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  • Conference paper
    Leene L, Maslik M, Feng P, Szostak K, Mazza F, Constandinou TGet al., 2018,

    Autonomous SoC for neural local field potential recording in mm-scale wireless implants

    , IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 1-5, ISSN: 2379-447X

    Next generation brain machine interfaces fundamentally need to improve the information transfer rate and chronic consistency when observing neural activity over a long period of time. Towards this aim, this paper presents a novel System-on-Chip (SoC) for a mm-scale wireless neural recording node that can be implanted in a distributed fashion. The proposed self-regulating architecture allows each implant to operate autonomously and adaptively load the electromagnetic field to extract a precise amount of power for full-system operation. This can allow for a large number of recording sites across multiple implants extending through cortical regions without increased control overhead in the external head-stage. By observing local field potentials (LFPs) only, chronic stability is improved and good coverage is achieved whilst reducing the spatial density of recording sites. The system features a ΔΣ based instrumentation circuit that digitises high fidelity signal features at the sensor interface thereby minimising analogue resource requirements while maintaining exceptional noise efficiency. This has been implemented in a 0.35 μm CMOS technology allowing for wafer-scale post-processing for integration of electrodes, RF coil, electronics and packaging within a 3D structure. The presented configuration will record LFPs from 8 electrodes with a 825 Hz bandwidth and an input referred noise figure of 1.77μVrms. The resulting electronics has a core area of 2.1 mm2 and a power budget of 92 μW

  • Conference paper
    Luo JW, Firfilionis D, Ramezani R, Dehkhoda F, Soltan A, Degenaar P, Liu Y, Constandinou Tet al., 2018,

    Live demonstration: A closed-loop cortical brain implant for optogenetic curing epilepsy

    A closed-loop optogenetic system for curing epilepsy is presented in this work. As it shown at figure 1, the system consists of a cortical brain implant with LEDs and recording electrodes, a customer designed CMOS chip[1][2][3] and a controller. The brain activities are recorded by the implant with recording electronics in a CMOS chip, the signals are processed by the controller, and the results are send back to the CMOS chip for delivering LED stimulation commands.

  • Conference paper
    Mifsud A, Haci D, Ghoreishizadeh SS, Liu Y, Constandinou TGet al., 2018,

    Adaptive power regulation and data delivery for multi-module implants

    , Pages: 1-4

    Emerging applications for implantable devices are requiring multi-unit systems with intrabody transmission of power and data through wireline interfaces. This paper proposes a novel method for power delivery within such a configuration that makes use of closed loop dynamic regulation. This is implemented for an implantable application requiring a single master and multiple identical slave devices utilising a parallel-connected 4-wire interface. The power regulation is achieved within the master unit through closed loop monitoring of the current consumption to the wired link. Simultaneous power transfer and full-duplex data communication is achieved by superimposing the power carrier and downlink data over two wires and uplink data over a second pair of wires. Measured results using a fully isolated (AC coupled) 4-wire lead, demonstrate this implementation can transmit up to 120 mW of power at 6 V (at the slave device, after eliminating any losses). The master device has a maximum efficiency of 80 % including a dominant dynamic power loss. A 6 V constant supply at the slave device is recovered 1.5 ms after a step of 22 mA.

  • Conference paper
    Saiz Alia M, Askari A, Forte AE, Reichenbach JDTet al., 2018,

    A model of the human auditory brainstem response to running speech

    , ARO 2018
  • Conference paper
    Kegler M, Etard OE, Forte AE, Reichenbach JDTet al., 2018,

    Complex Statistical Model for Detecting the Auditory Brainstem Response to Natural Speech and for Decoding Attention from High-Density EEG Recordings

    , ARO 2018
  • Conference paper
    Forte AE, Etard OE, Reichenbach JDT, 2018,

    Selective Auditory Attention At The Brainstem Level

    , ARO 2018
  • Patent
    Williams I, Rapeaux A, Luan S, Constandinou TGet al., 2018,

