700mV low power low noise implantable neural recording system design

This dissertation presents the work for design and implementation of a low power, low noise neural recording system consisting of Bandpass Amplifier and Pipelined Analog to Digital Converter (ADC) for recording neural signal activities. A low power, low noise two stage neural amplifier for use in an intelligent Radio-Frequency Identification (RFID) based on folded cascode Operational Transconductance Amplifier (OTA) is utilized to amplify the neural signals. The optimization of the number of amplifier stages is discussed to achieve the minimum power and area consumption. The amplifier power supply is 0.7V. The midband gain of amplifier is 58.4dB with a 3dB bandwidth from 0.71 to 8.26 kHz. Measured input-referred noise and total power consumption are 20.7 μVrms and 1.90 μW respectively. The measured result shows that the optimizing the number of stages can achieve lower power consumption and demonstrates the neural amplifier's suitability for instu neutral activity recording. The advantage of power consumption of Pipelined ADC over Successive Approximation Register (SAR) ADC and Delta-Sigma ADC is discussed. An 8 bit fully differential (FD) Pipeline ADC for use in a smart RFID is presented in this dissertation. The Multiplying Digital to Analog Converter (MDAC) utilizes a novel offset cancellation technique robust to device leakage to reduce the input drift voltage. Simulation results of static and dynamic performance show this low power Pipeline ADC is suitable for multi-channel neural recording applications. The performance of all proposed building blocks is verified through test chips fabricated in IBM 180nm CMOS process. Both bench-top and real animal test results demonstrate the system's capability of recording neural signals for neural spike detection

Low-Power Low-Noise CMOS Analog and Mixed-Signal Design towards Epileptic Seizure Detection

About 50 million people worldwide suffer from epilepsy and one third of them have seizures that are refractory to medication. In the past few decades, deep brain stimulation (DBS) has been explored by researchers and physicians as a promising way to control and treat epileptic seizures. To make the DBS therapy more efficient and effective, the feedback loop for titrating therapy is required. It means the implantable DBS devices should be smart enough to sense the brain signals and then adjust the stimulation parameters adaptively. This research proposes a signal-sensing channel configurable to various neural applications, which is a vital part for a future closed-loop epileptic seizure stimulation system. This doctoral study has two main contributions, 1) a micropower low-noise neural front-end circuit, and 2) a low-power configurable neural recording system for both neural action-potential (AP) and fast-ripple (FR) signals. The neural front end consists of a preamplifier followed by a bandpass filter (BPF). This design focuses on improving the noise-power efficiency of the preamplifier and the power/pole merit of the BPF at ultra-low power consumption. In measurement, the preamplifier exhibits 39.6-dB DC gain, 0.8 Hz to 5.2 kHz of bandwidth (BW), 5.86-μVrms input-referred noise in AP mode, while showing 39.4-dB DC gain, 0.36 Hz to 1.3 kHz of BW, 3.07-μVrms noise in FR mode. The preamplifier achieves noise efficiency factor (NEF) of 2.93 and 3.09 for AP and FR modes, respectively. The preamplifier power consumption is 2.4 μW from 2.8 V for both modes. The 6th-order follow-the-leader feedback elliptic BPF passes FR signals and provides -110 dB/decade attenuation to out-of-band interferers. It consumes 2.1 μW from 2.8 V (or 0.35 μW/pole) and is one of the most power-efficient high-order active filters reported to date. The complete front-end circuit achieves a mid-band gain of 38.5 dB, a BW from 250 to 486 Hz, and a total input-referred noise of 2.48 μVrms while consuming 4.5 μW from the 2.8 V power supply. The front-end NEF achieved is 7.6. The power efficiency of the complete front-end is 0.75 μW/pole. The chip is implemented in a standard 0.6-μm CMOS process with a die area of 0.45 mm^2. The neural recording system incorporates the front-end circuit and a sigma-delta analog-to-digital converter (ADC). The ADC has scalable BW and power consumption for digitizing both AP and FR signals captured by the front end. Various design techniques are applied to the improvement of power and area efficiency for the ADC. At 77-dB dynamic range (DR), the ADC has a peak SNR and SNDR of 75.9 dB and 67 dB, respectively, while consuming 2.75-mW power in AP mode. It achieves 78-dB DR, 76.2-dB peak SNR, 73.2-dB peak SNDR, and 588-μW power consumption in FR mode. Both analog and digital power supply voltages are 2.8 V. The chip is fabricated in a standard 0.6-μm CMOS process. The die size is 11.25 mm^2. The proposed circuits can be extended to a multi-channel system, with the ADC shared by all channels, as the sensing part of a future closed-loop DBS system for the treatment of intractable epilepsy

