This book addresses the need for energy-efficient amplifiers, providing gain enhancement strategies, suitable to run in parallel with lower supply voltages, by introducing a new family of single-stage cascode-free amplifiers, with proper design, optimization, fabrication and experimental evaluation. The authors describe several topologies, using the UMC 130 nm CMOS technology node with standard-VT devices, for proof-of-concept, achieving results far beyond what is achievable with a classic single-stage folded-cascode amplifier. Readers will learn about a new family of circuits with a broad range of applications, together with the familiarization with a state-of-the-art electronic design automation methodology used to explore the design space of the proposed circuit family.
This book presents a new methodology with reduced time impact to address the problem of analog integrated circuit (IC) yield estimation by means of Monte Carlo (MC) analysis, inside an optimization loop of a population-based algorithm. The low time impact on the overall optimization processes enables IC designers to perform yield optimization with the most accurate yield estimation method, MC simulations using foundry statistical device models considering local and global variations. The methodology described by the authors delivers on average a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. In addition to describing a newly developed yield estimation technique, the authors also provide detailed background on automatic analog IC sizing and optimization.
This book consists of the research, design and implementation, from sizing to layout with parasitic extraction and yield estimation, of a low-power, low-noise amplifier for biomedical and healthcare applications of bio-potential signals, particularly focusing on the electromyography and electrooculography. These signals usually operate in different broadbands, yet follow an impulse-shape transmission, hence being suitable to be applied and detected by the same receiver.
This book presents a new methodology with reduced time impact to address the problem of analog integrated circuit (IC) yield estimation by means of Monte Carlo (MC) analysis, inside an optimization loop of a population-based algorithm. The low time impact on the overall optimization processes enables IC designers to perform yield optimization with the most accurate yield estimation method, MC simulations using foundry statistical device models considering local and global variations. The methodology described by the authors delivers on average a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. In addition to describing a newly developed yield estimation technique, the authors also provide detailed background on automatic analog IC sizing and optimization.
This book addresses the need for energy-efficient amplifiers, providing gain enhancement strategies, suitable to run in parallel with lower supply voltages, by introducing a new family of single-stage cascode-free amplifiers, with proper design, optimization, fabrication and experimental evaluation. The authors describe several topologies, using the UMC 130 nm CMOS technology node with standard-VT devices, for proof-of-concept, achieving results far beyond what is achievable with a classic single-stage folded-cascode amplifier. Readers will learn about a new family of circuits with a broad range of applications, together with the familiarization with a state-of-the-art electronic design automation methodology used to explore the design space of the proposed circuit family.
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.