Understanding the Bias Dependence of Low Frequency Noise in Sin-gle Layer Graphene FETs

This letter investigates the bias-dependent low frequency noise of single layer graphene field-effect transistors. Noise measurements have been conducted with electrolyte-gated graphene transistors covering a wide range of gate and drain bias conditions for different channel lengths. A new analytical model that accounts for the propagation of the local noise sources in the channel to the terminal currents and voltages is proposed in this paper to investigate the noise bias dependence. Carrier number and mobility fluctuations are considered as the main causes of low frequency noise and the way these mechanisms contribute to the bias dependence of the noise is analyzed in this work. Typically, normalized low frequency noise in graphene devices has been usually shown to follow an M-shape dependence versus gate voltage with the minimum near the charge neutrality point (CNP). Our work reveals for the first time the strong correlation between this gate dependence and the residual charge which is relevant in the vicinity of this specific bias point. We discuss how charge inhomogeneity in the graphene channel at higher drain voltages can contribute to low frequency noise; thus, channel regions nearby the source and drain terminals are found to dominate the total noise for gate biases close to the CNP. The excellent agreement between the experimental data and the predictions of the analytical model at all bias conditions confirms that the two fundamental 1/f noise mechanisms, carrier number and mobility fluctuations, must be considered simultaneously to properly understand the low frequency noise in graphene FETs. The proposed analytical compact model can be easily implemented and integrated in circuit simulators, which can be of high importance for graphene based circuits design.Comment: 18 pages, 10 figure

Velocity Saturation effect on Low Frequency Noise in short channel Single Layer Graphene FETs

Graphene devices for analog and RF applications are prone to Low Frequency Noise (LFN) due to its upconversion to undesired phase noise at higher frequencies. Such applications demand the use of short channel graphene transistors that operate at high electric fields in order to ensure a high speed. Electric field is inversely proportional to device length and proportional to channel potential so it gets maximized as the drain voltage increases and the transistor length shrinks. Under these conditions though, short channel effects like Velocity Saturation (VS) should be taken into account. Carrier number and mobility fluctuations have been proved to be the main sources that generate LFN in graphene devices. While their contribution to the bias dependence of LFN in long channels has been thoroughly investigated, the way in which VS phenomenon affects LFN in short channel devices under high drain voltage conditions has not been well understood. At low electric field operation, VS effect is negligible since carriers velocity is far away from being saturated. Under these conditions, LFN can be precicely predicted by a recently established physics-based analytical model. The present paper goes a step furher and proposes a new model which deals with the contribution of VS effect on LFN under high electric field conditions. The implemented model is validated with novel experimental data, published for the first time, from CVD grown back-gated single layer graphene transistors operating at gigahertz frequencies. The model accurately captures the reduction of LFN especially near charge neutrality point because of the effect of VS mechanism. Moreover, an analytical expression for the effect of contact resistance on LFN is derived. This contact resistance contribution is experimentally shown to be dominant at higher gate voltages and is accurately described by the proposed model.Comment: Main Manuscript:10 pages, 6 figure

Bias Dependent Variability of Low Frequency Noise in Single Layer Graphene FETs

Low-frequency noise (LFN) variability in graphene transistors (GFETs) is for the first time researched in this work. LFN from an adequate statistical sample of long-channel solution-gated single-layer GFETs is measured in a wide range of operating conditions while a physics-based analytical model is derived that accounts for the bias dependence of LFN variance with remarkable performance. It is theoretically proved and experimentally validated that LFN deviations in GFETs stem from physical mechanisms that generate LFN. Thus, carrier number DN due to trapping/detrapping process and mobility fluctuations Dm which are the main causes of LFN, define its variability likewise as its mean value. DN accounts for an M-shape of normalized LFN variance versus gate bias with a minimum at the charge neutrality point (CNP) as it was the case for normalized LFN mean value while Dm contributes only near the CNP for both variance and mean value. Trap statistical nature is experimentally shown to differ from classical Poisson distribution at silicon-oxide devices, and this is probably caused by electrolyte interface in GFETs under study. Overall, GFET technology development is still in a premature stage which might cause pivotal inconsistencies affecting the scaling laws in GFETs of the same process

