A Low-Power Opamp-Less Second-Order Delta-Sigma Modulator for Bioelectrical Signals in 0.18 µm CMOS.

This article reports on a compact and low-power CMOS readout circuit for bioelectrical signals based on a second-order delta-sigma modulator. The converter uses a voltage-controlled, oscillator-based quantizer, achieving second-order noise shaping with a single opamp-less integrator and minimal analog circuitry. A prototype has been implemented using 0.18 µm CMOS technology and includes two different variants of the same modulator topology. The main modulator has been optimized for low-noise, neural-action-potential detection in the 300 Hz-6 kHz band, with an input-referred noise of 5.0 µVrms, and occupies an area of 0.0045 mm2. An alternative configuration features a larger input stage to reduce low-frequency noise, achieving 8.7 µVrms in the 1 Hz-10 kHz band, and occupies an area of 0.006 mm2. The modulator is powered at 1.8 V with an estimated power consumption of 3.5 µW.

A VCO-Based CMOS Readout Circuit for Capacitive MEMS Microphones.

Microelectromechanical systems (MEMS) microphone sensors have significantly improved in the past years, while the readout electronic is mainly implemented using switched-capacitor technology. The development of new battery powered "always-on" applications increasingly requires a low power consumption. In this paper, we show a new readout circuit approach which is based on a mostly digital Sigma Delta ( Σ Δ ) analog-to-digital converter (ADC). The operating principle of the readout circuit consists of coupling the MEMS sensor to an impedance converter that modulates the frequency of a stacked-ring oscillator-a new voltage-controlled oscillator (VCO) circuit featuring a good trade-off between phase noise and power consumption. The frequency coded signal is then sampled and converted into a noise-shaped digital sequence by a time-to-digital converter (TDC). A time-efficient design methodology has been used to optimize the sensitivity of the oscillator combined with the phase noise induced by 1 / f and thermal noise. The circuit has been prototyped in a 130 nm CMOS process and directly bonded to a standard MEMS microphone. The proposed VCO-based analog-to-digital converter (VCO-ADC) has been characterized electrically and acoustically. The peak signal-to-noise and distortion ratio (SNDR) obtained from measurements is 77.9 dB-A and the dynamic range (DR) is 100 dB-A. The current consumption is 750 µ A at 1.8 V and the effective area is 0.12 mm 2 . This new readout circuit may represent an enabling advance for low-cost digital MEMS microphones.

SNDR Limits of Oscillator-Based Sensor Readout Circuits.

This paper analyzes the influence of phase noise and distortion on the performance of oscillator-based sensor data acquisition systems. Circuit noise inherent to the oscillator circuit manifests as phase noise and limits the SNR. Moreover, oscillator nonlinearity generates distortion for large input signals. Phase noise analysis of oscillators is well known in the literature, but the relationship between phase noise and the SNR of an oscillator-based sensor is not straightforward. This paper proposes a model to estimate the influence of phase noise in the performance of an oscillator-based system by reflecting the phase noise to the oscillator input. The proposed model is based on periodic steady-state analysis tools to predict the SNR of the oscillator. The accuracy of this model has been validated by both simulation and experiment in a 130 nm CMOS prototype. We also propose a method to estimate the SNDR and the dynamic range of an oscillator-based readout circuit that improves by more than one order of magnitude the simulation time compared to standard time domain simulations. This speed up enables the optimization and verification of this kind of systems with iterative algorithms.

A Time-Domain Readout Technique for Neural Interfaces Based on VCO-Timestamping.

