Search Results (3)

Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed. Full article

Machine learning has been applied successfully for faulty wafer detection tasks in semiconductor manufacturing. For the tasks, prediction models are built with prior data to predict the quality of future wafers as a function of their precedent process parameters and measurements. In real-world problems, it is common for the data to have a portion of input variables that are irrelevant to the prediction of an output variable. The inclusion of many irrelevant variables negatively affects the performance of prediction models. Typically, prediction models learned by different learning algorithms exhibit different sensitivities with regard to irrelevant variables. Algorithms with low sensitivities are preferred as a first trial for building prediction models, whereas a variable selection procedure is necessarily considered for highly sensitive algorithms. In this study, we investigate the effect of irrelevant variables on three well-known representative learning algorithms that can be applied to both classification and regression tasks: artificial neural network, decision tree (DT), and k-nearest neighbors (k-NN). We analyze the characteristics of these learning algorithms in the presence of irrelevant variables with different model complexity settings. An empirical analysis is performed using real-world datasets collected from a semiconductor manufacturer to examine how the number of irrelevant variables affects the behavior of prediction models trained with different learning algorithms and model complexity settings. The results indicate that the prediction accuracy of k-NN is highly degraded, whereas DT demonstrates the highest robustness in the presence of many irrelevant variables. In addition, a higher model complexity of learning algorithms leads to a higher sensitivity to irrelevant variables. Full article

The uniqueness of microelectromechanical system (MEMS) devices, with their multiphysics characteristics, presents some limitations to the borrowed test methods from traditional integrated circuits (IC) manufacturing. Although some improvements have been performed, this specific area still lags behind when compared to the design and manufacturing competencies developed over the last decades by the IC industry. A complete digital solution for fast testing and characterization of inertial sensors with built-in actuation mechanisms is presented in this paper, with a fast, full-wafer test as a leading ambition. The full electrical approach and flexibility of modern hardware design technologies allow a fast adaptation for other physical domains with minimum effort. The digital system encloses a processor and the tailored signal acquisition, processing, control, and actuation hardware control modules, capable of the structure position and response analysis when subjected to controlled actuation signals in real time. The hardware performance, together with the simplicity of the sequential programming on a processor, results in a flexible and powerful tool to evaluate the newest and fastest control algorithms. The system enables measurement of resonant frequency (Fr), quality factor (Q), and pull-in voltage (Vpi) within 1.5 s with repeatability better than 5 ppt (parts per thousand). A full-wafer with 420 devices under test (DUTs) has been evaluated detecting the faulty devices and providing important design specification feedback to the designers. Full article