Frequency response of electrolyte-gated graphene electrodes and transistors

The interface between graphene and aqueous electrolytes is of high importance for applications of graphene in the field of biosensors and bioelectronics. The graphene/electrolyte interface is governed by the low density of states of graphene that limits the capacitance near the Dirac point in graphene and the sheet resistance. While several reports have focused on studying the capacitance of graphene as a function of the gate voltage, the frequency response of graphene electrodes and electrolyte-gated transistors has not been discussed so far. Here, we report on the impedance characterization of single layer graphene electrodes and transistors, showing that due to the relatively high sheet resistance of graphene, the frequency response is governed by the distribution of resistive and capacitive circuit elements along the graphene/electrolyte interface. Based on an analytical solution for the impedance of the distributed circuit elements, we model the graphene/electrolyte interface both for the electrode and the transistor configurations. Using this model, we can extract the relevant material and device parameters such as the voltage-dependent intrinsic sheet and series resistances as well as the interfacial capacitance. The model also provides information about the frequency threshold of electrolyte-gated graphene transistors, above which the device exhibits a non-resistive response, offering an important insight into the suitable frequency range of operation of electrolyte-gated graphene devices

A 1024-Channel 10-Bit 36-μW/ch CMOS ROIC for Multiplexed GFET-Only Sensor Arrays in Brain Mapping

This paper presents a 1024-channel neural read-out integrated circuit (ROIC) for solution-gated GFET sensing probes in massive muECoG brain mapping. The proposed time-domain multiplexing of GFET-only arrays enables low-cost and scalable hybrid headstages. Low-power CMOS circuits are presented for the GFET analog frontend, including a CDS mechanism to improve preamplifier noise figures and 10-bit 10-kS/s A/D conversion. The 1024-channel ROIC has been fabricated in a standard 1.8-V 0.18-mum CMOS technology with 0.012 mm 2 and 36 mu W per channel. An automated methodology for the in-situ calibration of each GFET sensor is also proposed. Experimental ROIC tests are reported using a custom FPGA-based muECoG headstage with 16times 32 and 32times 32 GFET probes in saline solution and agar substrate. Compared to state-of-art neural ROICs, this work achieves the largest scalability in hybrid platforms and it allows the recording of infra-slow neural signals

Multiplexed neural sensor array of graphene solution-gated field-effect transistors

Altres ajuts: this work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MICINN and the ICTS 'NANBIOSIS', more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBERBBN) at the IMB-CNM.Electrocorticography (ECoG) is a well-established technique to monitor electrophysiological activity from the surface of the brain and has proved crucial for the current generation of neural prostheses and brain-computer interfaces. However, existing ECoG technologies still fail to provide the resolution necessary to accurately map highly localized activity across large brain areas, due to the rapidly increasing size of connector footprint with sensor count. This work demonstrates the use of a flexible array of graphene solution-gated field-effect transistors (gSGFET), exploring the concept of multiplexed readout using an external switching matrix. This approach does not only allow for an increased sensor count, but due to the use of active sensing devices (i.e. transistors) over microelectrodes it makes additional buffer transistors redundant, which drastically eases the complexity of device fabrication on flexible substrates. The presented results pave the way for upscaling the gSGFET technology towards large-scale, high-density μECoG-arrays, eventually capable of resolving neural activity down to a single neuron level, while simultaneously mapping large brain regions

Improved metal-graphene contacts for low-noise, high-density microtransistor arrays for neural sensing

Poor metal contact interfaces are one of the main limitations preventing unhampered access to the full potential of two-dimensional materials in electronics. Here we present graphene solution-gated field-effect-transistors (gSGFETs) with strongly improved linearity, homogeneity and sensitivity for small sensor sizes, resulting from ultraviolet ozone (UVO) contact treatment. The contribution of channel and contact region to the total device conductivity and flicker noise is explored experimentally and explained with a theoretical model. Finally, in-vitro recordings of flexible microelectrocorticography (μ-ECoG) probes were performed to validate the superior sensitivity of the UVO-treated gSGFET to brain-like activity. These results connote an important step towards the fabrication of high-density gSGFET μ-ECoG arrays with state-of-the-art sensitivity and homogeneity, thus demonstrating the potential of this technology as a versatile platform for the new generation of neural interfaces