    Waveform Generator

  • Journal article
    Etard OE, Kegler M, Braiman C, Forte AE, Reichenbach JDTet al., 2018,

    Real-time decoding of selective attention from the human auditory brainstem response to continuous speech

    , BioRxiv
  • Journal article
    Reichenbach JDT, Ciganovic N, Warren R, Keceli B, Jacon S, Fridberger Aet al., 2018,

    Static length changes of cochlear outer hair cells can tune low-frequency hearing

    , PLoS Computational Biology, Vol: 14, ISSN: 1553-734X

    The cochlea not only transduces sound-induced vibration into neural spikes, it also amplifiesweak sound to boost its detection. Actuators of this active process are sensory outer haircells in the organ of Corti, whereas the inner hair cells transduce the resulting motion intoelectric signals that propagate via the auditory nerve to the brain. However, how the outerhair cells modulate the stimulus to the inner hair cells remains unclear. Here, we combinetheoretical modeling and experimental measurements near the cochlear apex to study theway in which length changes of the outer hair cells deform the organ of Corti. We develop ageometry-based kinematic model of the apical organ of Corti that reproduces salient, yetcounter-intuitive features of the organ’s motion. Our analysis further uncovers a mechanismby which a static length change of the outer hair cells can sensitively tune the signal transmittedto the sensory inner hair cells. When the outer hair cells are in an elongated state,stimulation of inner hair cells is largely inhibited, whereas outer hair cell contraction leads toa substantial enhancement of sound-evoked motion near the hair bundles. This novel mechanismfor regulating the sensitivity of the hearing organ applies to the low frequencies thatare most important for the perception of speech and music. We suggest that the proposedmechanism might underlie frequency discrimination at low auditory frequencies, as well asour ability to selectively attend auditory signals in noisy surroundings.

  • Journal article
    Liu Y, Pereira J, Constandinou TG, 2018,

    Event-driven processing for hardware-efficient neural spike sorting

    , Journal of Neural Engineering, Vol: 15, Pages: 1-14, ISSN: 1741-2552

    Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

  • Book chapter
    Williams I, Leene L, Constandinou TG, 2018,

    Next Generation Neural Interface Electronics

    , Circuit Design Considerations for Implantable Devices, Editors: Cong, Publisher: River Publishers, Pages: 141-178, ISBN: 978-87-93519-86-2
  • Patent
    Cavuto ML, Winter AG, Constandinou T, 2018,

    Apparatus and Method for Inserting Electrode-based Probes into Biological Tissue

  • Journal article
    Liu Y, Luan S, Williams I, Rapeaux A, Constandinou TGet al., 2017,

    A 64-Channel Versatile Neural Recording SoC with Activity Dependant Data Throughput

    , IEEE Transactions on Biomedical Circuits and Systems, Vol: 11, Pages: 1344-1355, ISSN: 1932-4545

    Modern microtechnology is enabling the channel count of neural recording integrated circuits to scale exponentially. However, the raw data bandwidth of these systems is increasing proportionately, presenting major challenges in terms of power consumption and data transmission (especially for wireless systems). This paper presents a system that exploits the sparse nature of neural signals to address these challenges and provides a reconfigurable low-bandwidth event-driven output. Specifically, we present a novel 64-channel low noise (2.1μVrms, low power (23μW per analogue channel) neural recording system-on-chip (SoC). This features individually-configurable channels, 10-bit analogue-to-digital conversion, digital filtering, spike detection, and an event-driven output. Each channel's gain, bandwidth & sampling rate settings can be independently configured to extract Local Field Potentials (LFPs) at a low data-rate and/or Action Potentials (APs) at a higher data rate. The sampled data is streamed through an SRAM buffer that supports additional on-chip processing such as digital filtering and spike detection. Real-time spike detection can achieve ~2 orders of magnitude data reduction, by using a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The SoC additionally features a latency-encoded asynchronous output that is critical if used as part of a closed-loop system. This has been specifically developed to complement a separate on-node spike sorting co-processor to provide a real-time (low latency) output. The system has been implemented in a commercially-available 0.35μm CMOS technology occupying a silicon area of 19.1mm² (0.3mm² gross per channel), demonstrating a low power & efficient architecture which could be further optimised by aggressive technology and supply voltage scaling.

