Dopamiinin hapettumisen lukija-anturirajapinta 65 nm CMOS teknologialla

Sensing and monitoring of neural activities within the central nervous system has become a fast-growing area of research due to the need to understand more about how neurons communicate. Several neurological disorders such as Parkinson’s disease, Schizophrenia, Alzeihmers and Epilepsy have been reported to be associated with imbalance in the concentration of neurotransmitters such as glutamate and dopamine [1] - [5]. Hence, this thesis proposes a solution for the measurement of dopamine concentration in the brain during neural communication. The proposed design of the dopamine oxidation readout sensor interface is based on a mixed-signal front-end architecture for minimizing noise and high resolution of detected current signals. The analog front-end is designed for acquisition and amplification of current signals resulting from oxidation and reduction at the biosensor electrodes in the brain. The digital signal processing (DSP) block is used for discretization of detected dopamine oxidation and reduction current signals that can be further processed by an external system. The results from the simulation of the proposed design show that the readout circuit has a current resolution of 100 pA and can detect minimum dopamine concentration of 10 μMol based on measured data from novel diamond-like carbon electrodes [6]. Higher dopamine concentration can be detected from the sensor interface due to its support for a wide current range of 1.2 μA(±600 nA). The digital code representation of the detected dopamine has a resolution of 14.3-bits with RMS conversion error of 0.18 LSB which results in an SNR of 88 dB at full current range input. However, the attained ENOB is 8-bits due to the effect of nonlinearity in the oscillator based ADC. Nonetheless, the achieved resolution of the readout circuit provides good sensitivity of released dopamine in the brain which is useful for further understanding of neurotransmitters and fostering research into improved treatments of related neurodegenerative diseases.Keskushermoston aktiivisuuden havainnointi ja tarkkailu on muodostunut tärkeäksi tutkimusalaksi, sillä tarve ymmärtää neuronien viestintää on kasvanut. Monien hermostollisten sairauksien kuten Parkinsonin taudin, skitsofrenian, Alzheimerin taudin ja epilepsian on huomattu aiheuttavan muutoksia välittäjäaineiden, kuten glutamaatin ja dopamiinin, pitoisuuksissa [1] - [5]. Aiheeseen liittyen tässä työssä esitetään ratkaisu dopamiinipitoisuuden mittaamiseksi aivoista. Esitetty dopamiinipitoisuuden lukijapiiri perustuu sekamuotoiseen etupäärakenteeseen, jolla saavutetaan matala kohinataso ja hyvä tarkkuus signaalien ilmaisemisessa. Suunniteltu analoginen etupää kykenee lukemaan ja vahvistamaan dopamiinipitoisuuden muutosten aiheuttamia virran muutoksia aivoihin asennetuista elektrodeista. Digitaalisen signaalinkäsittelyn avulla voidaan havaita dopamiinin hapettumis-ja pelkistymisvirtasignaalit, ja välittää ne edelleen ulkoisen järjestelmän muokattavaksi. Simulaatiotulokset osoittavat, että suunniteltu piiri saavuttaa 100 pA virran erottelukyvyn. Simuloinnin perustuessa hiilipohjaisiin dopamiinielektrodeihin piiri voi havaita 10 μMol dopamiinipitoisuuden [6]. Myös suurempia dopamiinipitoisuuksia voidaan havaita, sillä etupäärajapinta tukee 1.2 μA(±600 nA) virta-aluetta. Digitaalinen esitysmuoto tukee 14.3 bitin esitystarkkuutta 0.18 bitin RMS virheellä saavuttaen 88 dB dynaamisen virta-alueen. Saavutettu ENOB (tehollinen bittimäärä) on kuitenkin 8 bittiä oskillaattoripohjaisen ADC:n (analogia-digitaalimuuntimen) epälineaarisuuden takia. Saavutettu tarkkuus tuottaa hyvän herkkyyden dopamiinin havaitsemiseksi ja hyödyttää siten välittäjäainetutkimusta ja uusien hoitomuotojen kehittämistä hermostollisiin sairauksiin

Design and Implementation of Complexity Reduced Digital Signal Processors for Low Power Biomedical Applications

