Design and implementation of Radix-3/Radix-2 based novel hybrid SAR ADC in scaled CMOS technologies

This thesis focuses on low power and high speed design techniques for successive approximation register (SAR) analog-to-digital converters (ADCs) in nanoscale CMOS technologies. SAR ADCs’ speed is limited by the number of bits of resolution. An N-bit conventional SAR ADC takes N conversion cycles. To speed up the conversion process, we introduce a radix-3 SAR ADC which can compute 1:6 bits per cycle. To our knowledge, it is the first fully programmable and efficiently hardware controlled radix-3 SAR ADC. We had to use two comparators per cycle due to ADC architecture and we proposed a simple calibration scheme for the comparators. Also, as the architecture of the DAC array is completely different from the architecture of conventional radix-2 SAR ADC’s DAC arrays, we came up with an algorithm for calibration of capacitors of the DAC. Low power SAR ADCs face two major challenges especially at high resolutions: (1) increased comparator power to suppress the noise, and (2) increased DAC switching energy due to the large DAC size. Due to our proposed architecture,the radix-3 SAR ADC uses two comparators per cycle and two differential DACs. To improve the comparator’s power efficiency, an efficient and low cost calibration technique has been introduced. It allows a low power and noisy comparator to achieve high signal-to-noise ratio (SNR). To improve the DAC switching energy, we introduced a radix-3/radix-2 based novel hybrid SAR ADC. We use two single ended DACs for radix-3 SAR ADC and these two single ended DACs can be used as one differential DAC for radix-2 SAR ADC. So, overall, we only have a single DAC as conventional radix- 2 SAR ADC. In addition, a monotonic switching technique is adopted for radix-2 search to reduce the DAC capacitor size and hence, to reduce switching power. It can reduce the total number of unit capacitors by four times. Our proposed hybrid SAR ADC can achieve less DAC energy compared to radix-3 and radix-2 SAR ADCs. Also, to utilize technology scaling, we used the minimum capacitor size allowed by thermal noise limitations. To achieve high resolution, we introduced calibration algorithm for the DAC array. As mentioned earlier, the radix-3 SAR ADC offers higher power than conventional radix-2 SAR ADC because of simultaneous use of two comparators. In the proposed hybrid SAR ADC, we will be using radix-3 search for first few MSB bits. So, the resolution required for radix-3 comparators are much larger than the LSB value of 10-bit ADC. By implementing calibration of comparators, we can use low power, high input referred offset and high speed comparators for radix-3 search. Radix-2 search will be used for rest of the bits and the resolution of the radix-2 comparator has to be less than the required LSB value. So, a high power, low input referred offset and high speed comparator is used for radix-2 search. Also, we introduced clock gating for comparators. So, radix-3 comparators will not toggle during radix-2 search and the radix-2 comparators will be inactive during radix-3 search. By using the aforementioned techniques, the overall comparator power is definitely less than a radix-3 SAR ADC and comparable to a conventional radix-2 SAR ADC. A prototype radix-3/radix-2 based hybrid SAR ADC with the proposed technique is designed and fabricated in 40nm CMOS technology. It achieves an SNDR of 56.9 dB and consumes only 0.38 mW power at 30MS/s, leading to a Walden figure of merit of 21.5 fJ/conv-step.Electrical and Computer Engineerin

Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Bidirectional neural interfaces are tools designed to “communicate” with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 µLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the µLED drivers include a high-resolution arbitrary waveform generation mode for shaping of µLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd

Circuit Design for Realization of a 16 bit 1MS/s Successive Approximation Register Analog-to-Digital Converter

As the use of digital systems continues to grow, there is an increasing need to convert analog information into the digital domain. Successive Approximation Register (SAR) analog-to-digital converters are used extensively in this regard due to their high resolution, small die area, and moderate conversion speeds. However, capacitor mismatch within the SAR converter is a limiting factor in its accuracy and resolution. Without some form of calibration, a SAR converter can only reasonably achieve an accuracy of 10 bits. The Split-ADC technique is a digital, deterministic, background self-calibration algorithm that can be applied to the SAR converter. This thesis describes the circuit design and physical implementation of a novel 16-bit 1MS/s SAR analog-to-digital converter for use with the Split-ADC calibration algorithm. The system was designed using the Jazz 0.18um CMOS process, successfully operates at 1MS/s, and consumes a die area of 1.2mm2. The calibration algorithm was applied, showing an improvement in the overall accuracy of the converter