  • Journal article
    Nicola W, Clopath C, 2017,

    Supervised Learning in Spiking Neural Networks with FORCE Training

    , Nature Communications, Vol: 8, ISSN: 2041-1723

    Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

  • Journal article
    Szostak K, Grand L, Constandinou TG, 2017,

    Neural interfaces for intracortical recording: requirements, fabrication methods, and characteristics

    , Frontiers in Neuroscience, Vol: 11, ISSN: 1662-4548

    Implantable neural interfaces for central nervous system research have been designed with wire, polymer or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes- microwire, micromachined and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  • Journal article
    Leene L, Constandinou TG, 2017,

    Time domain processing techniques using ring oscillator-based filter structures

    , IEEE Transactions on Circuits and Systems Part 1: Regular Papers, Vol: 64, Pages: 3003-3012, ISSN: 1549-8328

    The ability to process time-encoded signals with high fidelity is becoming increasingly important for the time domain (TD) circuit techniques that are used at the advanced nanometer technology nodes. This paper proposes a compact oscillator-based subsystem that performs precise filtering of asynchronous pulse-width modulation encoded signals and makes extensive use of digital logic, enabling low-voltage operation. First- and second-order primitives are introduced that can be used as TD memory or to enable analogue filtering of TD signals. These structures can be modeled precisely to realize more advanced linear or nonlinear functionality using an ensemble of units. This paper presents the measured results of a prototype fabricated using a 65-nm CMOS technology to realize a fourth- order low-pass Butterworth filter. The system utilizes a 0.5-V supply voltage with asynchronous digital control for closed-loop operation to achieve a 73-nW power budget. The implemented filter achieves a maximum signal to noise and distortion ratio of 53 dB with a narrow 5-kHz bandwidth resulting in an figure- of-merit of 8.2 fJ/pole. With this circuit occupying a compact 0.004-mm2 silicon footprint, this technique promises a substantial reduction in size over conventional Gm-C filters, whilst addition- ally offering direct integration with digital systems.

  • Patent
    Constandinou TG, Jackson A, 2017,

    Implantable Neural Interface

    A neural interface arrangement comprising: a plurality of probes for subdural implantation into or onto a human brain, each probe including at least one sensing electrode, a coil for receiving power via inductive coupling, signal processing circuitry coupled to the sensing electrode(s), and means for wirelessly transmitting data-carrying signals arising from the sensing electrode(s); an array of coils for implantation above the dura, beneath the skull, the array of coils being for inductively coupling with the coil of each of the plurality of probes, for transmitting power to the probes; and a primary (e.g. subcutaneous) coil connected to the array of coils, the primary coil being for inductively coupling with an external transmitter device, for receiving power from the external transmitter device; wherein, in use, the primary coil is operable to receive power from the external transmitter device by inductive coupling and to cause the array of coils to transmit power to the plurality of probes by inductive coupling; and wherein, in use, the plurality of probes are operable to wirelessly transmit data-carrying signals arising from the sensing electrodes.

  • Journal article
    Farina D, Castronovo AM, Vujaklija I, Sturma A, Salminger S, Hofer C, Aszmann OCet al., 2017,

    Common synaptic input to motor neurons and neural drive to targeted reinnervated muscles

