This paper describes an ultra-low power SAR ADC for medical implant devices. To achieve the nano-watt range power consumption, an ultra-low power design strategy has been utilized, imposing maximum simplicity on the ADC architecture, low transistor count and matched capacitive DAC with a switching scheme which results in full-range sampling without switch bootstrapping and extra reset voltage. Furthermore, a dual-supply voltage scheme allows the SAR logic to operate at 0.4 V, reducing the overall power consumption of the ADC by 15% without any loss in performance. The ADC was fabricated in 0.13-μm CMOS. In dual-supply mode (1.0 V for analog and 0.4 V for digital), the ADC consumes 53 nW at a sampling rate of 1 kS/s and achieves the ENOB of 9.1 bits. The leakage power constitutes 25% of the 53-nW total power.

/pdf/a-53-nw-9-1-enob-1-ks-s-sar-adc-in-0-13-mu-m-cmos-for-49q2yef4do.pdf

A 53-nW 9.1-ENOB 1-kS/s SAR ADC in 0.13- $\mu$ m CMOS for Medical Implant Devices

To advance our understanding of the functioning of neuronal ensembles, systems are needed to enable simultaneous recording from a large number of individual neurons at high spatiotemporal resolution and good signal-to-noise ratio. Moreover, stimulation capability is highly desirable for investigating, for example, plasticity and learning processes. Here, we present a microelectrode array (MEA) system on a single CMOS die for in vitro recording and stimulation. The system incorporates 26,400 platinum electrodes, fabricated by in-house post-processing, over a large sensing area (3.85 × 2.10 mm2) with sub-cellular spatial resolution (pitch of 17.5 μm). Owing to an area and power efficient implementation, we were able to integrate 1024 readout channels on chip to record extracellular signals from a user-specified selection of electrodes. These channels feature noise values of 2.4 μVrms in the action-potential band (300 Hz-10 kHz) and 5.4 μVrms in the local-field-potential band (1 Hz-300 Hz), and provide programmable gain (up to 78 dB) to accommodate various biological preparations. Amplified and filtered signals are digitized by 10 bit parallel single-slope ADCs at 20 kSamples/s. The system also includes 32 stimulation units, which can elicit neural spikes through either current or voltage pulses. The chip consumes only 75 mW in total, which obviates the need of active cooling even for sensitive cell cultures.

/pdf/a-1024-channel-cmos-microelectrode-array-with-26-400-4firlhykn3.pdf

A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro

An implantable real-time blood pressure monitoring microsystem for laboratory mice has been demonstrated. The system achieves a 10-bit blood pressure sensing resolution and can wirelessly transmit the pressure information to an external unit. The implantable device is operated in a batteryless manner, powered by an external RF power source. The received RF power level can be sensed and wirelessly transmitted along with blood pressure signal for feedback control of the external RF power. The microsystem employs an instrumented silicone cuff, wrapped around a blood vessel with a diameter of approximately 200 ?m, for blood pressure monitoring. The cuff is filled by low-viscosity silicone oil with an immersed MEMS capacitive pressure sensor and integrated electronic system to detect a down-scaled vessel blood pressure waveform with a scaling factor of approximately 0.1. The integrated electronic system, consisting of a capacitance-to-voltage converter, an 11-bit ADC, an adaptive RF powering system, an oscillator-based 433 MHz FSK transmitter and digital control circuitry, is fabricated in a 1.5 ?m CMOS process and dissipates a power of 300 ?W. The packaged microsystem weighs 130 milligram and achieves a capacitive sensing resolution of 75 aF over 1 kHz bandwidth, equivalent to a pressure sensing resolution of 1 mmHg inside an animal vessel, with a dynamic range of 60 dB. Untethered laboratory animal in vivo evaluation demonstrates that the microsystem can capture real-time blood pressure information with a high fidelity under an adaptive RF powering and wireless data telemetry condition.

