A personal historical perspective of ion implantation equipment for semiconductor applications

McKenna, C.M.

doi:10.1109/iit.2000.924079

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…We test the proposed MC PdM methodology for replacing tungsten filaments used in Ion Implantation [13], one of the most important processes in semiconductor manufacturing fabrication. Ion Implantation is used to modify the electrical properties of wafers by injecting doping atoms.…”

Section: A Use Casementioning

confidence: 99%

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Susto

Schirru

Pampuri

et al. 2015

IEEE Trans. Ind. Inf.

643

223

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In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called 'health factors' or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

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Section: A Use Casementioning

confidence: 99%

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Susto

Schirru

Pampuri

et al. 2015

IEEE Trans. Ind. Inf.

643

223

View full text Add to dashboard Cite

show abstract

“…A N ION implanter [1] is a bottleneck machine in the semiconductor manufacturing process because of its expensiveness; thus, ion implantation is a critical operation for throughput. A damaged wafer due to the malfunction of the ion implanter is not reworkable, hence it significantly affects the yield.…”

Section: Introductionmentioning

confidence: 99%

“…The structure of an ion implanter is shown in Fig. 1 [1]. In general, the equipment supplier provides digital equipment to monitor the proper operation of the scanning subsystem of the machine.…”

Section: Introductionmentioning

confidence: 99%

“…The measured parameters can be, for example, filament voltage, filament current, discharge voltage, etc. However, there are several tens to hundreds of recipes 1 for wafer fabrication in a semiconductor foundry each day. Although the setting of a scanning subsystem is independent of the recipes, the other four subsystems' parameters may vary widely due to various recipes.…”

Section: Introductionmentioning

confidence: 99%

“…Collecting a large set of abnormal data patterns with known faults in an ion implanter is very difficult, because there are several hundreds of steps in fabricating a chip and the chip failure is most probably known when it is under test. To find out which step in the complete manufacturing process causes the failure is already difficult, 1 A recipe controls how settings are initialized or changed during a process step. Examples include recipe numbers which index tables of set points in furnaces or written instructions to operators.…”

Section: Introductionmentioning

confidence: 99%

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A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter

Lin

Horng

2006

IEEE Trans. Semicond. Manufact.

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We propose a classification-based fault detection and isolation scheme for the ion implanter. The proposed scheme consists of two parts: 1) the classification part and 2) the fault detection and isolation part. In the classification part, we propose a hybrid classification tree (HCT) with learning capability to classify the recipe of a working wafer in the ion implanter, and a k-fold cross-validation error is treated as the accuracy of the classification result. In the fault detection and isolation part, we propose a warning signal generation criteria based on the classification accuracy to detect and fault isolation scheme based on the HCT to isolate the actual fault of an ion implanter. We have compared the proposed classifier with the existing classification software and tested the validity of the proposed fault detection and isolation scheme for real cases to obtain successful results.

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Ion implantation in CMOS Technology: Machine Challenges

2006

Ion Implantation and Synthesis of Materials

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A personal historical perspective of ion implantation equipment for semiconductor applications

Cited by 10 publications

References 12 publications

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter

Ion implantation in CMOS Technology: Machine Challenges

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