Ultra-low noise, high-frame rate readout design for a 3D-stacked CMOS image sensor

Due to the switch from CCD to CMOS technology, CMOS based image sensors have become smaller, cheaper, faster, and have recently outclassed CCDs in terms of image quality. Apart from the extensive set of applications requiring image sensors, the next technological breakthrough in imaging would be to consolidate and completely shift the conventional CMOS image sensor technology to the 3D-stacked technology. Stacking is recent and an innovative technology in the imaging field, allowing multiple silicon tiers with different functions to be stacked on top of each other. The technology allows for an extreme parallelism of the pixel readout circuitry. Furthermore, the readout is placed underneath the pixel array on a 3D-stacked image sensor, and the parallelism of the readout can remain constant at any spatial resolution of the sensors, allowing extreme low noise and a high-frame rate (design) at virtually any sensor array resolution. The objective of this work is the design of ultra-low noise readout circuits meant for 3D-stacked image sensors, structured with parallel readout circuitries. The readout circuit’s key requirements are low noise, speed, low-area (for higher parallelism), and low power. A CMOS imaging review is presented through a short historical background, followed by the description of the motivation, the research goals, and the work contributions. The fundamentals of CMOS image sensors are addressed, as a part of highlighting the typical image sensor features, the essential building blocks, types of operation, as well as their physical characteristics and their evaluation metrics. Following up on this, the document pays attention to the readout circuit’s noise theory and the column converters theory, to identify possible pitfalls to obtain sub-electron noise imagers. Lastly, the fabricated test CIS device performances are reported along with conjectures and conclusions, ending this thesis with the 3D-stacked subject issues and the future work. A part of the developed research work is located in the Appendices.Devido à mudança da tecnologia CCD para CMOS, os sensores de imagem em CMOS tornam se mais pequenos, mais baratos, mais rápidos, e mais recentemente, ultrapassaram os sensores CCD no que respeita à qualidade de imagem. Para além do vasto conjunto de aplicações que requerem sensores de imagem, o próximo salto tecnológico no ramo dos sensores de imagem é o de mudar completamente da tecnologia de sensores de imagem CMOS convencional para a tecnologia “3D-stacked”. O empilhamento de chips é relativamente recente e é uma tecnologia inovadora no campo dos sensores de imagem, permitindo vários planos de silício com diferentes funções poderem ser empilhados uns sobre os outros. Esta tecnologia permite portanto, um paralelismo extremo na leitura dos sinais vindos da matriz de píxeis. Além disso, num sensor de imagem de planos de silício empilhados, os circuitos de leitura estão posicionados debaixo da matriz de píxeis, sendo que dessa forma, o paralelismo pode manter-se constante para qualquer resolução espacial, permitindo assim atingir um extremo baixo ruído e um alto debito de imagens, virtualmente para qualquer resolução desejada. O objetivo deste trabalho é o de desenhar circuitos de leitura de coluna de muito baixo ruído, planeados para serem empregues em sensores de imagem “3D-stacked” com estruturas altamente paralelizadas. Os requisitos chave para os circuitos de leitura são de baixo ruído, rapidez e pouca área utilizada, de forma a obter-se o melhor rácio. Uma breve revisão histórica dos sensores de imagem CMOS é apresentada, seguida da motivação, dos objetivos e das contribuições feitas. Os fundamentos dos sensores de imagem CMOS são também abordados para expor as suas características, os blocos essenciais, os tipos de operação, assim como as suas características físicas e suas métricas de avaliação. No seguimento disto, especial atenção é dada à teoria subjacente ao ruído inerente dos circuitos de leitura e dos conversores de coluna, servindo para identificar os possíveis aspetos que dificultem atingir a tão desejada performance de muito baixo ruído. Por fim, os resultados experimentais do sensor desenvolvido são apresentados junto com possíveis conjeturas e respetivas conclusões, terminando o documento com o assunto de empilhamento vertical de camadas de silício, junto com o possível trabalho futuro

Architectures and circuits for low-voltage energy conversion and applications in renewable energy and power management

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 337-343).In this thesis we seek to develop smaller, less expensive, and more efficient power electronics. We also investigate emerging applications where the proper implementation of these new types of power converters can have a significant impact on the overall system performance. We have developed a new two-stage dc-dc converter architecture suitable for low-voltage CMOS power delivery. The architecture, which combines the benefits of switched-capacitor and inductor-based converters, achieves both large voltage step-down and high switching frequency, while maintaining good efficiency. We explore the benefits of a new soft-charging technique that drastically reduces the major loss mechanism in switched-capacitor converters, and we show experimental results from a 5-to-1 V, 0.8 W integrated dc-dc converter developed in 180 nm CMOS technology. The use of power electronics to increase system performance in a portable thermophotovoltaic power generator is also investigated in this thesis. We show that mechanical non-idealities in a MEMS fabricated energy conversion device can be mitigated with the help of low-voltage distributed maximum power point tracking (MPPT) dc-dc converters. As part of this work, we explore low power control and sensing architectures, and present experimental results of a 300 mW integrated MPPT developed in 0.35 um CMOS with all power, sensing and control circuitry on chip. The final piece of this thesis investigates the implementation of distributed power electronics in solar photovoltaic applications. We explore the benefits of small, intelligent power converters integrated directly into the solar panel junction box to enhance overall energy capture in real-world scenarios. To this end, we developed a low-cost, high efficiency (>98%) power converter that enables intelligent control and energy conversion at the sub-panel level. Experimental field measurements show that the solution can provide up to a 35% increase in panel output power during partial shading conditions compared to current state-of-the-art solutions.by Robert C. N. Pilawa-Podgurski.Ph.D

Advances in Solid State Circuit Technologies

This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields

Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing

The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications. A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd

Advanced CMOS Integrated Circuit Design and Application

The recent development of various application systems and platforms, such as 5G, B5G, 6G, and IoT, is based on the advancement of CMOS integrated circuit (IC) technology that enables them to implement high-performance chipsets. In addition to development in the traditional fields of analog and digital integrated circuits, the development of CMOS IC design and application in high-power and high-frequency operations, which was previously thought to be possible only with compound semiconductor technology, is a core technology that drives rapid industrial development. This book aims to highlight advances in all aspects of CMOS integrated circuit design and applications without discriminating between different operating frequencies, output powers, and the analog/digital domains. Specific topics in the book include: Next-generation CMOS circuit design and application; CMOS RF/microwave/millimeter-wave/terahertz-wave integrated circuits and systems; CMOS integrated circuits specially used for wireless or wired systems and applications such as converters, sensors, interfaces, frequency synthesizers/generators/rectifiers, and so on; Algorithm and signal-processing methods to improve the performance of CMOS circuits and systems

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

NASA Tech Briefs, March 1989

This issue's special features cover the NASA inventor of the year, and the other nominees for the year. Other Topics include: Electronic Components & and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, and Life Science

Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date