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Semiconductor manufacturing has been extensively exploiting machine-learning (ML) to process equipment sensory data (ESD) for near-real time anomaly detection (AD). ESD characteristics are highly diversified and data lengths vary among processing steps and cycles. Cleansing ESD with minimum distortion (CMD) to fit the fixed-length input requirement by ML-based AD is critical to AD effectiveness and is challenging. This paper presents a novel CMD method of four innovations: i) statistical mode-based equalization of step data lengths for the least number of step data length changes, ii) importance indicator value (IIV) of a data sample based on its relative difference with the subsequent sample, and iii) step data segmentation into groups based on samples of significant IIVs and the least-entropy-group-to-cleanse-first rule, and iv) cleansing the least IIV sample(s) in the selected group for step data length equalization. CMD application to ESD demonstrates its characteristics preservation property. Simulation experiments are on an integration of data cleansing with an unsupervised ML-based AD system, STALAD. Comparisons with two benchmark methods over AD scenarios of small-scale drifts and shifts show that CMD not only is superior in facilitating accurate detection by STALAD but also helps detect anomaly much earlier than using the two benchmarks. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing processes, yield analysis aims to increase the yield by determining and managing the causes of low yield. The variable data collected from semiconductor manufacturing processes, in which hundreds of unit processes are implemented according to specific conditions and sequences, are interdependent, and the variables related to previous processes influence the variables in subsequent processes. Therefore, the order of processes should be considered when building a model that searches for the causes of low yield. However, there have been few studies in this area. This paper proposes a low-yield root cause search method considering the order of processes using a long short-term memory with attention mechanism (LSTM-AM) model. Specifically, the LSTM-AM model is applied to data classified according to the process structure of semiconductor products, and the causes of low yield are determined considering the order of processes by extracting attention weights. Experiments are conducted to verify the suitability of the proposed method using real yield data from a semiconductor company. The experimental results confirm that the proposed method outperforms the existing low yield root cause search methods in terms of low yield prediction. [ABSTRACT FROM AUTHOR]

Data-driven Fault Detection and Classification approaches are becoming increasingly important in semiconductor manufacturing and in other industries aiming at implementing the Zero-defect paradigm. Two of the main challenges in developing such solutions are: (i) the complexity of sensor data, that typically presents themselves in the form of time-series, requiring the employment of time-consuming and possibly sub-optimal feature extraction approaches; (ii) the fact that faults/defects may be caused by more than a single process, but in many cases they are generated by a cascade of processes. In this paper, we tackle the first issue, by considering a two-stage case study consisting of a deposition process and a rapid thermal process. The proposed approach is based on convolutional deep autoencoders employed to perform feature extraction from time-series sensor data in frontend production equipment. We will show on the reported case study, how the proposed approach outperfoms key numbers-based approaches typically used in the industry. To allow reproducibility of the reported results and to foster research in the field, we publicly share the data used in this work. [ABSTRACT FROM AUTHOR]