Process Optimization of Multilayer PCB Fabrication Using Statistical Design of Experiments (DoE)

Multilayer printed circuit boards (PCBs) used in automotive electronics demand high reliability, driving the need for optimized fabrication processes. This paper presents a comprehensive study on optimizing key multilayer PCB fabrication processes – including layer lamination, drilling, electroless/electrolytic copper plating, photo-imaging, and etching – through Statistical Design of Experiments (DoE) methods. A full-factorial experimental design is applied to the PCB imaging and etching process, revealing how exposure energy, developer speed, and etchant speed impact defect rates and yield. Response Surface Methodology (RSM) with a Central Composite Design is then employed to fine-tune the copper plating process parameters (current density, plating time, bath temperature), targeting adequate copper thickness within minimal plating time. Simulated industrial data with realistic variability are used to analyze factor effects and interactions. Results show that careful control of process parameters can dramatically improve outcomes: for example, optimized photo-imaging settings increased yield from ~80% to over 95%, and plating parameter optimization reduced plating time by ~30% to achieve target thickness. Colorful data visualizations (main-effects Pareto charts, interaction plots, 3D response surfaces, contour maps) and engineering flow diagrams illustrate the findings. Analysis of variance (ANOVA) models quantify each factor’s contribution, and regression models are derived for prediction of process performance. The study also discusses practical implications for automotive PCB manufacturing – improved yield, uniform plating thickness, and reduced defects translate to greater reliability in harsh automotive environments. Overall, the DoE-driven optimizations demonstrate a rigorous, data-driven approach for refining PCB fabrication processes to meet the stringent quality standards of automotive electronics.