CAREER: Universal Design Automation Framework for Analog Integrated Systems

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Analog integrated circuits (ICs) are indispensable components in modern electronics. They allow computers to interact with the real world by performing key functions such as sensor interfacing, signal conditioning, power delivery, and energy harvesting. Their importance keeps growing as these functions are increasingly essential in the next-generation artificial intelligence, automotive, and medical applications that seek to sense, learn, and act anytime, anywhere. To meet the surging market demand, it is critical to ensure the development and production of analog ICs are agile, low-cost, and high-quality. However, this goal is currently being hindered by two grand challenges. On the technical level, the analog IC design practice in the industry to date remains a slow, experience-demanding manual process due to the lack of a general-purpose, reliable, and scalable analog design automation tool. This deficiency imposes a severe bottleneck for cost and design time reduction. Additionally, societal factors include experienced designers retiring fast while younger generations may shy away from pursuing an IC designer career. A paradigm shift in analog IC design practices is needed to prevent this analog IC productivity crisis from turning into a pressing roadblock for technology development and industry growth. To that end, this CAREER research aims to establish and validate a novel general-purpose analog IC design automation flow that overcomes the existing productivity barrier. The research will lead to a game-changing tool that allows designers to turn their ideas into circuit structures for a wide range of analog systems in a LEGO-like plug-and-play manner, improves their design reliably with machine learning, and generates fabrication-ready mask layouts swiftly. It will not only bring radical improvement to analog IC productivity and accelerate new technology development but also create a far-reaching impact on the competitiveness of the US semiconductor industry. The integrated education activities will also revolutionize IC technology education at both undergraduate and graduate levels, facilitating workforce revitalization. Analog IC design is conventionally deemed a highly ad-hoc practice, where high-level abstraction is hardly feasible. Although analog design automation (ADA) has received growing attention in the past decade, there is a lack of an ADA solution that combines versatile system-scale handling capabilities, reliability, and sufficient human interactions. In this research, the investigator identifies a path of breakthrough. This framework is established through endeavors in three research thrusts. (1) Development of an innovative unified analog system modeling method, which can describe a wide range of analog systems in the form of linear filter models built from a common set of basic operation cells. This method includes an intelligent synthesis algorithm and a user interface to aid designers in turning their thoughts into models. (2) Development of a soft cell concept, which performs on-the-fly circuit creation for the operation cell. This idea will address the reliability issue through a software-hardware co-design methodology. (3) Development of an early performance prediction mechanism that guides designers to achieve better results. The framework will be thoroughly validated by applying it to a practical biomedical system-on-chip design and demonstrating competitive circuit performances via real silicon measurements. In essence, the project closely incorporates the interdisciplinary efforts of modeling, circuit design, and algorithm, a unique path in ADA research. The generated knowledge bridges the circuit design community and the electronic design automation (EDA) community to catalyze closer collaboration and co-development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.