Research on the Prediction of Thick-Film Resistor Values Using Genetic Algorithm-Optimized Neural Network

This paper introduces a neural network model optimized using a genetic algorithm to predict the resistance of thick-film resistors. Thick-film resistors are essential electronic components with significant industrial applications. However, achieving the target resistance often requires multiple process adjustments during production, resulting in low efficiency. The manufacturing process involves numerous steps and parameters, with resistance being closely tied to these variables. To address this issue, the proposed model predicts resistance directly from production process parameters, effectively enhancing efficiency. By integrating production practices and constraints, the method achieves an average prediction deviation as low as 1.198%. Additionally, the neural network analysis identifies key process parameters influencing resistance, offering targeted guidance for process optimization.