An Extended IDM Business Model to Ensure Time-to-Quality in Semiconductor Manufacturing Industry

Shahzad, Muhammad Kashif; Hubac, Stéphane; Siadat, Ali; Tollenaere, Michel

doi:10.1007/978-3-642-24358-5_12

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…For instance, failure analysis in engineering systems has been introduced in the literature, which contributed to support for diagnosis, prognosis and maintenance in an APS. 22,23 Many others present failures that are classified based on the following characteristics: failure mode, 24 characteristics of the production system, 25 and expert experiments 26 . An another classification method is root cause analysis, which is a process designed for use in investigating and categorizing the root causes of events involving safety, health, environment, quality, reliability and product impacts.…”

Section: Failure Sources and Their Relationship With The Operating Partmentioning

confidence: 99%

“…semiconductors and automotive industries) studies have shown that the products have no impact on humans. 23,24 Thus, we have taken the restrictive but not generalizable assumption that the product does not have an effect on humans. Based on this, the causal graph in the production process is presented in Figure 7.…”

Section: Failure Sources and Their Relationship With The Operating Partmentioning

confidence: 99%

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Fault diagnosis for the complex manufacturing system

Nguyen

Duong

Zamaï

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part O:

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Present manufacturing systems are equipped with sensors that provide a basis for real-time monitoring and diagnosis; however, placement of these sensors is constrained by the the functions that they perform and the system's structure. Moreover, sensors cannot be placed across all components in the equipment due to significant data challenges. This results in non-observable components, which limit our ability to support effective real-time monitoring and fault diagnosis initiatives. Consequently, product quality drifts found during inspection often result in unscheduled breakdown of all equipment involved in respective production operation. This situation becomes more complex for automated manufacturing lines, where success depends on our ability to capitalize maximum production capacities. This paper proposes a methodology that exploits historical data over unobserved equipment components to reduce the search space of potential faulty components, followed by a more accurate diagnosis of failures and causes. The purpose is to improve the effectiveness and efficiency of both the real-time monitoring of potential faulty components and the diagnosis of causes. In the proposed approach, we use a logical diagnosis approach to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network. The proposed approach computes the risk priority for suspected equipment with corresponding factors (such as human factor and recipe), using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins in real-time and support effective corrective maintenance decisions. The key advantages offered by this approach are: (i) reduced unscheduled equipment breakdown duration, and (ii) stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic methodology and can be deployed on fully or semi-automated manufacturing systems.

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Section: Failure Sources and Their Relationship With The Operating Partmentioning

confidence: 99%

Section: Failure Sources and Their Relationship With The Operating Partmentioning

confidence: 99%

Fault diagnosis for the complex manufacturing system

Nguyen

Duong

Zamaï

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…These are installed in almost all the equipment around us ranging from dish washer, microwave ovens and flat screens to office equipment. The sales revenues in the SI are characterized by cyclic demand patterns and positive compound annual growth rate (CAGR) of 8.78% (Shahzad, Hubac, Siadat, & Tollenaere, 2011). The demand for ICs is mainly driven by end user markets from the electronics industry (EI) e.g.…”

Section: Introductionmentioning

confidence: 99%

“…It is 8% of total SI market but is expected to dominate in the future (Shahzad, 2012). Therefore, demand is increasing not only in volume but also in diversity that led the emergence of high-mix low- 1 Retrieved from: http://maltiel-consulting.com/ Semiconductor Industry After Economic Upheaval maltiel semiconductor.pdf volume production environment and shortening product life cycles, in the SI (Shahzad et al, 2011). The success in the SI requires sustainable production capacities to cope up with associated challenges in this complex and highly competitive environment.…”

Section: Introductionmentioning

confidence: 99%

Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI)