High-Bandwidth Graphene Neural Interfaces

Premi Extraordinari de Doctorat concedit pels programes de doctorat de la UAB per curs acadèmic 2020-2021El funcionament del cervell es basa en processos complexos, que encara no s'han descrit i comprès detalladament. En les últimes dècades, la neurociència ha experimentat un desenvolupament accelerat, impulsat per noves neurotecnologías que permeten monitoritzar les dinàmiques de l'activitat elèctrica al cervell amb una major resolució espai-temporal i una àrea de cobertura més àmplia. No obstant això, a causa de l'alta complexitat de les xarxes neuronals al cervell, que són compostes per poblacions neuronals fortament interconnectades en àmplies regions cerebrals, estem lluny de detectar una fracció significativa de les neurones que donen lloc a funcions complexes. Per tal d'investigar les dinàmiques neuronals a gran escala amb alta resolució espacial, s'han utilitzat diverses tecnologies, que inclouen la ressonància magnética funcional (fMRI), imatges amb marcadors sensibles al voltatge o registres electrofisiològics d'alt recompte de sensors. No obstant això, la resolució temporal del fMRI i els mètodes òptics es limita típicament a uns pocs hertzs, gairebé tres ordres de magnitud per sota de la dels potencials d'acció, i es limiten a les condicions en què el subjecte es troba immòbil. D'altra banda, els registres electrofisiològics basats en matrius de microelèctrodes proporcionen una alta resolució espai-temporal, el que permet detectar amb precisió dinàmiques ràpides de centenars de neurones individuals simultàniament en animals que es mouen lliurement. No obstant això, les interfícies de detecció neuroelectrónica presenten una limitació en el producte entre la resolució espacial i l'àrea de cobertura. A més, presenten una baixa sensibilitat a la banda de freqüència infra-lenta (<0.5Hz), que està relacionada amb la connectivitat funcional de llarg abast. En aquesta tesi es presenta una nova tecnologia basada en sensors actius de grafè, que permet incrementar l'àrea de cobertura i la resolució espacial dels registres electrofisiològics conservant una alta sensibilitat en una banda de freqüència àmplia, des de l'activitat infra-lenta fins a la de una sola cèl·lula electrogénica. Aquest desenvolupament tecnològic es divideix en tres etapes principals; en primer lloc, s'obté una comprensió més profunda de les característiques intrínseques del soroll i la resposta en freqüència d'aquests sensors basant-se en l'estat de l'art en tecnologia de sensors de grafè. En la segona etapa, es mostra un sistema quasi-comercial basat en matrius de sensors de grafè epi-cortical i transmissió sense fil per a la implantació crònica en rates. Amb aquest sistema, es demostra la reproductibilitat de les matrius de sensors de grafè, la seva estabilitat a llarg termini i la seva biocompatibilitat crònica. A més, es proporciona evidència preliminar per a una àmplia gamma de nous patrons electrofisiològics gràcies a la seva sensibilitat en la banda de freqüència infra-lenta. Finalment, en l'última etapa d'aquesta tesi, l'enfocament se centra en el desenvolupament de noves estratègies de multiplexació per augmentar el nombre de sensors a les sondes neuronals. Aquestes tres etapes principals de desenvolupament han portat a la demostració del potencial de les matrius de sensors de grafè multiplexats per al mapejat de les dinàmiques neuronals a gran escala en una banda de freqüència àmplia, en animals que es mouen lliurement, durant llargs períodes. La combinació d'aquestes capacitats fa que les matrius de sensors actius de grafè siguin una tecnologia prometedora per a interfícies cervell-ordinador d'alt ample de banda i una eina única per investigar el paper de l'activitat infra-lenta en la coordinació de les dinàmiques neuronals d'alta freqüència.El funcionamiento del cerebro se basa en procesos complejos, que aún no se han descrito y comprendido detalladamente. En las últimas décadas, la neurociencia ha experimentado un desarrollo acelerado, impulsado por nuevas neurotecnologías que permiten monitorear la dinámica de la actividad eléctrica en el cerebro con una mayor resolución espacio-temporal y un área de cobertura más amplia. Sin embargo, debido a la alta complejidad de las redes neuronales en el cerebro, que están compuestas por poblaciones neuronales fuertemente interconectadas en amplias regiones cerebrales, estamos lejos de monitorear una fracción significativa de neuronas que dan lugar a funciones complejas. Con el fin de investigar las dinámica neuronales a gran escala con alta resolución espacial, se han utilizado diversas tecnologías, que incluyen la resonancia magnética funcional (fMRI), imágenes con marcadores sensibles al voltaje o registros electrofisiológicos de alto conteo de sensores. Sin embargo, la resolución temporal del fMRI y los métodos ópticos se limita típicamente a unos pocos hercios, casi tres órdenes de magnitud por debajo de la de los potenciales de acción, y se limitan a condiciones en los que el sujeto se encuentra inmóvil. Por otro lado, los registros electrofisiológicos basados en matrices de microelectrodos proporcionan una alta resolución espacio-temporal, lo que permite detectar con precisión dinámicas rápidas de cientos de neuronas individuales simultáneamente en animales que se mueven libremente. Sin embargo, las interfaces de detección neuroelectrónica presentan