Smdled

A machine learning study predicting component self-alignment during SMT reflow using Random Forest, Neural Networks, and SVR, achieving high accuracy for placement optimization.

Analysis of LED applications in greenhouse lighting, covering energy efficiency, plant physiology, economic benefits, and future technological developments.

A scalable platform for nanophotonic emitters using individually addressable micro-LED-on-CMOS arrays and heterogeneous integration of nanowires.

Research on optimizing SMT component placement using machine learning to predict self-alignment effects, reducing positional errors in electronic manufacturing.

Analysis of novel spatial-domain complex modulation techniques (QCM, DCM, SM-DCM) for VLC, including performance, detectors, and future applications.

Comprehensive technical documentation and resources about smdled technology and applications.

A study analyzing the behavior and contributing factors of component shift in Surface Mount Technology using real production line data and statistical methods.