CMOS neural interfaces are aimed at studying the electrical activity of neurons and may help to restore lost functions of the nervous system in the future. The central function of most neural interfaces is the detection of extracellular electrical potentials by means of numerous microelectrodes positioned in close vicinity to the neurons. Modern neural interfaces require compact low-power, low-noise readout circuits, capable of recording from thousands of electrodes simultaneously without excessive area consumption and heat dissipation. In this article, we propose a novel readout technique for neural interfaces. The readout is based on a voltage-controlled oscillator (VCO), the frequency of which is modulated by the input voltage. The novelty of this work lies in the postprocessing of the VCO output, which is based on generating digital timestamps that contain temporal information about the oscillation. This method is potentially advantageous, because it requires mostly digital circuitry, which is more scalable than analog circuitry. Furthermore, most of the digital circuitry required for VCO-timestamping can be shared among several VCOs, rendering the architecture efficient for multi-channel architectures. This article introduces the VCO-timestamping concept, including theoretical derivations and simulations, and presents measurements of a prototype fabricated in 0.18-µm CMOS technology. The measured input-referred noise in the 300 Hz-5 kHz band was 5.7 µVrms, and the prototype was able to detect pre-recorded extracellular action potentials.

3D In Vitro Blood-Brain-Barrier Model for Investigating Barrier Insults.

Blood-brain-barrier (BBB) disruption has been associated with a variety of central-nervous-system diseases. In vitro BBB models enable to investigate how the barrier reacts to external injury events, commonly referred to as insults. Here, a human-cell-based BBB platform with integrated, transparent electrodes to monitor barrier tightness in real time at high resolution is presented. The BBB model includes human cerebral endothelial cells and primary pericytes and astrocytes in a 3D arrangement within a pump-free, open-microfluidic platform. With this platform, this study demonstrates that oxygen-glucose deprivation (OGD), which mimics the characteristics of an ischemic insult, induces a rapid remodeling of the cellular actin structures and subsequent morphological changes in the endothelial cells. High-resolution live imaging shows the formation of large actin stress-fiber bundles in the endothelial layer during OGD application, which ultimately leads to cell shrinkage and barrier breakage. Simultaneous electrical measurements evidence a rapid decrease of the barrier electrical resistance before the appearance of stress fibers, which indicates that the barrier function is compromised already before the appearance of drastic morphological changes. The results demonstrate that the BBB platform recapitulates the main barrier functions in vitro and can be used to investigate rapid reorganization of the BBB upon application of external stimuli.

Impedance Imaging of Cells and Tissues: Design and Applications.

Due to their label-free and noninvasive nature, impedance measurements have attracted increasing interest in biological research. Advances in microfabrication and integrated-circuit technology have opened a route to using large-scale microelectrode arrays for real-time, high-spatiotemporal-resolution impedance measurements of biological samples. In this review, we discuss different methods and applications of measuring impedance for cell and tissue analysis with a focus on impedance imaging with microelectrode arrays in in vitro applications. We first introduce how electrode configurations and the frequency range of the impedance analysis determine the information that can be extracted. We then delve into relevant circuit topologies that can be used to implement impedance measurements and their characteristic features, such as resolution and data-acquisition time. Afterwards, we detail design considerations for the implementation of new impedance-imaging devices. We conclude by discussing future fields of application of impedance imaging in biomedical research, in particular applications where optical imaging is not possible, such as monitoring of ex vivo tissue slices or microelectrode-based brain implants.

Circuit-Based Design of Microfluidic Drop Networks.

Microfluidic-drop networks consist of several stable drops-interconnected through microfluidic channels-in which organ models can be cultured long-term. Drop networks feature a versatile configuration and an air-liquid interface (ALI). This ALI provides ample oxygenation, rapid liquid turnover, passive degassing, and liquid-phase stability through capillary pressure. Mathematical modeling, e.g., by using computational fluid dynamics (CFD), is a powerful tool to design drop-based microfluidic devices and to optimize their operation. Although CFD is the most rigorous technique to model flow, it falls short in terms of computational efficiency. Alternatively, the hydraulic-electric analogy is an efficient "first-pass" method to explore the design and operation parameter space of microfluidic-drop networks. However, there are no direct electric analogs to a drop, due to the nonlinear nature of the capillary pressure of the ALI. Here, we present a circuit-based model of hanging- and standing-drop compartments. We show a phase diagram describing the nonlinearity of the capillary pressure of a hanging drop. This diagram explains how to experimentally ensure drop stability. We present a methodology to find flow rates and pressures within drop networks. Finally, we review several applications, where the method, outlined in this paper, was instrumental in optimizing design and operation.