Physics Informed Neural Operators (PINO): A Scientific Machine Learning Approach for the Extremely Fast First-Principles Monte Carlo Analysis of Nanoscale FETs (PI)

• Funding Agency: Anusandhan National Research Foundation (ANRF), India
  
• Period: Dec. 2025 – Dec. 2030
  
• PI: Dr Sourajeet Roy (CMAS Lab, IITR)
• Co-PIs: Dr. Avirup Dasgupta (DiRac Lab, IITR) and Dr. Oves Badami (IITH)
• Budget Sanctioned: ₹95,00,000

Machine Learning Augmented SPICE Models for Efficient Circuit Design (Co-PI)

• Funding Agency: Semiconductor Research Corporation (SRC), USA
  
• Period: Dec. 2022 – Dec. 2025
  
• Budget Sanctioned: ₹34,80,000
  

Fast Machine Learning-Based Parametric SPICE Macromodel Extraction for FinFET Device-to-System Level Optimization (PI)

• Funding Agency: Qualcomm, India
  
• Period: Aug. 2021 – July 2022
  
• Budget Sanctioned: ₹10,08,464
  

A Polymorphic Polynomial Chaos Approach towards Stochastic Design Optimization of Integrated Circuits in the Presence of Epistemic and Aleatory Variables (PI)

• Funding Agency: SERB, Department of Science and Technology, India
  
• Program: Startup Research Grant (SRG)
  
• Period: Dec. 2019 – Dec. 2021
  
• Budget Sanctioned: ₹20,22,370