Abu-Samah

Shahzad

̈ı

et al. 2020

IJPHM

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Increasing demand diversity and volume in semiconductor industry (SI) have resulted in shorter product life cycles. This competitive environment, with high-mix low-volume production,requires sustainable production capacities that can be achieved by reducing unscheduled equipment breakdowns. The fault detection and classification (FDC) is a well-known approach, used in the SI, to improve and stabilize the production capacities. This approach models equipment as a single unit and uses sensors data to identify equipment failures against product and process drifts. Besides its successful deployment for years, recent increase in unscheduled equipment breakdown needs an improved methodology to ensure sustainable capacities. The analysis on equipment utilization, using data collected from a world reputed semiconductor manufacturer, shows that failure durations as well as number of repair actions in each failure have significantly increased. This is an evidence of misdiagnosis in the identification of failures and prediction of its likely causes. In this paper, we propose two lines of defense against unstable and reducing production capacities. First, equipment should be stopped only if it is suspected as a source for product and process drifts whereas second defense line focuses on more accurate identification of failures and detection of associated causes. The objective is to facilitate maintenance engineers for more accurate decisions about failures and repair actions, upon an equipment stoppage. In the proposed methodology, these two lines of defense are modeled as Bayesian network (BN) with unsupervised learning of structure using data collected from the variables (classified as symptoms) across production, process, equipment and maintenance databases. The proofs of concept demonstrate that contextual or statistical information other than FDC sensor signals, used as symptoms, provide reliable information (posterior probabilities) to find the source of product/process quality drifts, a.k.a. failure modes (FM), as well as potential failure and causes. The reliability and learning curves concludes that modeling equipment at module level than equipment offers 45% more accurate diagnosis. The said approach contributes in reducing not only the failure durations but also the number of repair actions that has resulted in recent increase in unstable production capacities and unscheduled equipment breakdowns.

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“…Demand is continuously increasing not only in volume but also in diversity. This diversity has witnessed significant growth that ultimately leads to short product life cycles (Shahzad, Hubac, Siadat, & Tollenaere, 2011). The Figure 2 above presents equipment utilization for a thermal treatment (TT) workshop at the world reputed semiconductor manufacturer.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Integrated Failure-Cause Diagnosis with Bayesian Approach: Application to Semiconductor Manufacturing Equipment

ABU SAMAH,

SHAHZAD,

ZAMAÏ

et al. 2014

PHME_CONF

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Semiconductor Industry (SI) is facing the challenge of short product life cycles due to increasing diversity in customer demands. As a result, it has transformed into a high-mix low -volume production line that requires sustainable production capacities. However, significant increase in the unscheduled equipment breakdowns, limits its success. It is observed that in a high-mix low-volume production, product commonality is inversely proportional to failure occurrences and number of corrective actions in each failure. This provides evidence of misdiagnosis for equipment failures and causes. Moreover, equipment is believed to be the only source for product quality drifts that increase the unscheduled breakdowns and result in unstable production capacities. In this paper, we propose two defense lines against increasing unscheduled equipment breakdowns due to misdiagnosis. We argue that product quality drift can be traced to product itself, process and maintenance events, besides equipment. The Bayesian Belief Network (BBN) is proposed using symptoms, collected across drift sources, that improves equipment breakdown decisions by accurately identifying the source of product quality drift. The misdiagnosis of equipment failures and causes, if equipment is found as a source of drift, is another significant factor for increasing unscheduled equipment breakdowns. Existing failures and causes diagnosis approaches, in the SI, model equipment as a single unit and use fault detection and classification (FDC) sensor data. We also argue that these are the key reasons for the misdiagnosis because of neglected facts that production equipment is composed of multiple modules and FDC sensors undergo reliability issues in a high-mix low-volume production line. Therefore, to improve these misdiagnosis, another BBN is proposed that uses statistical information, collected from the equipment database, at the module level. These BBN models are evaluated in a thermal treatment (TT) workshop at the world reputed semiconductor manufacturer. The BBN model for the identification of the source of product quality drift (failure mode) demonstrates 97.8% prediction accuracy; whereas, module level BBNs for equipment failures and causes diagnosis are found 45.7% more accurate than equipment level BBN.

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An Extended IDM Business Model to Ensure Time-to-Quality in Semiconductor Manufacturing Industry

Cited by 4 publications

References 6 publications

Fault diagnosis for the complex manufacturing system

Fault diagnosis for the complex manufacturing system

Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI)

Methodology for Integrated Failure-Cause Diagnosis with Bayesian Approach: Application to Semiconductor Manufacturing Equipment

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