A Wireless and Batteryless 10-Bit Implantable Blood Pressure Sensing Microsystem With Adaptive RF Powering for Real-Time Laboratory Mice Monitoring

The design of an analog-to-digital converter or digital-to-analog converter is one of the most fascinating tasks in micro-electronics. In a converter the analog world with all its intricacies meets the realm of the formal digital abstraction. Both disciplines must be understood for an optimum conversion solution. In a converter also system challenges meet technology opportunities. Modern systems rely on analog-to-digital converters as an essential part of the complex chain to access the physical world. And processors need the ultimate performance of digital-to-analog converters to present the results of their complex algorithms. The same progress in CMOS technology that enables these VLSI digital systems creates new challenges for analog-to-digital converters: lower signal swings, less power and variability issues. Last but not least, the analog-to-digital converter must follow the cost reduction trend. These changing boundary conditions require micro-electronics engineers to consider their design choices for every new design. Analog-to-Digital Conversion discusses the different analog-to-digital conversion principles: sampling, quantization, reference generation, nyquist architectures and sigma-delta modulation. Analog-to-Digital Conversion presents an overview of the state-of-the-art in this field and focuses on issues of optimizing accuracy and speed while reducing the power level. A lot of background knowledge and practical tips complement the discussion of basic principles, which makes Analog-to-Digital Conversion also a reference for the experienced engineer.

Analog-to-Digital Conversion

A 64-channel 0.13- $\mu \text{m}$ CMOS system on a chip (SoC) for neuroelectrical monitoring and responsive neurostimulation is presented. The $\Delta \Sigma $ -based neural channel records signals with rail-to-rail dc offset at the input without any area-intensive dc-removing passive components, which leads to a compact 0.013-mm2 integration area of recording and stimulation circuits. The channel consumes 630 nW, yields a signal to noise and distortion ratio of 72.2 dB, a 1.13- $\mu $ Vrms integrated input-referred noise over 0.1–500 Hz frequency range, and a noise efficiency factor of 2.86. Analog multipliers are implemented in each channel with minimum additional area cost by reusing the multi-bit current-digital to analog converter that is originally placed for current-mode stimulation. The multipliers are used for compact implementation of bandpass finite impulse response filters, as well as voltage gain scaling. A tri-core low-power DSP conducts phase-synchrony-based neurophysiological event detection and triggers a subset of 64 programmable arbitrary-waveform current-mode stimulators for subsequent neuromodulation. Two ultra-wideband (UWB) wireless transmitters communicate to receivers located at 10 cm to 2 m distance from the implanted SoC with data rates of 10–46 Mb/s, respectively. An inductive link that operates at 1.5 MHz provides power to the SoC and is also used to communicate commands to an on-chip ASK receiver. The chip occupies 6 mm2 while consuming 1.07 and 5.44 mW with delay-based and voltage controlled oscillator-based UWB transmitters, respectively. The SoC is validated in vivo using epilepsy monitoring (seizure detection) and treatment (seizure suppression) experiments.

Rail-to-Rail-Input Dual-Radio 64-Channel Closed-Loop Neurostimulator

Thermal noise represents a major limitation on the performance of most electronic circuits. It is particularly important in switched circuits, such as the switched-capacitor (SC) filters widely used in mixed-mode CMOS integrated circuits. In these circuits, switching introduces a boost in the power spectral density of the thermal noise due to aliasing. Unfortunately, even though the theory of noise in SC circuits is discussed in the literature, it is very intricate. The numerical calculation of noise in switched circuits is very tedious, and requires highly sophisticated and not widely available software. The purpose of this paper is twofold. It provides a tutorial description of the physical phenomena taking place in an SC circuit while it processes noise (Sections II-III). It also proposes some specialized but highly efficient algorithms for estimating the resulting sampled noise in SC circuits, which need only simple calculations (Sections IV-VI ). A practical design procedure, which follows directly from the estimate, is also described. The accuracy of the proposed estimation algorithms is verified by simulation using SpectreRF. As an example, it is applied to the estimation of the total thermal noise in a second-order low-distortion delta-sigma converter.

J. Silva

Papers

Design-oriented estimation of thermal noise in switched-capacitor circuits