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Grant: Discovery Grants Program - Individual

Memorial University of Newfoundland

$39,000.00

Machine-Learning-Driven Synthesis Methodologies for Analog and RF Integrated Circuits in Advanced Nanometer Technologies

EF 2023-24

Résumé
As the conventional planar CMOS technology scales down to 22nm and below, maintaining ideal transistor characteristics becomes increasingly challenging. For this reason, nonplanar field-effect transistors (FETs) have been regarded as effective substitutes for ultimate scaling. Due to physical complexity of the nonplanar FET structure in advanced nanometer technologies, transistor performance is strongly affected by associated parasitics, layout dependent effects (LDEs), and lithographic imperfection. To maintain signal integrity, these issues have to be seriously considered in the synthesis of analog/RF integrated circuits (ICs). In this research program, the fascinating advancement of artificial intelligence will be leveraged to promote electronic design automation (EDA) of analog/RF ICs. A complete set of synthesis methodologies and computer-aided design tools will be studied to strengthen the link between performance optimization and physical effects. An innovative machine-learning-driven circuit topology synthesis methodology will be developed. It can emulate expert human designers to apply the knowledge extracted from the given training data to effectively generate proper circuit topologies through inference. Moreover, a novel parasitic/LDE/lithography-aware circuit-sizing methodology will be studied; this methodology would consist of a quick approximate optimization stage followed by a simulation-based refined sizing process. Parasitics, LDEs, and lithographic effects in the advanced nonplanar nanometer technologies will be integrated into MOSFET modeling, which can be included into the topology synthesis and circuit sizing for proactive consideration of layout impact. Furthermore, to fill the vacuum of similar commercial tools in the EDA market, we will continue to explore automated analog/RF layout migration strategies to address parasitics, LDEs, and lithography-related constraints in the nonplanar nanometer technologies. Due to their high sensitivity to complicated analog effects, analog/RF ICs have been recognized as the design bottleneck for promptly pushing mixed-signal system-on-chip products to market. Systematic countermeasures in the layout-aware comprehensive synthesis of analog/RF ICs have not yet been addressed worldwide. With enormous potential for commercialization, this proposed research program addresses the increasingly challenging parasitics, LDEs, and lithographic issues in the nonplanar nanometer technologies; these issues cannot be ignored for analog/RF IC synthesis especially under the shrinking design window and pressing process variation. This program will train over half a dozen next-generation highly qualified personnel (HQP) on advanced EDA in upgraded nanometer technologies. It will benefit the analog/RF design community through significant improvements in design productivity and reliability, which can enhance Canada's competitive advantage in this field.
Programme
Discovery Grants Program - Individual
Emplacement
Newfoundland and Labrador, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2019-04130
Name nom
Zhang, Lihong
Department département
Electrical and Computer Engineering
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2019
Award amount
39000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1510
Committee name en
Electrical and Computer Engineering
Committee name fr
Génie électrique et informatique
Area of application code
513
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Electrical and electronic machinery and equipment (including computer hardware)
Area of application fr
Machinerie et équipement électriques et électroniques (y compris matériel informatique)
Research subject code
2519
Research subject group en
Electrical and electronic engineering
Research subject group fr
Microelectronics
Research subject en
Génie électrique et électronique
Research subject fr
Microélectronique
Application title
Machine-Learning-Driven Synthesis Methodologies for Analog and RF Integrated Circuits in Advanced Nanometer Technologies
Keyword
advanced CMOS and FinFET technologies; analog and RF integrated circuits; artificial-intelligence-inspired circuit design reasoning; circuit sizing and layout synthesis; circuit topology synthesis; constraint-driven circuit optimization flow; machine learning; methodologies and computer-aided-design tools; nonplanar nanometer technologies; VLSI electronic design automation
Application summary
As the conventional planar CMOS technology scales down to 22nm and below, maintaining ideal transistor characteristics becomes increasingly challenging. For this reason, nonplanar field-effect transistors (FETs) have been regarded as effective substitutes for ultimate scaling. Due to physical complexity of the nonplanar FET structure in advanced nanometer technologies, transistor performance is strongly affected by associated parasitics, layout dependent effects (LDEs), and lithographic imperfection. To maintain signal integrity, these issues have to be seriously considered in the synthesis of analog/RF integrated circuits (ICs). In this research program, the fascinating advancement of artificial intelligence will be leveraged to promote electronic design automation (EDA) of analog/RF ICs. A complete set of synthesis methodologies and computer-aided design tools will be studied to strengthen the link between performance optimization and physical effects. An innovative machine-learning-driven circuit topology synthesis methodology will be developed. It can emulate expert human designers to apply the knowledge extracted from the given training data to effectively generate proper circuit topologies through inference. Moreover, a novel parasitic/LDE/lithography-aware circuit-sizing methodology will be studied; this methodology would consist of a quick approximate optimization stage followed by a simulation-based refined sizing process. Parasitics, LDEs, and lithographic effects in the advanced nonplanar nanometer technologies will be integrated into MOSFET modeling, which can be included into the topology synthesis and circuit sizing for proactive consideration of layout impact. Furthermore, to fill the vacuum of similar commercial tools in the EDA market, we will continue to explore automated analog/RF layout migration strategies to address parasitics, LDEs, and lithography-related constraints in the nonplanar nanometer technologies. Due to their high sensitivity to complicated analog effects, analog/RF ICs have been recognized as the design bottleneck for promptly pushing mixed-signal system-on-chip products to market. Systematic countermeasures in the layout-aware comprehensive synthesis of analog/RF ICs have not yet been addressed worldwide. With enormous potential for commercialization, this proposed research program addresses the increasingly challenging parasitics, LDEs, and lithographic issues in the nonplanar nanometer technologies; these issues cannot be ignored for analog/RF IC synthesis especially under the shrinking design window and pressing process variation. This program will train over half a dozen next-generation highly qualified personnel (HQP) on advanced EDA in upgraded nanometer technologies. It will benefit the analog/RF design community through significant improvements in design productivity and reliability, which can enhance Canada's competitive advantage in this field.