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

Smart Sensor Networks For Sensor-Neural Interface

One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface. In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5μm CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18μm CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system. The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption

Challenges and Opportunities in Near-Threshold DNN Accelerators around Timing Errors

AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges and opportunities for operation in the NTC paradigm. By performing rigorous simulations in TPU systolic array, we reveal the severity of timing errors and its impact on inference accuracy at NTC. We analyze various attributes—such as data–delay relationship, delay disparity within arithmetic units, utilization pattern, hardware homogeneity, workload characteristics—and uncover unique localized and global techniques to deal with the timing errors in NTC

Low-Power Energy Efficient Circuit Techniques for Small IoT Systems

Although the improvement in circuit speed has been limited in recent years, there has been increased focus on the internet of things (IoT) as technology scaling has decreased circuit size, power usage and cost. This trend has led to the development of many small sensor systems with affordable costs and diverse functions, offering people convenient connection with and control over their surroundings. This dissertation discusses the major challenges and their solutions in realizing small IoT systems, focusing on non-digital blocks, such as power converters and analog sensing blocks, which have difficulty in following the traditional scaling trends of digital circuits. To accommodate the limited energy storage and harvesting capacity of small IoT systems, this dissertation presents an energy harvester and voltage regulators with low quiescent power and good efficiency in ultra-low power ranges. Switched-capacitor-based converters with wide-range energy-efficient voltage-controlled oscillators assisted by power-efficient self-oscillating voltage doublers and new cascaded converter topologies for more conversion ratio configurability achieve efficient power conversion down to several nanowatts. To further improve the power efficiency of these systems, analog circuits essential to most wireless IoT systems are also discussed and improved. A capacitance-to-digital sensor interface and a clocked comparator design are improved by their digital-like implementation and operation in phase and frequency domain. Thanks to the removal of large passive elements and complex analog blocks, both designs achieve excellent area reduction while maintaining state-of-art energy efficiencies. Finally, a technique for removing dynamic voltage and temperature variations is presented as smaller circuits in advanced technologies are more vulnerable to these variations. A 2-D simultaneous feedback control using an on-chip oven control locks the supply voltage and temperature of a small on-chip domain and protects circuits in this locked domain from external voltage and temperature changes, demonstrating 0.0066 V/V and 0.013 °C/°C sensitivities to external changes. Simple digital implementation of the sensors and most parts of the control loops allows robust operation within wide voltage and temperature ranges.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138743/1/wanyeong_1.pd

Energy efficient hybrid computing systems using spin devices

Emerging spin-devices like magnetic tunnel junctions (MTJ\u27s), spin-valves and domain wall magnets (DWM) have opened new avenues for spin-based logic design. This work explored potential computing applications which can exploit such devices for higher energy-efficiency and performance. The proposed applications involve hybrid design schemes, where charge-based devices supplement the spin-devices, to gain large benefits at the system level. As an example, lateral spin valves (LSV) involve switching of nanomagnets using spin-polarized current injection through a metallic channel such as Cu. Such spin-torque based devices possess several interesting properties that can be exploited for ultra-low power computation. Analog characteristic of spin current facilitate non-Boolean computation like majority evaluation that can be used to model a neuron. The magneto-metallic neurons can operate at ultra-low terminal voltage of ∼20mV, thereby resulting in small computation power. Moreover, since nano-magnets inherently act as memory elements, these devices can facilitate integration of logic and memory in interesting ways. The spin based neurons can be integrated with CMOS and other emerging devices leading to different classes of neuromorphic/non-Von-Neumann architectures. The spin-based designs involve `mixed-mode\u27 processing and hence can provide very compact and ultra-low energy solutions for complex computation blocks, both digital as well as analog. Such low-power, hybrid designs can be suitable for various data processing applications like cognitive computing, associative memory, and currentmode on-chip global interconnects. Simulation results for these applications based on device-circuit co-simulation framework predict more than ∼100x improvement in computation energy as compared to state of the art CMOS design, for optimal spin-device parameters