# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 1 Introduction  
Circuits are the basis of information communication and power transmission. There are a lot of interconnects in the circuit systems, such as connectors, bonding wires and solder balls. Mechanical shock, vibration, high temperature and high humidity, as well as dust create environmental stresses that may induce failure of these interconnects. As the working frequency of circuits becomes higher and higher, the interconnection shows complex electromagnetic characteristics. Due to the complex structure and electromagnetic characteristics of these circuits and devices, engineers expend great amounts of time and other resources for fault diagnosis, often needing to disassemble the problematic system when a circuit fault occurs. With the development of artificial intelligence technology, the application of intelligent algorithms to circuit fault detection has become a new focus of attention, with the goal of simplifying the complexity of fault diagnosis and making fault location detection more convenient and efficient. With regard to research on circuit fault diagnosis using intelligent algorithms, [PERSON] et al. [1] proposed a rider optimization algorithm (ROA) based on a neural network for fault detection in analog circuits. [PERSON] et al. [2] studied an analog circuit fault diagnosis algorithm using a genetic algorithm (GA) and neural network approach, which can minimize online measurement and offline calculation requirements. [PERSON] et al. [3, 4] investigated a simulation-after-test (SAT) fault diagnosis method based on a GA strategy for all possible continuous parameter faults (CPF) in analog filter circuits. [PERSON] et al. [5] proposed two modified decision tree algorithms, CV-DT and FR-DT, for analog circuitfault detection, which improves the accuracy and effectiveness of the decision tree, while [PERSON] et al. [6] used an improved GA for real-time circuit fault recovery. In this method, an analysis tree was used to monitor circuit faults, and an evolutionary mechanism with a recovery library and improved genetic algorithm were used to repair the circuit while the fault compensation mechanism was running. The improved algorithm significantly improves the fault-tolerant recovery rate. [PERSON] et al. [7] studied a new method for analog circuit fault diagnosis based on kurtosis and entropy, which has a high accuracy rate for the classification of soft and hard circuit faults. [PERSON] [8] et al. studied a method based on a support vector machine to detect faulty circuit conditions, isolate fault locations, and predict the remaining useful performance of analog circuits; the method was validated with two example filter circuits. In addition, there is also some research on the application of artificial intelligence algorithms to fault detection for power transmission systems [9; 10]. At present, almost all electrical connection fault detection methods are based on testing a specific hardware/component, such as connection resistance tests, impedance tests, S-parameter tests and time domain reflectometry. To the best of our knowledge, there has been little research to date on electrical connection fault detection using artificial intelligence based on testing a device/circuit rather than a specific component. In the current work, the fault characteristics of connectors, bonding wires, and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of a filter circuit with electrical connection faults were calculated using SPICE circuit simulation. These parameters were then used for input features training and testing of selected machine learning algorithm. Support vector machine (SVM), Logistic regression (LR), and Gradient boosting decision tree (GBDT) are three typical algorithms with distinct mechanisms selected to detect three types of electrical connection faults--due to connectors, bonding wires, and solder balls--in a filter circuit.