una limitación en el producto entre la resolución espacial y el área de cobertura. Además, presentan una baja sensibilidad en la banda de frecuencia infra-lenta (<0.5Hz), que está relacionada con la conectividad funcional de largo alcance. En esta tesis se presenta una nueva tecnología basada en sensores activos de grafeno, que permite incrementar el área de cobertura y la resolución espacial de los registros electrofisiológicos conservando una alta sensibilidad en una amplia banda de frecuencia, desde la actividad infra-lenta hasta la de una sola célula electrogénica. Este desarrollo tecnológico se divide en tres etapas principales; en primer lugar, se obtiene una comprensión más profunda de las características intrínsecas del ruido y la respuesta en frecuencia de estos sensores basándose en el estado del arte en tecnología de sensores de grafeno. En la segunda etapa, se muestra un sistema cuasi-comercial basado en matrices de sensores de grafeno epi-cortical y transmisión inalámbrica para implantación crónica en ratas. Con este sistema, se demuestra la reproducibilidad de las matrices de sensores de grafeno, su estabilidad a largo plazo y su biocompatibilidad crónica. Además, se proporciona evidencia preliminar para una amplia gama de nuevos patrones electrofisiológicos debido a su sensibilidad en la banda de frecuencia infra-lenta. Finalmente, en la última etapa de esta tesis, el enfoque se centra en el desarrollo de nuevas estrategias de multiplexación para aumentar el número de sensores en las sondas neuronales. Estas tres etapas principales de desarrollo han llevado a la demostración del potencial de las matrices de sensores de grafeno multiplexados para el mapeado de las dinámicas neuronales a gran escala en una amplia banda de frecuencia en animales que se mueven libremente durante largos períodos. La combinación de estas capacidades hace que las matrices de sensores activos de grafeno sean una tecnología prometedora para interfaces cerebro-ordenador de alto ancho de banda y una herramienta única para investigar el papel de la actividad infra-lenta en la coordinación de las dinámicas neuronales de alta frecuencia.Brain function is based on highly complex processes, which remain yet to be described and understood in detail. In the last decades, neuroscience has experienced an accelerated development, prompted by novel neurotechnologies that allow monitoring the dynamics of electrical activity in the brain with a higher spatio-temporal resolution and wider coverage area. However, due to the high complexity of neural networks in the brain, which are composed of strongly interconnected neural populations across large brain regions, we are far from monitoring a significant fraction of neurons mediating complex functions. In order to investigate large-scale brain dynamics with high spatial resolution several technologies have been extensively used, including functional magnetic resonance imaging (fMRI), voltage-sensitive dye imaging or high sensor-count electrophysiological recordings. However, the temporal resolution of fMRI and optical methods is typically limited to few hertz, almost three orders of magnitude below that of action potentials, and are limited to head-fixed conditions. On the other hand, electrophysiological recordings based on micro-electrode arrays provide a high spatio-temporal resolution, allowing to accurately detect fast dynamics from hundreds of individual neurons simultaneously in freely moving animals. However, neuroelectronic sensing interfaces present a trade-off between spatial resolution and coverage area. Moreover, they present a poor sensitivity in the infra-slow frequency band ( $< 0.5$\ , $h z$) , which is related to long-range functional connectivity. In this thesis, a novel technology based on graphene active sensors is presented, which allows to increase the coverage area and spatial resolution of electrophysiological recordings while preserving a high sensitivity in a wide frequency band, from infra-slow to single electrogenic cell activity. This technological development is divided into three main stages; first, a deeper understanding of the intrinsic noise characteristics and frequency response of these sensors is obtained by building on prior graphene sensor technology. In the second stage, a quasi-commercial system based on epi-cortical graphene sensor arrays and a wireless headstage for chronic implantation in rats is shown. Using this system, the reproducibility of the graphene sensor arrays, their long-term stability and their chronic biocompatibility are demonstrated. Furthermore, preliminary evidence is provided for a wide range of novel electrophysiological patterns owing to their sensitivity in the infra-slow frequency band. Finally, in the last stage of this thesis, the focus is centred on the development of new multiplexing strategies to upscale the number of sensors on the neural probes. These three main development stages have led to the demonstration of the potential of multiplexed graphene sensor arrays for mapping of large-scale brain dynamics in a wide frequency band in freely moving animals over long periods. The combination of these capabilities makes graphene active sensor arrays a promising technology for high bandwidth brain computer interfaces and a unique tool to investigate the role of infra-slow activity on the coordination of higher frequency brain dynamics