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This work-in-progress research-to-practice paper presents the development and pilot implementation of curriculum that introduces semiconductor contents in a high school calculus class. The demand for chips soared through the COVID-19 pandemic, exposing our country's semiconductor manufacturing and supply chain risks. The need to reassert US semiconductor leadership will require training a well-educated workforce, starting at the K-12 level. Meanwhile, K-12 STEM teachers often juggle the conflicting requirements of standardized tests and the need to cultivate 21st-century skills, deeper learning, and transferable knowledge, among others. This paper presents a pilot implementation that could address both problems. Selected teachers attended an NSF-funded Research Experience for Teachers (RET) summer program to learn about chip design basics. They also received curriculum development support to design new modules on semiconductor topics that would attract their students' interests. 

With the passage of the Chips and Science Act, semiconductor workforce development has become front and center for US universities. Among the many skills needed for undergraduates to enter the semiconductor industry, debugging is an important skill that is rarely taught. As the transistor count and complexity of today’s chips grow, thanks to Moore’s Law, fewer new chips can work perfectly for the first time. Hence, much engineering effort is put into debugging, a process that identifies and fixes any discrepancies between the expected and measured chip behavior. This paper first investigates the need and the economic incentives of debugging in the semiconductor industry. It was estimated that a typical semiconductor project spent 35 to 50 percent of its time in debugging. The need for silicon debugging has led to a new profession called validation engineers. Debugging has also gained the nickname of the Schedule Killer, highlighting its impact on the project schedule and the company’s bottom line. Next, the paper summarizes existing cognitive models of troubleshooting. Early models often failed to capture the role of experience, which was essential for circuit and hardware debugging. Jonassen et al. proposed a troubleshooting learning architecture that includes the contribution of past experiences. This cognitive framework has been successfully applied in computer science and physics education, leading to some of the latest pedagogy innovations, such as collaborative pair debugging. This paper also investigates multiple emotions associated with debugging, such as frustration, fear, and anxiety. These emotions may lead to disengagement and avoidance of the subjects. Debugging may also be related to other non-cognitive factors, such as mindsets. The positive effect of teaching self-theory and a growth mindset has been observed in different age groups. However, studies also found that domain-specific aptitudes were more helpful in changing student’s performance in the subject matter. The takeaway message from this paper is that a genuinely effective debugging education intervention must be holistic and domain-specific. Holistic means that the intervention should address both cognitive and affective components. Domain specificity means that any growth mindset message should be contextually situated within the subject matter materials. How to design such an intervention will be the next million-dollar question, as it not only fills the gap of collegiate debug education in microelectronics but also serves as a critical missing piece toward developing a globally competent semiconductor workforce for generations to come.