• Nanostructures and nanotechnology by Douglas Natelson, Cambridge University Press (2015)
• eBook available if accessed from AS domain.

• Quiz and attendance 30%
• Midterm I 20%
• Midterm II 20%
• Final presentation 30%

• 課程核心能力(Core competencies of the course)
• 基礎物理知識 Basic Physics Knowledge
• 中階物理知識 Intermediate Physics Knowledge
• 研究導向物理知識 Research Oriented Physics Knowledge
• 課程分類對照(Course classification comparison)
• 固態物理領域 Field of Solid State Physics
• 綜合領域 Comprehensive Field

In this course, students will learn the fundamental properties, synthesis methods and applications of various advanced materials, ranging from semiconductors, to quantum matters and low-dimensional nano-materials.

Start from Si technology, you will learn the thermodynamics and kinetics of bulk and thin film growth processes. Especially, the principles and development of the specific process and factors that control the material quality will be taught. In addition, some important issues like contact problem, doping and dielectric layer would be discussed. Besides Si, the second and third generation semiconductors, such as GaAs and GaN, would be introduced. Breakthroughs in synthesis of these materials have enabled or enhanced their unique properties, which can change our daily life.

After introducing the semiconductor properties and applications, we will take a step forward to a variety of materials, including ferroelectrics, oxide-based superconductor, spintronics materials, followed by energy materials such as thermoelectrics, batteries, perovskites, polymers and metal organic framework (MOF). Low-dimensional materials from 0D, 1D to 2D will be taught in the end of the course.

This multidisciplinary course will provide the knowledge of the physics and materials science of different advanced materials. Moreover, the course provides a comprehensive overview of a variety of growth methods, ranging from solution-based process, solid state synthesis, ball milling, to vapor phase deposition techniques such as sputtering, CVD, MOCVD, and MBE.

The ultimate course objective is to enhance the critical and innovative thinking of students by in depth understanding the relationship between the growth/morphology/structure of the materials and the fundamental properties, which holds the key for realizing practical applications of these advanced materials and devices.

• KITTEL, Charles, Introduction to Solid State Physics, 8th edition. Wiley, 2004
• CHUNG, Yip-Wah and KAPOOR, Monica, Introduction to Materials Science and Engineering, 2nd
• NAKAMURA, Shuji; CHICHIBU, Shigefusa F. (ed.). Introduction to Nitride Semiconductor Blue Lasers and Light Emitting Diodes. CRC Press, 2000
• NAKAMURA, Shuji; PEARTON, Stephen; FASOL, Gerhard. The Blue Laser Diode: The Complete Story. Springer Science & Business Media, 2000
• XIAO, Hong. Introduction to Semiconductor Manufacturing Technology, 2nd edition. SPIE Press E-Books, 2012
• PIERSON, H. O. Handbook of Chemical Vapor Deposition. Noyes Publication, 1999
• JONES, Anthony C.; HITCHMAN, Michael L. (ed.). Chemical Vapor Deposition: Precursors, Processes and Applications. Royal Society of Chemistry, 2009

• Final Exam: 55%
• Midterm Oral Presentation: 15%
• Midterm Essay: 15%
• Engagement in Class (e.g., asking questions, etc.): 15%

1. Handbook of crystal growth (vol 2), bulk crystal growth, ISBN: 9780444633033
2. Field Guide to Crystal Growth, Ashok K. Batra, Mohan D. Aggarwal
3. Chemistry of the elements/N. N. Greenwood and A. E. Shaw, ISBN: 0750633654

※ the above books are available at NTU library

This graduate course introduces modern machine learning for advanced nanotechnology, including nanomaterials and nanobiotechnology, designed for STEM students without an EE/CS background. The course will build core intuition for neural networks, convolutional models, Transformers, and generative approaches (GANs, diffusion), and then pivot to applications in materials simulation (graph neural networks and machine-learned interatomic potentials), materials discovery, and the principles behind autonomous, closed-loop experimentation. Guest mini-modules covering biomedical imaging modeling and data-driven drug discovery will be included. Emphasis is on concepts, seminal papers, and scientific decision-making rather than coding.

By the end of the course, students will be able to interpret and critique foundational ML papers; explain when and why to use CNNs, Transformers, and generative models; frame materials property prediction and inverse-design problems; reason about accuracy, data efficiency, uncertainty, and constraints in scientific workflows; and outline the architecture of a self-driving laboratory. Students will also gain a high-level understanding of biomedical imaging pipelines and molecular modeling workflows and how these connect to materials discovery.

• Homeworks: 60%
• Project: 40%