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Neural signal recording and optical stimulation using implantable devices have become a ubiquitous method to treat brain disorders, yet there lie some shortcomings, such as size, weight, and functionalities of the implants. This work presents a commercial off-the-shelf (COTS) component-based miniaturized wireless optogenetic headstage with simultaneous optical stimulation and electrophysiological recording for freely moving rats. The system includes a battery-based neural stimulator consisting of a low-dropout (LDO) regulator, an oscillator, and a μ LED. The electrophysiological signal recording system includes an intracortical neural probe implemented on a shape memory polymer (SMP) substrate, an array of neural amplifiers with an integrated analog-to-digital converter (ADC), a transceiver IC, and a ceramic antenna. A digital sub-1-GHz transceiver integrated with a low-power microcontroller (MCU) is used to transmit the acquired neural data to a remote receiver unit, followed by offline spike detection and sorting in LabVIEW. The front-end recording amplifiers provide a gain of 45.7 dB with the input-referred noise of 2.4μVrms . The integrated multiplexer (MUX) with the ADC allows sampling of the amplified voltage at a configurable sampling rate of 160–480 kSamples/s. The total power consumption of the stimulation and the recording system is 23 mW. The dimension of the headstage device is 13.5×21.3 mm, weighing 4 g without the battery. The system is experimentally validated in an in vivo setting by placing the headstage on the head of a male rat and recording the neural signals from the ventral tegmental area (VTA) of the brain. This integrative neural signal recording and spike sorting approach would be useful for the development of a closed-loop neuromodulation system. 

Abstract This paper presents a motion-sensing device with the capability of harvesting energy from low-frequency motion activities. Based on the high surface area reverse electrowetting-on-dielectric (REWOD) energy harvesting technique, mechanical modulation of the liquid generates an AC signal, which is modeled analytically and implemented in Matlab and COMSOL. A constant DC voltage is produced by using a rectifier and a DC–DC converter to power up the motion-sensing read-out circuit. A charge amplifier converts the generated charge into a proportional output voltage, which is transmitted wirelessly to a remote receiver. The harvested DC voltage after the rectifier and DC–DC converter is found to be 3.3 V, having a measured power conversion efficiency (PCE) of the rectifier as high as 40.26% at 5 Hz frequency. The energy harvester demonstrates a linear relationship between the frequency of motion and the generated output power, making it highly suitable as a self-powered wearable motion sensor. 

This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system. 

Multi-channel data acquisition of bio-signals is a promising technology that is being used in many fields these days. Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as an electrocardiogram (ECG) and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increase, data volume is increased resulting in impermissible transmitting power. This paper presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. The power consumed by the bio-signal amplifier block is 2.35 μW. The SAR-ADC block that is designed to digitize the amplified signal consumes 277 μW of power and the power consumption value of the pseudo-random bit sequence generator (PRBS) is 344.2μW. The sampling rate of PRBS block is 611.76 Kbps. We have considered collecting neural data from the 32 channels, and achieved an 8.5X compression rate. The low power consumption per channel confirms the importance of the proposed approach for multiple channel high-density neural interfaces. 

This paper presents a fully reconfigurable readout circuit including a chopper-stabilized neural amplifier and a successive approximation register (SAR) analog-to-digital converter (ADC) for neural signal recording applications. Since the target neural signals - action potentials (APs) and local field potentials (LFPs) differ in the peak amplitude while occupying different frequency bandwidths, gain, and bandwidth reconfigurability would be advantageous in improving power and noise performance. The readout circuit is designed in 180 nm standard CMOS technology. It achieves the mid-band gain of 50.3 dB in the frequency band of 0.1 Hz - 250 Hz to detect the LFPs, and 63.4 dB in 267 Hz - 20.8 kHz for detecting the APs. The neural amplifier consumes a total power of 1.54 μW and 1.94 μW for LFP and AP configurations, respectively. The input-referred noises have been achieved as 0.97 μV rms (0.1 Hz - 250 Hz), and 0.44 μV rms (250 Hz - 5 kHz), leading to a noise efficiency factor (NEF) of 1.27 and 1.21, for the two configurations, respectively. It rejects the generated large DC offset up to 40 mV at the electrode-tissue interface, by implementing a DC servo loop (DSL). The offset voltage with the DSL becomes 0.23 mV, which is acceptable for the neural experiments. Enabling the impedance boosting loop, the DC input impedance is found to be within the range of 1.77 - 2.27 GΩ, introducing the reconfigurability in impedance for matching with the electrode impedance. The SAR-ADC having a varying sampling frequency ranging from 10 - 40 ksamples/s demonstrates to digitize the APs and the LFPs with the resolution from 8 - 10 bits. The entire AFE provides good compatibility to record the neural signal while lowering the large DC offset down to 0.23 mV. 

