Diagnosis of semiconductor manufacturing equipment and processes

Saxena, Sharad; Unruh, Amy

doi:10.1109/66.286857

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…Extracted from the equipment models and measurement values, it corresponds to how well the predicted output(s), assuming a hypothetical input change, matches the measurements. It is similar to the algorithms used by May [8], and Saxena and Unruh [10], but instead of assigning the probability directly to a faulty input, our algorithm assigns the probability to the evidence that a faulty input could have caused the problem. Then, if multiple inputs could each have caused the problem, the diagnostic system will calculate the probability of each one, depending on their past frequency (5).…”

Section: Number Of Faulty Measurements Total Number Of Measurementsmentioning

confidence: 96%

“…It is very powerful when these models are physically based and well known, because deep level diagnostic systems can find the root cause of the problem by deriving it from the theory of the domain [5], [12], [15]. If the domain model is empirical, this method is not as powerful because the evidence must lie within the experimental range of the model [8]- [10]. In either case, however, a deep level diagnostic system is still very desirable because it can find the proper solutions in unanticipated situations.…”

Section: A Deep Versus Shallow Knowledge Base Approachesmentioning

confidence: 99%

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A general equipment diagnostic system and its application on photolithographic sequences

Leang

Spanos

1997

IEEE Trans. Semicond. Manufact.

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This paper presents a general diagnostic system that can be applied to semiconductor equipment to assist the operator in finding the causes of decreased machine performance. Based on conventional probability theory, the diagnostic system incorporates both shallow and deep level information. From the observed evidence, and from the conditional probabilities of faults initially supplied by machine experts (and subsequently updated by the system), the unconditional fault probabilities and their bounds are calculated. We have implemented a software version of the diagnostic system, and tested it on real photolithography equipment malfunctions and performance drifts. Initial experimental results are encouraging.

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Section: Number Of Faulty Measurements Total Number Of Measurementsmentioning

confidence: 96%

Section: A Deep Versus Shallow Knowledge Base Approachesmentioning

confidence: 99%

A general equipment diagnostic system and its application on photolithographic sequences

Leang

Spanos

1997

IEEE Trans. Semicond. Manufact.

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“…The Js determined by (5) and (6) appear in the last row in Table 4. In Table 4, the J = 1 for the first test pattern means that the fault is mainly attributed to a variation in the source power, which is clearly represented in the corresponding actual pattern (0.3, 0, 0, 0) shown in the second row.…”

Section: Fault Sensitivitymentioning

confidence: 99%

“…Wafer measurements such as an etch rate or a deposition rate, meanwhile, have been used to identify process faults [1][2][3][4][5]. Diagnostic systems relying upon ex situ measurements are somewhat limited in that they can be applied only to in-line fault detection.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of plasma processing equipment using neural network recognition of wavelet-filtered impedance matching

Kim

2005

Microelectronic Engineering

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“…During the MMST demonstration, the process control system was responsible for invoking the equipment-level diagnosis techniques described in [9]. Although the diagnosis system was designed to be an independent system that can operate independently if necessary, it was designed with the same automatic component activation framework used for process control.…”

Section: Selecting Process Control Modelsmentioning

confidence: 99%

A control-to-target architecture for process control

Sullivan

Butler

Hirsch

et al. 1994

IEEE Trans. Semicond. Manufact.

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In this paper, we present an architecture for a run-to-run supervisory process control system that allows the engineer to tailor the form of control for specific processes. The architecture supports different degrees of control, from model-based control to statistical process control to diagnosis. The architecture is compatible with different techniques for model optimization, data acquisition and analysis, and model adjustment and feedback. A primary feature of this architecture is that engineers can define processes in terms of their desired effects, and use process models that transform those effects into machine settings. We have used object technology as the basis for our design and implementation of the architecture. Object-oriented modeling provides the flexibility required to support the varying degrees of control required in a large-scale manufacturing facility. In this paper, we define the components of the architecture, and describe in detail a process control system that was built with this architecture and used in the Computer Integrated Manufacturing (CIM) system built for the Microelectronics Manufacturing Science and Technology (MMST) demonstration facility. Although the architecture was developed for the purpose of controlling semiconductor manufacturing processes, the principles behind the architecture may be applied to the control of any process.

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Diagnosis of semiconductor manufacturing equipment and processes

Cited by 8 publications

References 15 publications

A general equipment diagnostic system and its application on photolithographic sequences

A general equipment diagnostic system and its application on photolithographic sequences

Diagnosis of plasma processing equipment using neural network recognition of wavelet-filtered impedance matching

A control-to-target architecture for process control

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