Energy aware ultra-low power SAR ADC in 180nm CMOS for biomedical application

Power consumption is one of the main design constraints in today’s integrated circuits. For systems powered by batteries, such as implantable biomedical devices, ultra-low power consumption is paramount. In these systems, analog-to-digital converters (ADCs) are key components as the interface between the analog world and the digital domain. This thesis addresses the design challenges, strategies, as well as circuit techniques of ultra-low-power ADCs for medical implant devices. In this thesis four architectures of SAR ADC is implemented with different energy efficiency. In first architecture, conventional SAR ADC was designed in 180nm CMOS technology with a 1-V power supply and a 1-kS/s sampling rate for monitoring bio potential signals, the ADC achieves a signal-to-noise and distortion ratio of 57.16 dB and consumes 43 nW power, resulting in a figure of merit of 73 fJ/conversion-step. In second architecture, Split capacitor SAR ADC was designed in 180nm CMOS with same resolution and sampling speed

Time-based, Low-power, Low-offset 5-bit 1 GS/s Flash ADC Design in 65nm CMOS Technology

Low-power, medium resolution, high-speed analog-to-digital converters (ADCs) have always been important block which have abundant applications such as digital signal processors (DSP), imaging sensors, environmental and biomedical monitoring devices. This study presents a low power Flash ADC designed in nanometer complementary metal-oxide semiconductors (CMOS) technology. Time analysis on the output delay of the comparators helps to generate one more bit. The proposed technique reduced the power consumption and chip area substantially in comparison to the previous state-of-the-art work. The proposed ADC was developed in TSMC 65nm CMOS technology. The offset cancellation technique was embedded in the proposed comparator to decrement the static offset of the comparator. Moreover, one more bit was generated without using extra comparators. The proposed ADC achieved 4.1 bits ENOB at input Nyquist frequency. The simulated differential and integral non-linearity static tests were equal to +0.26/-0.17 and +0.22/-0.15, respectively. The ADC consumed 7.7 mW at 1 GHz sampling frequency, achieving 415 fJ/Convstep Figure of Merit (FoM)

Data Conversion Within Energy Constrained Environments

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

Registro de aproximaciones sucesivas y convertidor digital a analógico para SAR ADC de 10 bits de baja potencia para aplicaciones biomédicas

This document presents the design of a successive approximation register (SAR) and a digital to analog converter (DAC). The two circuits are designed, synthesized, and implemented in layout using TSMC 180nm technology. The DAC has a Charge-Scaling Capacitors architecture and uses a switching scheme different to the conventional one, which improves the power consumption of the circuit. These modules are used in a low power 10-bit ADC and are designed for Biomedical applications. The SAR design is synthesized using a clock with a frequency of 250 MHz, the total power consumption of the SAR is 22045.030 uW. A testbench is designed to verify the functionality of the SAR by testing several cases of interest. The base capacitance of the DAC is 1 pF. The functionality of the DAC is tested using a mixed signal simulator and a testbench with the required circuit. This also includes the Verilog model of the SAR block. The testbench is able to apply single conversions test case and a ramp-test case, in which all the possible inputs to the DAC are tested sequentially.ITESO, A. C

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

A Current-Mode Multi-Channel Integrating Analog-to-Digital Converter

Multi-channel analog to digital converters (ADCs) are required where signals from multiple sensors can be digitized. A lower power per channel for such systems is important in order that when the number of channels is increased the power does not increase drastically. Many applications require signals from current output sensors, such as photosensors and photodiodes to be digitized. Applications for these sensors include spectroscopy and imaging. The ability to digitize current signals without converting currents to voltages saves power, area, and the design time required to implement I-to-V converters. This work describes a novel and unique current-mode multi-channel integrating ADC which processes current signals from sensors and converts it to digital format. The ADC facilitates the processing of current analog signals without the use of transconductors. An attempt has been made also to incorporate voltage-mode techniques into the current-mode design so that the advantages of both techniques can be utilized to augment the performance of the system. Additionally since input signals are in the form of currents, the dynamic range of the ADC is less dependant on the supply voltage. A prototype 4-channel ADC design was fabricated in a 0.5-micron bulk CMOS process. The measurement results for a 10Ksps sampling rate include a DNL, which is less than 0.5 LSB, and a power consumption of less than 2mW per channel

Ultra-low Power Circuits and Architectures for Neuromorphic Computing Accelerators with Emerging TFETs and ReRAMs

Neuromorphic computing using post-CMOS technologies is gaining increasing popularity due to its promising potential to resolve the power constraints in Von-Neumann machine and its similarity to the operation of the real human brain. To design the ultra-low voltage and ultra-low power analog-to-digital converters (ADCs) for the neuromorphic computing systems, we explore advantages of tunnel field effect transistor (TFET) analog-to-digital converters (ADCs) on energy efficiency and temperature stability. A fully-differential SAR ADC is designed using 20 nm TFET technology with doubled input swing and controlled comparator input common-mode voltage. To further increase the resolution of the ADC, we design an energy efficient 12-bit noise shaping (NS) successive-approximation register (SAR) ADC. The 2nd-order noise shaping architecture with multiple feed-forward paths is adopted and analyzed to optimize system design parameters. By utilizing tunnel field effect transistors (TFETs), the Delta-Sigma SAR is realized under an ultra-low supply voltage VDD with high energy efficiency. The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM\u27s conducting filament with built-in stochasticity is used to mimic the neuron\u27s membrane capacitor, which temporally integrates input spikes. A capacitor-less neuron circuit is designed, laid out, and simulated. The output spiking train of the neuron obeys the Poisson distribution. Based on the ReRAM based neuron, we propose a scalable and reconfigurable architecture that exploits the ReRAM-based neurons for deep Spiking Neural Networks (SNNs). In prior publications, neurons were implemented using dedicated analog or digital circuits that are not area and energy efficient. In our work, for the first time, we address the scaling and power bottlenecks of neuromorphic architecture by utilizing a single one-transistor-one-ReRAM (1T1R) cell to emulate the neuron. We show that the ReRAM-based neurons can be integrated within the synaptic crossbar to build extremely dense Process Element (PE)–spiking neural network in memory array–with high throughput. We provide microarchitecture and circuit designs to enable the deep spiking neural network computing in memory with an insignificant area overhead