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To measure the variation of device $V_{t}$ requires long test for conventional wafer acceptance test (WAT) test structures. This paper presents a framework that can efficiently and effectively obtain the mean and variance of $V_{t}$ for a large number of designs under test (DUTs). The proposed framework applies the model-based random forest as its core model-fitting technique to learn a model that can predict the mean and variance of $V_{t}$ based only on the combined $I_{d}$ measured from parallel connected DUTs. The proposed framework can further minimize the total number of $I_{d}$ measurement required for prediction models while limiting their accuracy loss. The experimental results based on the SPICE simulation of a UMC 28-nm technology demonstrate that the proposed model-fitting framework can achieve a more than 99% ${R}$ -squared for predicting either $V_{t}$ mean or $V_{t}$ variance. Compared to conventional WAT test structures using binary search, our proposed framework can achieve a $120.3 \times$ speedup on overall test time for test structures with 800 DUTs. [ABSTRACT FROM AUTHOR]

As process technologies continually advance, process variation has greatly increased and has gradually become one of the most critical factors for IC manufacturing. Furthermore, these increasingly complex processes continue to make greater use of stressors for mobility enhancement, thus requiring large volumes of data for extensive characterization of layout-dependent effects (LDE) for validation of both SPICE models and design for manufacturing. Transistor threshold voltage (Vt) is a commonly used parameter both for characterization during process development and for monitoring of volume manufacturing. To adequately quantify local process variation or LDE, Vt must be measured for a sufficiently large number of device-under-tests (DUTs) to obtain a statistically representative sample population. The number of Vt measurements required to obtain such a statistically significant result, however, requires extremely long testing time, especially for array-based test structure designs including thousands of DUTs. In this paper, we present a very fast threshold voltage measurement methodology using an operational amplifier-based source-measure unit test configuration, which greatly improves testing efficiency and accuracy, and is not sensitive to process variation. The proposed test methodology can improve Vt testing time by a factor of 5–10 relative to the commonly used binary-search algorithm, and by a factor of \sim2 relative to an optimized interpolation algorithm, and achieves better accuracy (standard deviation of Vt=0.15~mV , versus typical accuracy of \sim0.5~mV for the two algorithms mentioned). Furthermore, the layout and configuration of conventional test structures need not be modified to adapt the proposed methodology. The measured results from the most advanced process technology nodes demonstrate the testing efficiency and accuracy of the proposed test structure in characterizing the large number of DUTs required for quantifying process variation or LDEs. [ABSTRACT FROM AUTHOR]

As process technologies continually advance, local process variation has greatly increased and gradually become one of the most critical factors for integrated circuit manufacturing. To monitor local process variation, a large number of devices-under-test (DUTs) in close proximity must be measured. In this paper, we present a novel array-based test structure to characterize local process variation with limited area overhead. The proposed test structure can guarantee high measurement accuracy by application of the test techniques proposed in this paper: hardware IR compensation, voltage bias elevation, and leakage-current cancelation. Furthermore, the DUT layout need not be modified for the proposed test structure. Thus, the measured variation exactly reflects the reality in the manufacturing environment. The measured results from the few most advanced process-technology nodes demonstrate the effectiveness and efficiency of the proposed test structure in quantifying local process variation. [ABSTRACT FROM AUTHOR]