To monitor process and identify the deviation as early as possible, data-driven methods have been applied for process monitoring and fault detection in semiconductor manufacturing. Although various fault detection and classification models had been discussed in the literature, however, little research has been devoted to feature selection from trace data that is important for process monitoring of natural variation. Additionally, the high-mix production mode with different recipes leads to process dynamic of wafer-to-wafer (W2W) variation which should also be identified for safeguarding false alarms and serving as a warning indicator. Therefore, this paper proposes a data-driven framework to identify the key features with respect to the W2W variation. In particular, the self-organizing map is used to annotate the grade of wafer variation among the in-line metrology data. Subsequently, the adaptive boosting (AdaBoost) is adopted to examine the effectiveness of every feature and its processing times, respectively. To validate the proposed framework, an empirical study from a semiconductor fabrication plant is conducted. The experimental results demonstrate that the key feature identification is of critical importance to build highly capable models for process monitoring. Through the dimensionality reduction technique, it has been illustrated that a smaller set of the identified key features are able to pinpoint the W2W variation of different wafer grades more clearly than the whole set of process features.Note to Practitioners— Process monitoring has become more difficult with the shrinking linewidth in semiconductor manufacturing. The challenges of analyzing equipment sensor or raw trace data for process monitoring in high-mix manufacturing processes are to incorporate subject-matter expert knowledge for setting control limit meticulously, to detect the subtle changes by analyzing the whole trace data profile, and to identify W2W variation for reducing false alarms. This paper proposes a data-driven framework for process monitoring by adopting data-driven approaches without recourse to domain judgement. Experimental results demonstrate that the proposed data-driven framework can effectively identify the key features via sensor readings and corresponding processing times, respectively. The engineers can make use of the extracted features to perform a predictive monitoring on metrology data for detection of potential process deterioration.

Mixed-product production is common practice in semiconductor manufacturing and has attracted considerable academic attention recently. The main purpose of the mixed-product mode is to manufacture a wide variety of products in the same time period, to meet customer demand. The complex and changeable group of products is called “mixed-product production mode.” A specific tool used and a specific product manufactured are together called a “thread.” There is a need for “threaded” Run-to-Run (RtR) control in semiconductor manufacturing. This article develops a new product-based, disturbance-accumulating strategy in threaded RtR control by means of the double exponentially weighted moving average (dEWMA) technique. The main idea is to develop two disturbance-accumulating strategies; one for the intercept update of the same product between cycles and the other for the disturbance update in terms of the previous product between cycles, cope with the transient and transition effects, respectively. A comprehensive simulation study is conducted to compare the performance of the proposed product-based RtR controller with the existing controls specifically designed for the mixed-product mode. The proposed control has great promise as an extremely competitive threaded RtR controller in practice.Note to Practitioners—Run-to-run (RtR) control has been widely applied in batch manufacturing processes to reduce variation and has been identified as a turn-key technology for maintaining quality outputs in semiconductor manufacturing. The mixed-product production mode poses a great challenge of how to adjust process recipes between different products to alleviate initial recipe bias, process shifts, and patterned disturbances from run to run, product to product, and cycle to cycle. The proposed mixed-product RtR controller is developed based on a novel disturbance-accumulating, product-based strategy. This new controller is easy to implement in practice and has been shown to be effective in producing high-quality control outputs.

MOTIVATION: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. RESULTS: PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. AVAILABILITY: Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm.

BACKGROUND: Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms. RESULTS: In this study we present a Java-based freeware, easyExon http://microarray.ym.edu.tw/easyexon, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool. CONCLUSION: EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.

BACKGROUND: The identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers. RESULTS: Signature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes; (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly; (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets; and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions. CONCLUSION: Here we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research.