Wearable health monitoring systems can provide remote care with supervised, inde-pendent living which are capable of signal sensing, acquisition, local processing and transmission. A generic biopotential signal (such as Electrocardiogram (ECG), and Electroencephalogram (EEG)) processing platform consists of four main functional components. The signals acquired by the electrodes are amplified and preconditioned by the (1) Analog-Front-End (AFE) which are then digitized via the (2) Analog-to-Digital Converter (ADC) for further processing. The local digital signal processing is usually handled by a custom designed (3) Digital Signal Processor (DSP) which is responsible for either anyone or combination of signal processing algorithms such as noise detection, noise/artefact removal, feature extraction, classification and compres-sion. The digitally processed data is then transmitted via the (4) transmitter which is renown as the most power hungry block in the complete platform. All the afore-mentioned components of the wearable systems are required to be designed and fitted into an integrated system where the area and the power requirements are stringent. Therefore, hardware complexity and power dissipation of each functional component are crucial aspects while designing and implementing a wearable monitoring platform. The work undertaken focuses on reducing the hardware complexity of a biosignal DSP and presents low hardware complexity solutions that can be employed in the aforemen-tioned wearable platforms. A typical state-of-the-art system utilizes Sigma Delta (Σ∆) ADCs incorporating a Σ∆ modulator and a decimation filter whereas the state-of-the-art decimation filters employ linear phase Finite-Impulse-Response (FIR) filters with high orders that in-crease the hardware complexity [1–5]. In this thesis, the novel use of minimum phase Infinite-Impulse-Response (IIR) decimators is proposed where the hardware complexity is massively reduced compared to the conventional FIR decimators. In addition, the non-linear phase effects of these filters are also investigated since phase non-linearity may distort the time domain representation of the signal being filtered which is un-desirable effect for biopotential signals especially when the fiducial characteristics carry diagnostic importance. In the case of ECG monitoring systems the effect of the IIR filter phase non-linearity is minimal which does not affect the diagnostic accuracy of the signals. The work undertaken also proposes two methods for reducing the hardware complexity of the popular biosignal processing tool, Discrete Wavelet Transform (DWT). General purpose multipliers are known to be hardware and power hungry in terms of the number of addition operations or their underlying building blocks like full adders or half adders required. Higher number of adders leads to an increase in the power consumption which is directly proportional to the clock frequency, supply voltage, switching activity and the resources utilized. A typical Field-Programmable-Gate-Array’s (FPGA) resources are Look-up Tables (LUTs) whereas a custom Digital Signal Processor’s (DSP) are gate-level cells of standard cell libraries that are used to build adders [6]. One of the proposed methods is the replacement of the hardware and power hungry general pur-pose multipliers and the coefficient memories with reconfigurable multiplier blocks that are composed of simple shift-add networks and multiplexers. This method substantially reduces the resource utilization as well as the power consumption of the system. The second proposed method is the design and implementation of the DWT filter banks using IIR filters which employ less number of arithmetic operations compared to the state-of-the-art FIR wavelets. This reduces the hardware complexity of the analysis filter bank of the DWT and can be employed in applications where the reconstruction is not required. However, the synthesis filter bank for the IIR wavelet transform has a higher computational complexity compared to the conventional FIR wavelet synthesis filter banks since re-indexing of the filtered data sequence is required that can only be achieved via the use of extra registers. Therefore, this led to the proposal of a novel design which replaces the complex IIR based synthesis filter banks with FIR fil-ters which are the approximations of the associated IIR filters. Finally, a comparative study is presented where the hybrid IIR/FIR and FIR/FIR wavelet filter banks are de-ployed in a typical noise reduction scenario using the wavelet thresholding techniques. It is concluded that the proposed hybrid IIR/FIR wavelet filter banks provide better denoising performance, reduced computational complexity and power consumption in comparison to their IIR/IIR and FIR/FIR counterparts

