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The Four Core Disciplines of Chemical Engineering in Semiconductor Manufacturing: Thermodynamics, Reaction Engineering, Transport Phenomena, and Separation Processes

Semiconductor manufacturing is one of the most sophisticated applications of modern engineering. The fabrication of advanced integrated circuits requires precise control over chemical reactions, material properties, heat transfer, fluid flow, mass transport, and contamination. As transistor architectures evolve from FinFET to Gate-All-Around (GAA) structures, and manufacturing technologies advance toward nominal process nodes such as 3 nm and 2 nm, the importance of understanding these fundamental mechanisms continues to grow. Although semiconductor manufacturing is commonly associated with electrical engineering, materials science, and solid-state physics, chemical engineering provides much of the theoretical foundation for wafer fabrication. Many semiconductor processes can be understood as specialized chemical reactions, interfacial phenomena, and unit operations conducted under tightly controlled conditions. Four core disc...
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A Unified Understanding of Convolution, Laplace Transform, Fourier Transform, DTFT, DFT, and FFT

The Relationship Between Time Domain and Frequency Domain Signal and systems analysis is one of the fundamental subjects in electrical engineering, control engineering, mechanical engineering, and digital signal processing. Its central objective is to understand how signals behave over time and how systems transform input signals into output signals. Two important perspectives are the time domain and the frequency domain . In the time domain, a signal is represented as a function of time, such as \(x(t)\) for a continuous-time signal or \(x[n]\) for a discrete-time signal. This representation is useful for observing transient behavior, signal amplitude, and changes over time. In the frequency domain, a signal is represented in terms of its frequency components. This perspective is useful for identifying dominant frequencies, analyzing filtering behavior, and understanding the dynamic characteristics of physical systems. Dif...
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Advanced Signal Processing, System Identification, and Machine Learning for Structural Health Monitoring

Structural Health Monitoring (SHM) is an interdisciplinary engineering field that combines structural mechanics, vibration analysis, signal processing, system identification, statistical analysis, and artificial intelligence to evaluate the condition and performance of civil infrastructure. Bridges, high-rise buildings, dams, tunnels, and other critical structures are continuously exposed to environmental and operational influences, including wind, traffic, temperature variations, earthquakes, and material deterioration. These influences generate dynamic responses that can be measured using accelerometers, strain gauges, displacement sensors, inclinometers, and other sensing technologies. The primary objective of SHM is not simply to detect unusual sensor measurements. Rather, it is to determine whether changes in measured responses indicate variations in environmental conditions, sensor malfunctions, changes in structural properties, or potentially significant struc...
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