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 µECoG 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- µm CMOS technology with 0.012 mm 2 and 36 µ 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 µECoG headstage with 16×32 and 32×32 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.

Switchless Multiplexing of Graphene Active Sensor Arrays for Brain Mapping.

Sensor arrays used to detect electrophysiological signals from the brain are paramount in neuroscience. However, the number of sensors that can be interfaced with macroscopic data acquisition systems currently limits their bandwidth. This bottleneck originates in the fact that, typically, sensors are addressed individually, requiring a connection for each of them. Herein, we present the concept of frequency-division multiplexing (FDM) of neural signals by graphene sensors. We demonstrate the high performance of graphene transistors as mixers to perform amplitude modulation (AM) of neural signals in situ, which is used to transmit multiple signals through a shared metal line. This technology eliminates the need for switches, remarkably simplifying the technical complexity of state-of-the-art multiplexed neural probes. Besides, the scalability of FDM graphene neural probes has been thoroughly evaluated and their sensitivity demonstrated in vivo. Using this technology, we envision a new generation of high-count conformal neural probes for high bandwidth brain machine interfaces.

Distortion-Free Sensing of Neural Activity Using Graphene Transistors.

Graphene solution-gated field-effect transistors (g-SGFETs) are promising sensing devices to transduce electrochemical potential signals in an electrolyte bath. However, distortion mechanisms in g-SGFET, which can affect signals of large amplitude or high frequency, have not been evaluated. Here, a detailed characterization and modeling of the harmonic distortion and non-ideal frequency response in g-SGFETs is presented. This accurate description of the input-output relation of the g-SGFETs allows to define the voltage- and frequency-dependent transfer functions, which can be used to correct distortions in the transduced signals. The effect of signal distortion and its subsequent calibration are shown for different types of electrophysiological signals, spanning from large amplitude and low frequency cortical spreading depression events to low amplitude and high frequency action potentials. The thorough description of the distortion mechanisms presented in this article demonstrates that g-SGFETs can be used as distortion-free signal transducers not only for neural sensing, but also for a broader range of applications in which g-SGFET sensors are used.

High-resolution mapping of infraslow cortical brain activity enabled by graphene microtransistors.

Recording infraslow brain signals (<0.1 Hz) with microelectrodes is severely hampered by current microelectrode materials, primarily due to limitations resulting from voltage drift and high electrode impedance. Hence, most recording systems include high-pass filters that solve saturation issues but come hand in hand with loss of physiological and pathological information. In this work, we use flexible epicortical and intracortical arrays of graphene solution-gated field-effect transistors (gSGFETs) to map cortical spreading depression in rats and demonstrate that gSGFETs are able to record, with high fidelity, infraslow signals together with signals in the typical local field potential bandwidth. The wide recording bandwidth results from the direct field-effect coupling of the active transistor, in contrast to standard passive electrodes, as well as from the electrochemical inertness of graphene. Taking advantage of such functionality, we envision broad applications of gSGFET technology for monitoring infraslow brain activity both in research and in the clinic.

Understanding the bias dependence of low frequency noise in single 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.

Quantum Size Effect in Organometal Halide Perovskite Nanoplatelets.

Organometal halide perovskites have recently emerged displaying a huge potential for not only photovoltaic, but also light emitting applications. Exploiting the optical properties of specifically tailored perovskite nanocrystals could greatly enhance the efficiency and functionality of applications based on this material. In this study, we investigate the quantum size effect in colloidal organometal halide perovskite nanoplatelets. By tuning the ratio of the organic cations used, we can control the thickness and consequently the photoluminescence emission of the platelets. Quantum mechanical calculations match well with the experimental values. We find that not only do the properties of the perovskite, but also those of the organic ligands play an important role. Stacking of nanoplatelets leads to the formation of minibands, further shifting the bandgap energies. In addition, we find a large exciton binding energy of up to several hundreds of meV for nanoplatelets thinner than three unit cells, partially counteracting the blueshift induced by quantum confinement. Understanding of the quantum size effects in perovskite nanoplatelets and the ability to tune them provide an additional method with which to manipulate the optical properties of organometal halide perovskites.