This paper presents a motion-sensing device with the capability of harvesting energy from low-frequency motion activities that can be utilized for long-term human health monitoring. The energy harvester used in the proposed motion sensor is based on the mechanical modulation of liquid on an insulated electrode, which utilizes a technique referred to as reverse electrowetting-on-dielectric (REWOD). The generated AC signal from the REWOD is rectified to a DC voltage using a Schottky diode-based rectifier and boosted subsequently with the help of a linear charge-pump circuit and a low-dropout regulator (LDO). The constant DC voltage from the LDO (1.8 V) powers the motion-sensing read-out circuitry, which converts the generated charge into a proportional output voltage using a charge amplifier. After amplification of the motion data, a 5-bit SAR-ADC (successive-approximation register ADC) digitizes the signal to be transmitted to a remote receiver. Both the CMOS energy harvester circuit including the rectifier, the charge-pump circuit, the LDO, and the read-out circuit including the charge amplifier, and the ADC is designed in the standard 180 nm CMOS technology. The amplified amplitude goes up to 1.76 V at 10 Hz motion frequency, following linearity with respect to the frequency. The generated DC voltage from the REWOD after the rectifier and the charge-pump is found to be 2.4 V, having the voltage conversion ratio (VCR) as 32.65% at 10 Hz of motion frequency. The power conversion efficiency (PCE) of the rectifier is simulated as high as 68.57% at 10 Hz. The LDO provides the power supply voltage of 1.8 V to the read-out circuit. The energy harvester demonstrates a linear relationship between the frequency of motion and the generated output power, making it suitable as a self-powered wearable motion sensor. 

Abstract Increasing demand for self-powered wearable sensors has spurred an urgent need to develop energy harvesting systems that can reliably and sufficiently power these devices. Within the last decade, reverse electrowetting-on-dielectric (REWOD)-based mechanical motion energy harvesting has been developed, where an electrolyte is modulated (repeatedly squeezed) between two dissimilar electrodes under an externally applied mechanical force to generate an AC current. In this work, we explored various combinations of electrolyte concentrations, dielectrics, and dielectric thicknesses to generate maximum output power employing REWOD energy harvester. With the objective of implementing a fully self-powered wearable sensor, a “zero applied-bias-voltage” approach was adopted. Three different concentrations of sodium chloride aqueous solutions (NaCl-0.1 M, NaCl-0.5 M, and NaCl-1.0 M) were used as electrolytes. Likewise, electrodes were fabricated with three different dielectric thicknesses (100 nm, 150 nm, and 200 nm) of Al₂O₃and SiO₂with an additional layer of CYTOP for surface hydrophobicity. The REWOD energy harvester and its electrode–electrolyte layers were modeled using lumped components that include a resistor, a capacitor, and a current source representing the harvester. Without using any external bias voltage, AC current generation with a power density of 53.3 nW/cm²was demonstrated at an external excitation frequency of 3 Hz with an optimal external load. The experimental results were analytically verified using the derived theoretical model. Superior performance of the harvester in terms of the figure-of-merit comparing previously reported works is demonstrated. The novelty of this work lies in the combination of an analytical modeling method and experimental validation that together can be used to increase the REWOD harvested power extensively without requiring any external bias voltage. 

Monitoring human health in real-time using wearable and implantable electronics (WIE) has become one of the most promising and rapidly growing technologies in the healthcare industry. In general, these electronics are powered by batteries that require periodic replacement and maintenance over their lifetime. To prolong the operation of these electronics, they should have zero post-installation maintenance. On this front, various energy harvesting technologies to generate electrical energy from ambient energy sources have been researched. Many energy harvesters currently available are limited by their power output and energy densities. With the miniaturization of wearable and implantable electronics, the size of the harvesters must be miniaturized accordingly in order to increase the energy density of the harvesters. Additionally, many of the energy harvesters also suffer from limited operational parameters such as resonance frequency and variable input signals. In this work, low frequency motion energy harvesting based on reverse electrowetting-ondielectric (REWOD) is examined using perforated high surface area electrodes with 38 µm pore diameters. Total available surface area per planar area was 8.36 cm2 showing a significant surface area enhancement from planar to porous electrodes and proportional increase in AC voltage density from our previous work. In REWOD energy harvesting, high surface area electrodes significantly increase the capacitance and hence the power density. An AC peak-to-peak voltage generation from the electrode in the range from 1.57-3.32 V for the given frequency range of 1-5 Hz with 0.5 Hz step is demonstrated. In addition, the unconditioned power generated from the harvester is converted to a DC power using a commercial off-theshelf Schottky diode-based voltage multiplier and low dropout regulator (LDO) such that the sensors that use this technology could be fully self-powered. The produced charge is then converted to a proportional voltage by using a commercial charge amplifier to record the features of the motion activities. A transceiver radio is also used to transmit the digitized data from the amplifier and the built-in analog-to-digital converter (ADC) in the micro-controller. This paper proposes the energy harvester acting as a self-powered motion sensor for different physical activities for wearable and wireless healthcare devices. 

This paper presents a reverse electrowetting-on-dielectric (REWOD) energy harvester integrated with rectifier, boost converter, and charge amplifier that is, without bias voltage, capable of powering wearable sensors for monitoring human health in real-time. REWOD has been demonstrated to effectively generate electrical current at a low frequency range (< 3 Hz), which is the frequency range for various human activities such as walking, running, etc. However, the current generated from the REWOD without external bias source is insufficient to power such motion sensors. In this work, to eventually implement a fully self-powered motion sensor, we demonstrate a novel bias-free REWOD AC generation and then rectify, boost, and amplify the signal using commercial components. The unconditioned REWOD output of 95–240 mV AC is generated using a 50 μL droplet of 0.5M NaCl electrolyte and 2.5 mm of electrode displacement from an oscillation frequency range of 1–3 Hz. A seven-stage rectifier using Schottky diodes having a forward voltage drop of 135–240 mV and a forward current of 1 mA converts the generated AC signal to DC voltage. ∼3 V DC is measured at the boost converter output, proving the system could function as a self-powered motion sensor. Additionally, a linear relationship of output DC voltage with respect to frequency and displacement demonstrates the potential of this REWOD energy harvester to function as a self-powered wearable motion sensor.