The experimental teaching of semiconductor physics, an important practical link in majors such as microelectronics and optoelectronic information, directly affects the students’ understanding of the physical essence of semiconductor devices and the cultivation of innovative abilities. Recently, with the rapid development of cutting-edge fields such as third-generation semiconductors and quantum devices, the inconsistency between traditional experimental teaching models and industry demand has become increasingly prominent. The current semiconductor physics experiment teaching mostly adopts the principle-verification single-point experiment mode. This approach lacks a systematic correlation between various experimental projects. This makes it difficult for students to construct a systematic cognitive framework of “material properties interface behavior device functionality,” which is significantly different from the emphasis on cultivating “systematic thinking” in engineering education certification.

To effectively solve the problem of poor connection between various knowledge modules and overcome students’ difficulty in building systematic cognition in semiconductor physics teaching, the School of Integrated Circuits at Huazhong University of Science and Technology systematically reconstructed the semiconductor physics curriculum and experiments with the core teaching philosophy of “crossing potential barriers.” Through carefully designed experimental teaching content updates and thematic connections, we focused on bridging the knowledge gap and established a modular experimental reform plan using thin-film solar cells as a carrier. This design breaks through the traditional linear structure of experiments and integrates eight key experiments into a knowledge loop through a four-dimensional advanced module of “semiconductor characteristics interface engineering device physics innovation expansion.” We innovatively adopted the “4-for-1+collaborative learning” model based on constructivist learning theory. This model not only resolves the contradiction between class hour limitations and knowledge breadth but also incorporates cutting-edge technologies such as AI data analysis and Raspberry Pi intelligent development to enhance teaching challenges.

The core of the innovative experimental system is in the construction of a closed-loop ability cultivation chain. The semiconductor basic module realizes a coupling analysis of PL spectroscopy and conductivity experiments, allowing students to independently establish a quantitative relationship between carrier concentration, mobility, and film quality. In the device research stage, the volt–ampere characteristic curve of solar cells is correlated and modeled with the previous material parameters, such as the interface defect density inferred by fitting the series resistance. This continuous design concretizes theoretical knowledge into actionable engineering indicators. The innovation challenge module introduces real scientific research scenarios that enable students to apply their ability to use open-source hardware to build testing systems and develop AI analysis algorithms.

(1) Integration of the knowledge system: Using thin-film solar cells as a carrier, knowledge of semiconductor physics was organically integrated through a four-dimensional module design (material properties interface engineering device research innovation challenges). The data-sharing mechanism enables students to master the key knowledge points of eight experiments while completing four, thus doubling the teaching capacity. (2) Cutting-edge technology integration: The introduction of TRPL, AI data analysis (such as convolutional neural network processing of photoconductive attenuation curves), and Raspberry Pi intelligent hardware enables breaking through the technical limitations of traditional silicon-based experiments. (3) Advanced ability cultivation: Through the design of “basic challenge” gradient tasks, students are transformed from passive operators to active creators, helping them achieve breakthroughs in their own technology transfer abilities. (4) Efficient resource construction: Dynamic update mechanisms (such as the rapid introduction of research on perovskite/organic stacked devices) ensure the synchronization of experimental content with cutting-edge technological achievements in the semiconductor field.