Bioelectronic Sensor Nodes for Internet of Bodies

Energy-efficient sensing with Physically-secure communication for bio-sensors on, around and within the Human Body is a major area of research today for development of low-cost healthcare, enabling continuous monitoring and/or secure, perpetual operation. These devices, when used as a network of nodes form the Internet of Bodies (IoB), which poses certain challenges including stringent resource constraints (power/area/computation/memory), simultaneous sensing and communication, and security vulnerabilities as evidenced by the DHS and FDA advisories. One other major challenge is to find an efficient on-body energy harvesting method to support the sensing, communication, and security sub-modules. Due to the limitations in the harvested amount of energy, we require reduction of energy consumed per unit information, making the use of in-sensor analytics/processing imperative. In this paper, we review the challenges and opportunities in low-power sensing, processing and communication, with possible powering modalities for future bio-sensor nodes. Specifically, we analyze, compare and contrast (a) different sensing mechanisms such as voltage/current domain vs time-domain, (b) low-power, secure communication modalities including wireless techniques and human-body communication, and (c) different powering techniques for both wearable devices and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which has been accepted for Publication in Volume 25 of the Annual Review of Biomedical Engineering (2023). Only Personal Use is Permitte

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces

A 23μW Solar-Powered Keyword-Spotting ASIC with Ring-Oscillator-Based Time-Domain Feature Extraction

Voice-controlled interfaces on acoustic Internet-of-Things (IoT) sensor nodes and mobile devices require integrated low-power always-on wake-up functions such as Voice Activity Detection (VAD) and Keyword Spotting (KWS) to ensure longer battery life. Most VAD and KWS ICs focused on reducing the power of the feature extractor (FEx) as it is the most power-hungry building block. A serial Fast Fourier Transform (FFT)-based KWS chip [1] achieved 510nW; however, it suffered from a high 64ms latency and was limited to detection of only 1-to-4 keywords (2-to-5 classes). Although the analog FEx [2]–[3] for VAD/KWS reported 0.2μW-to-1 μW and 10ms-to-100ms latency, neither demonstrated >5 classes in keyword detection. In addition, their voltage-domain implementations cannot benefit from process scaling because the low supply voltage reduces signal swing; and the degradation of intrinsic gain forces transistors to have larger lengths and poor linearity

A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION

This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 9292m for the implemented digital-backend

A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION

This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 92×92µm for the implemented digital-backend

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

A Triple-Mode Performance-Optimized Reconfigurable Incremental ADC for Smart Sensor Applications

This paper proposes a triple-mode discrete-time incremental analog-to-digital converter (IADC) employing successive approximation register (SAR)-based zooming and extended counting (EC) schemes to achieve programmable trade-off capability of resolution and power consumption in various smart sensor applications. It mainly consists of an incremental delta???sigma modulator and the proposed SAR-EC sub-ADC for alternate operation of the coarse SAR conversion and EC. They can be reconfigured to operate separately depending on the application requirements. The SAR-based zooming structure allows the IADC to have better linearity and resolution, and additional activation of the EC function gives the further resolution. During this reconfigurable conversion process, pipelined reusing operation of sub-blocks reduces the silicon area and the number of cycles for target resolutions. A prototype ADC is fabricated in a 180-nm CMOS process, and its triple-mode operation of high-resolution, medium-resolution, and low-power is experimentally verified to achieve 116.1-, 109.4-, and 73.3-dB dynamic ranges, consuming 1.60, 1.26, and 0.39 mW, respectively

Delta-Sigma Modulator based Compact Sensor Signal Acquisition Front-end System

The proposed delta-sigma modulator ($\Delta\Sigma$M) based signal acquisition architecture uses a differential difference amplifier (DDA) customized for dual purpose roles, namely as instrumentation amplifier and as integrator of $\Delta\Sigma$M. The DDA also provides balanced high input impedance for signal from sensors. Further, programmable input amplification is obtained by adjustment of $\Delta\Sigma$M feedback voltage. Implementation of other functionalities, such as filtering and digitization have also been incorporated. At circuit level, a difference of transconductance of DDA input pairs has been proposed to reduce the effect of input resistor thermal noise of front-end R-C integrator of the $\Delta\Sigma$M. Besides, chopping has been used for minimizing effect of Flicker noise. The resulting architecture is an aggregation of functions of entire signal acquisition system within the single block of $\Delta\Sigma$M, and is useful for a multitude of dc-to-medium frequency sensing and similar applications that require high precision at reduced size and power. An implementation of this in 0.18-$\mu$m CMOS process has been presented, yielding a simulated peak signal-to-noise ratio of 80 dB and dynamic range of 109dBFS in an input signal band of 1 kHz while consuming 100 $\mu$W of power; with the measured signal-to-noise ratio being lower by about 9 dB.Comment: 13 pages, 16 figure