturization of sensors into the nano-scale has provided new ways to probe biological systems. Careful co-design of these electronics and sensors can unlock measurements and experiments that would otherwise be impossible to achieve. This thesis describes the design of two such instrumentation amplifiers and shows that significant gains in temporal resolution and noise performance are possible through careful optimization. A custom integrated amplifier is developed for improving the temporal resolution in nanopore recordings. The amplifier is designed in a commercial 0.18 μm complementary metal-oxide-semiconductor (CMOS) process. A platform is then built with the amplifier at its core that integrates glass-passivated solid-state nanopores to achieve measurement bandwidth over an order of magnitude greater than the state of the art. The use of wavelet transforms for denoising the data and further improving the signal-to-noise ratio (SNR) is then explored. A second amplifier is designed in a 0.18 μm CMOS process for intracellular recordings from neurons. The amplifier contains all the compensation circuitry required for canceling the effects of the electrode non-idealities. Compared to equivalent commercial systems and the state of the art, the amplifier performs comparably or better while consuming orders of magnitude lower power. These systems can inform the design of extremely miniaturized application-specific integrated amplifiers of the future.