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Due to the importance of metal layers in the product yield, serpentine test structures are usually fabricated on test chips to extract parameters for yield prediction. In this paper, the confidence level and estimation precision of the average defect density on metal layers are investigated to minimize the randomness of experimental results and make the measured parameters more convincing. On the basis of the Poisson yield model, the method to determine the total area of all serpentine test structures is obtained using the law of large numbers and the Lindeberg-Levy theorem. Furthermore, the method to determine an adequate area of each serpentine test structure is proposed under a specific requirement of confidence level and estimation precision. The results of Monte Carlo simulation show that the proposed method is consistent with theoretical analyses. It is also revealed by wafer experimental results that the method of designing serpentine test structure proposed in this paper has better performance.

For accurate prediction of via yield, via chains are usually fabricated on test chips to investigate issues about vias. To minimize the randomness of experiments and make the testing results more convincing, the confidence level and estimation precision of the via failure rate are investigated in this paper. Based on the Poisson yield model, the method of determining an adequate number of total vias is obtained using the law of large numbers and the de Moivre-Laplace theorem. Moreover, for a specific confidence level and estimation precision, the method of determining a suitable via chain length is proposed. For area minimization, an optimal combination of total vias and via chain length is further determined. Monte Carlo simulation results show that the method is in good accordance with theoretical analyses. Results of via failure rates measured on test chips also reveal that via chains designed using the proposed method has a better performance. In addition, the proposed methodology can be extended to investigate statistical significance for other failure modes.