    , Journal of Neuroscience, Vol: 37, Pages: 11285-11292, ISSN: 0270-6474

    e compared the behavior of motor neurons innervating their physiological muscle targets with motor neurons from the same spinal segment whose axons were surgically redirected to remnant muscles (targeted muscle reinnervation). The objective was to assess whether motor neurons with nonphysiological innervation receive similar synaptic input and could be voluntary controlled as motor neurons with natural innervation. For this purpose, we acquired high-density EMG signals from the biceps brachii in 5 male transhumeral amputees who underwent targeted reinnervation of this muscle by the ulnar nerve and from the first dorsal interosseous muscle of 5 healthy individuals to investigate the natural innervation of the ulnar nerve. The same recordings were also performed from the biceps brachii muscle of additional 5 able-bodied individuals. The EMG signals were decomposed into discharges of motor unit action potentials. Motor neurons were progressively recruited for the full range of submaximal muscle activation in all conditions. Moreover, their discharge rate significantly increased from recruitment to target activation level in a similar way across the subject groups. Motor neurons across all subject groups received common synaptic input as identified by coherence analysis of their spike trains. However, the relative strength of common input in both the delta (0.5–5 Hz) and alpha (5–13 Hz) bands was significantly smaller for the surgically reinnervated motor neuron pool with respect to the corresponding physiologically innervated one. The results support the novel approach of motor neuron interfacing for prosthesis control and provide new insights into the role of afferent input on motor neuron activity. SIGNIFICANCE STATEMENT Targeted muscle reinnervation surgically redirects nerves that lost their target in the amputation into redundant muscles in the region of the stump. The study of the behavior of motor neurons following this surgery is needed for designin

  • Journal article
    Cayco-Gajic NA, Clopath C, Silver RA, 2017,

    Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks

    , Nature Communications, Vol: 8, ISSN: 2041-1723

    Pattern separation is a fundamental function of the brain. The divergent feedforward networks thought to underlie this computation are widespread, yet exhibit remarkably similar sparse synaptic connectivity. Marr-Albus theory postulates that such networks separate overlapping activity patterns by mapping them onto larger numbers of sparsely active neurons. But spatial correlations in synaptic input and those introduced by network connectivity are likely to compromise performance. To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the input or output patterns. Our results show that sparse synaptic connectivity is essential for separating spatially correlated input patterns over a wide range of network activity, and that expansion and correlations, rather than sparse activity, are the major determinants of pattern separation.

  • Conference paper
    Mifsud A, Haci D, Ghoreishizadeh S, Liu Y, Constandinou TGet al., 2017,

    Adaptive Power Regulation and Data Delivery for Multi-Module Implants

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 584-587
  • Conference paper
    Feng P, Constandinou TG, Yeon P, Ghovanloo Met al., 2017,

    Millimeter-Scale Integrated and Wirewound Coils for Powering Implantable Neural Microsystems

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 488-491
  • Conference paper
    Szostak K, Mazza F, Maslik M, Feng P, Leene L, Constandinou TGet al., 2017,

    Microwire-CMOS Integration of mm-Scale Neural Probes for Chronic Local Field Potential Recording

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 492-495
  • Conference paper
    De Marcellis A, Palange E, Faccio M, Stanchieri GDP, Constandinou TGet al., 2017,

    A 250Mbps 24pJ/bit UWB-inspired Optical Communication System for Bioimplants

    , Turin, Italy, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 132-135
  • Conference paper
    Luo J, Firfilionis D, Ramezani R, Dehkhoda F, Soltan A, Degenaar P, Liu Y, Constandinou TGet al., 2017,

    Live demonstration: a closed-loop cortical brain implant for optogenetic curing epilepsy

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 169-169
  • Journal article
    Forte AE, Etard O, Reichenbach J, 2017,

    The human auditory brainstem response to running speech reveals a subcortical mechanism for selective attention

    , eLife, Vol: 6, ISSN: 2050-084X

    Humans excel at selectively listening to a target speaker in background noise such as competing voices. While the encoding of speech in the auditory cortex is modulated by selective attention, it remains debated whether such modulation occurs already in subcortical auditory structures. Investigating the contribution of the human brainstem to attention has, in particular, been hindered by the tiny amplitude of the brainstem response. Its measurement normally requires a large number of repetitions of the same short sound stimuli, which may lead to a loss of attention and to neural adaptation. Here we develop a mathematical method to measure the auditory brainstem response to running speech, an acoustic stimulus that does not repeat and that has a high ecological validity. We employ this method to assess the brainstem's activity when a subject listens to one of two competing speakers, and show that the brainstem response is consistently modulated by attention.

