Abstract
Through silicon via (TSV) is the enabling technology for three-dimensional integrated circuit (3D IC) realization. During the TSV manufacturing process, the TSV inevitably has a multi-fault with both resistive-open and leakage faults. The multi-fault will decrease the reliability of the 3D IC seriously. A method of classifying and diagnosing TSV multi-fault is proposed by combining ring oscillator and least square support vector machine (LSSVM). Firstly, the schmidt trigger (ST) as a TSV test receiver based on the initial ring oscillator test structure is implemented. The parameters such as the oscillation period and duty cycle are measured with the TSV as load. A variety of fault types can be detected by testing the changes of these parameters. For further increase the accuracy of the fault test, the oscillation period and duty cycle of different faults are utilized as the feature vectors set, and the set is trained by LSSVM to obtain the fault diagnosis model. In order to get the optimization parameters of the LSSVM model, the particle swarm optimization (PSO) is adopted. As the problem of PSO is tend to local optimization and premature convergence, it may lead to misjudgment of TSV fault. Based on this, W-PSO that dynamically changes the inertia weight is used to optimize the LSSVM. Finally, experiment results imply that the W-PSO-LSSVM fault diagnosis model has a higher fault diagnosis accuracy rate than the PSO-LSSVM fault diagnosis model.
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Acknowledgments
This research work is supported by the National Natural Science Foundation of China (No.61661013) and Guangxi Science Foundation of China (No.2018GXNSFAA281327).
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Shang, Y., Tan, W., Li, C. et al. Detection and Diagnosis of Multi-Fault for through Silicon Vias in 3D IC. J Electron Test 36, 771–783 (2020). https://doi.org/10.1007/s10836-020-05916-y
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DOI: https://doi.org/10.1007/s10836-020-05916-y