Design Of Integrated Circuit Chip Fault Diagnosis System Based On Neural Network

In recent years,the space industry has entered a new period of rapid development.The intensity,cost and mission duration of space activities have all been greatly increased,so the need for reliability of space products have become more and more stringent.Compared with other space equipment,integrated circuit devices are extremely sensitive to the abnormal environment in space,which leads to parameter deviation,functional disorder and even permanent damage of IC devices.Therefore,to ensure the reliability of aerospace electronic systems,especially integrated circuit devices,is the key to ensure the reliability of aerospace products.It is very important to design a failure diagnosis system for aerospace integrated circuit devices.Meanwhile,with the mature theory of machine learning and high-speed parallel computing,the age of big data has come.The application of neural network in various fields has become a general trend,which injects new impetus into the research of integrated circuit reliability.The design idea adopted in this paper is that theoretical research,system framework construction,software implementation,hardware implementation and verification experiments are carried out successively.Firstly,the main failure mechanisms of VLSI are studied,and the relative changes of failure process,inducement and parameters are analyzed.The key parameters that can reflect the failure state of devices are selected as characteristic parameters.On this basis,the circuit of the system under test is designed.The system under test includes chip under test,feature parameter acquisition device and general peripheral circuit.The acquisition device takes the specific parameters of the chip under test as characteristic parameters and transmits them to the failure diagnosis system for analysis.Secondly,the theory of pattern recognition is studied,and an integrated circuit chip failure diagnosis system based on back propagation neural network is designed.Through the fault diagnosis system,the fault characteristic parameters are analyzed to predict the working state of the device under test.The fault diagnosis system mainly includes characteristic parameter processing module,neural network training platform and neural network diagnosis model.The function of feature parameter processing is to process feature parameters into a form suitable as a neural network diagnostic model input.The characteristic parameter processing module is designed by Verilog HDL and implemented on FPGA.The function of neural network training platform is to train neural network parameters by machine learning so that the neural network diagnostic model can better analyze the characteristic parameters.The neural network training platform is designed in Python and implemented in software.The function of neural network diagnostic model is to analyze the characteristic parameters and predict the working state of the system under test.The neural network diagnosis model is designed by Verilog HDL and implemented on FPGA.Finally,the fault diagnosis system based on neural network is verified and tested.The validation experiment is divided into two parts: one is the independent validation experiment of the failure diagnosis system,and the other is the collaborative validation experiment of the failure diagnosis system and the system under test.In the independent verification experiment,high temperature simulation data of CMOS static storage unit circuit and JFM4VSX55 RT FPGA tri-temperature test data were taken as characteristic parameters to diagnose the failure state of target circuit and target device.When the number of training times exceeds 2000,the accuracy rate of failure state diagnosis for CMOS static storage unit circuit and JFM4VSX55 RT FPGA is stable over 97%.The current of the DSP chip was selected as the Characteristic parameter in the collaborative verification experiment,and the working state of the chip under test was diagnosed.The experimental results show that the acquisition frequency of the characteristic parameters is 2.5MHz,the diagnosis period is 30?s,and the diagnostic accuracy for high temperature failure for small samples is close to 100%.