Design of sigma-delta modulators for analog-to-digital conversion intensively using passive circuits

This thesis presents the analysis, design implementation and experimental evaluation of passiveactive discrete-time and continuous-time Sigma-Delta (ΣΔ) modulators (ΣΔMs) analog-todigital converters (ADCs). Two prototype circuits were manufactured. The first one, a discrete-time 2nd-order ΣΔM, was designed in a 130 nm CMOS technology. This prototype confirmed the validity of the ultra incomplete settling (UIS) concept used for implementing the passive integrators. This circuit, clocked at 100 MHz and consuming 298 μW, achieves DR/SNR/SNDR of 78.2/73.9/72.8 dB, respectively, for a signal bandwidth of 300 kHz. This results in a Walden FoMW of 139.3 fJ/conv.-step and Schreier FoMS of 168 dB. The final prototype circuit is a highly area and power efficient ΣΔM using a combination of a cascaded topology, a continuous-time RC loop filter and switched-capacitor feedback paths. The modulator requires only two low gain stages that are based on differential pairs. A systematic design methodology based on genetic algorithm, was used, which allowed decreasing the circuit’s sensitivity to the circuit components’ variations. This continuous-time, 2-1 MASH ΣΔM has been designed in a 65 nm CMOS technology and it occupies an area of just 0.027 mm2. Measurement results show that this modulator achieves a peak SNR/SNDR of 76/72.2 dB and DR of 77dB for an input signal bandwidth of 10 MHz, while dissipating 1.57 mW from a 1 V power supply voltage. The ΣΔM achieves a Walden FoMW of 23.6 fJ/level and a Schreier FoMS of 175 dB. The innovations proposed in this circuit result, both, in the reduction of the power consumption and of the chip size. To the best of the author’s knowledge the circuit achieves the lowest Walden FOMW for ΣΔMs operating at signal bandwidth from 5 MHz to 50 MHz reported to date

LOW-VOLTAGE LOW-POWER ANALOG-TO-DIGITAL CONVERTERS

Ph.DDOCTOR OF PHILOSOPH

Interface Circuits for Microsensor Integrated Systems

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [Recent advances in sensing technologies, especially those for Microsensor Integrated Systems, have led to several new commercial applications. Among these, low voltage and low power circuit architectures have gained growing attention, being suitable for portable long battery life devices. The aim is to improve the performances of actual interface circuits and systems, both in terms of voltage mode and current mode, in order to overcome the potential problems due to technology scaling and different technology integrations. Related problems, especially those concerning parasitics, lead to a severe interface design attention, especially concerning the analog front-end and novel and smart architecture must be explored and tested, both at simulation and prototype level. Moreover, the growing demand for autonomous systems gets even harder the interface design due to the need of energy-aware cost-effective circuit interfaces integrating, where possible, energy harvesting solutions. The objective of this Special Issue is to explore the potential solutions to overcome actual limitations in sensor interface circuits and systems, especially those for low voltage and low power Microsensor Integrated Systems. The present Special Issue aims to present and highlight the advances and the latest novel and emergent results on this topic, showing best practices, implementations and applications. The Guest Editors invite to submit original research contributions dealing with sensor interfacing related to this specific topic. Additionally, application oriented and review papers are encouraged.