  • Journal article
    Xiloyannis M, Gavriel C, Thomik AA, Faisal AAet al., 2017,

    Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics

    , IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 25, Pages: 1785-1801, ISSN: 1534-4320

    Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process (gP) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our gP approach achieves high levels of performance (RMSE of 8°/s and ρ = 0.79). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of

  • Conference paper
    Gao C, Ghoreishizadeh S, Liu Y, Constandinou TGet al., 2017,

    On-chip ID generation for multi-node implantable devices using SA-PUF

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 678-681

    This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 m CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.

  • Conference paper
    Dávila-Montero S, Barsakcioglu DY, Jackson A, Constandinou TG, Mason AJet al., 2017,

    Real-time clustering algorithm that adapts to dynamic changes in neural recordings

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 690-693

    This work presents a computationally efficient real-time adaptive clustering algorithm that recognizes and adapts to dynamic changes observed in neural recordings. The algorithm consists of an off-line training phase that determines initial cluster positions, and an on-line operation phase that continuously tracks drifts in clusters and periodically verifies acute changes in cluster composition. Analysis of chronic recordings from non-human primates shows that adaptive clustering achieves an improvement of 14% in classification accuracy and demonstrates an ability to recognize acute changes with 78% accuracy, with up to 29% computational efficiency compared to the state-of-the-art. The presented algorithm is suitable for long-term chronic monitoring of neural activity in various applications such as neuroscience research and control of neural prosthetics and assistive devices.

  • Conference paper
    Haci D, Liu Y, Constandinou TG, 2017,

    32-channel ultra-low-noise arbitrary signal generation platform for biopotential emulation

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 698-701

    This paper presents a multichannel, ultra-low-noise arbitrary signal generation platform for emulating a wide range of different biopotential signals (e.g. ECG, EEG, etc). This is intended for use in the test, measurement and demonstration of bioinstrumentation and medical devices that interface to electrode inputs. The system is organized in 3 key blocks for generating, processing and converting the digital data into a parallel high performance analogue output. These blocks consist of: (1) a Raspberry Pi 3 (RPi3) board; (2) a custom Field Programmable Gate Array (FPGA) board with low-power IGLOO Nano device; and (3) analogue board including the Digital-to-Analogue Converters (DACs) and output circuits. By implementing the system this way, good isolation can be achieved between the different power and signal domains. This mixed-signal architecture takes in a high bitrate SDIO (Secure Digital Input Output) stream, recodes and packetizes this to drive two multichannel DACs, with parallel analogue outputs that are then attenuated and filtered. The system achieves 32-parallel output channels each sampled at 48kS/s, with a 10kHz bandwidth, 110dB dynamic range and uV-level output noise.

  • Conference paper
    Leene L, Constandinou TG, 2017,

    A 0.5V time-domain instrumentation circuit with clocked and unclocked ΔΣ operation

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2619-2622, ISSN: 2379-447X

    This paper presents a time-domain instrumentation circuit with exceptional noise efficiency directed at using nanometre CMOS for next generation neural interfaces. Current efforts to realize closed loop neuromodulation and high fidelity BMI prosthetics rely extensively on digital processing which isnot well integrated with conventional analogue instrumentation. The proposed time-domain topology employs a differential ring oscillator that is put into feedback using a chopper stabilized low noise transconductor and capacitive feedback. This realization promises better digital integration by extensively using time encoded digital signals and seamlessly allows both clocked & unclocked ΔΣ behavior which is useful on-chip characterizationand interfacing with synchronous systems. A 0.5V instrumentation system is implemented using a 65nm TSMC technology to realize a highly compact footprint that is 0.006mm2 in size. Simulation results demonstrate an excess of 55 dB dynamic range with 3.5 Vrms input referred noise for the given 810nW total system power budget corresponding to an NEF of 1.64.

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