This 1-credit course introduces modern semiconductor chip design, packaging, and co-design principles with an emphasis on multi-physics modeling and engineering optimization. Topics include chip manufacturing history, advanced packaging technologies, heat transfer, fluid mechanics, elasticity, and thermal stress in electronic systems. Students learn foundational numerical methods such as finite difference, finite volume, and finite element methods, as well as optimization techniques including genetic algorithms and topology optimization. Statistical tools for performance evaluation and parameter tuning are introduced, along with emerging data-driven approaches such as physics-informed neural networks. The course emphasizes physical intuition, system-level thinking, and practical engineering applications.
This course includes 15 lectures divided by topic into 5 modules. Each module concludes with a brief assessment.
Format: Online, asynchronous
Platform: Canvas via nanoHUB
Estimated Time: 9 hours
Level: Advanced Undergraduate/Graduate Student
Prerequisites: Experience in engineering mathematics, including calculus, derivatives, integrals, differential equations, and linear algebra. Engineering physics, heat transfer, thermodynamics, and fluid mechanics.
Instructor: Professor Liang Pan, Purdue University
Course Objectives
Explain modern semiconductor packaging and co-design concepts
Describe the role of multi-physics modeling in chip and package design
Interpret basic heat transfer and thermal stress behavior in electronic systems
Compare common numerical modeling approaches used in engineering
Explain the principles of optimization and genetic algorithms
Understand how statistical tools are used to evaluate algorithm performance
Recognize the role of data-driven and physics-informed modeling methods
Request Course Enrollment
To enroll in this course, complete the course registration form. The SCALE team will review your request and arrange enrollment in the Canvas course on nanoHUB.