Low-voltage Low-power Switched-Capacitor ?S Modulator Design

Ph.DDOCTOR OF PHILOSOPH

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

Power Efficient Architectures for High Accuracy Analog-to-Digital Converters

Incremental ADCs (IADCs) have found wide applications in sensor interface circuitry since, compared to ∆Σ ADCs, they provide low-latency high-accuracy conversion and easy multiplexing among multiple channels. On the other hand, continuous-time ∆Σ ADCs (CTDSM) have been receiving more and more attention as a power-efficient solution in targeting medium to high accuracy over wider range of signal bandwidth (tens of MHz). In this dissertation, novel configurations have been explored in both architectures for power-efficient and high-accuracy data conversion. First, a multi-step incremental ADC (IADC) using multi-slope extended counting technique is described. Only one active integrator is used in the three-step conversion cycle. The accuracy of the IADC is extended by having it configured as multi-slope ADCs in two additional steps. The proposed IADC uses the same circuitry as a first-order IADC (IADC1), but it exhibits as good efficiency as its second-order ∆Σ ADC counterpart. For the same accuracy, the conversion cycle is shortened by a factor of more than 2⁹ compared to the IADC1. Fabricated in 0.18-μm CMOS process, the prototype ADC occupies 0.5 mm². With a 642 kHz clock, it achieves SNDR of 52.2 dB in the first step. The SNDR is boosted to 79.8 dB in the second step, and to 96.8 dB in the third step, over a 1 kHz signal band. The power consumption is 35 µW from a 1.5 V power supply. This gives an excellent Schreier FoM of 174.6 dB. Secondly, a multi-step incremental ADC with extended binary counting is proposed. It achieves high accuracy by splitting one conversion cycle into two serial steps. During the first step, the ADC works as a first-order incremental ADC (IADC1). The second step reuses the single integrator and extends the accuracy to 16 bits by a two-capacitor SAR-assisted binary counting technique. For the same accuracy, the conversion cycle is shortened by a factor of more than 2⁸ as compared to the single-step IADC. Fabricated in 0.18-μm CMOS process, the SAR-assisted IADC achieves a peak SNR/SNDR/DR of 97.1/96.6/100.2 dB over a 1.2 kHz bandwidth, while dissipating 33.2 μW from a 1.5 V supply. This gives a Schreier FoM of 175.8 dB and Walden FoM of 0.25 pJ/conv.-step. Finally, the design of a continuous-time ∆Σ modulator (CTDSM) to be used in an ultrasound beamformer for biomedical imaging is described. To achieve better resolution, the prototype modulator operates at 1.2 GHz. It incorporates a digital excess loop delay (ELD) compensation to replace the active adder in front of the internal quantizer. A digitally controlled reference-switching matrix, combined with the data-weighted averaging (DWA) technique, results in a delay-free feedback path. A multi-bit FIR feedback DAC, along with its compensation path, is used to achieve lower clock jitter sensitivity and better loop filter linearity. The modulator achieves 79.4 dB dynamic range, 77.3 dB SNR and 74.3 dB SNDR over a 15 MHz signal bandwidth. Fabricated in a 65 nm CMOS process, the core modulator occupies an area of only 0.16 mm² and dissipates 6.96 mW from a 1 V supply. A 58.6 fJ/conversion-step figure of merit was achieved.Keywords: Incremental ADC, multi-step operation, instrumentation and measurement, sensor interface, analog-to-digital converter, extended counting, chopper stabilization, delta-sigma ADC, multi-slope ADCsKeywords: Incremental ADC, multi-step operation, instrumentation and measurement, sensor interface, analog-to-digital converter, extended counting, chopper stabilization, delta-sigma ADC, multi-slope ADC

Integrated temperature sensors in deep sub-micron CMOS technologies

textIntegrated temperature sensors play an important role in enhancing the performance of on-chip power and thermal management systems in today's highly-integrated system-on-chip (SoC) platforms, such as microprocessors. Accurate on-chip temperature measurement is essential to maximize the performance and reliability of these SoCs. However, due to non-uniform power consumption by different functional blocks, microprocessors have fairly large thermal gradient (and variation) across their chips. In the case of multi-core microprocessors for example, there are task-specific thermal gradients across different cores on the same die. As a result, multiple temperature sensors are needed to measure the temperature profile at all relevant coordinates of the chip. Subsequently, the results of the temperature measurements are used to take corrective measures to enhance the performance, or save the SoC from catastrophic over-heating situations which can cause permanent damage. Furthermore, in a large multi-core microprocessor, it is also imperative to continuously monitor potential hot-spots that are prone to thermal runaway. The locations of such hot spots depend on the operations and instruction the processor carries out at a given time. Due to practical limitations, it is an overkill to place a big size temperature sensor nearest to all possible hot spots. Thus, an ideal on-chip temperature sensor should have minimal area so that it can be placed non-invasively across the chip without drastically changing the chip floor plan. In addition, the power consumption of the sensors should be very low to reduce the power budget overhead of thermal monitoring system, and to minimize measurement inaccuracies due to self-heating. The objective of this research is to design an ultra-small size and ultra-low power temperature sensor such that it can be placed in the intimate proximity of all possible hot spots across the chip. The general idea is to use the leakage current of a reverse-bias p-n junction diode as an operand for temperature sensing. The tasks within this project are to examine the theoretical aspect of such sensors in both Silicon-On-Insulator (SOI), and bulk Complementary Metal-Oxide Semiconductor (CMOS) technologies, implement them in deep sub-micron technologies, and ultimately evaluate their performances, and compare them to existing solutions.Electrical and Computer Engineerin