A neural network approach for classifying test structure results

Khera, D.; Zaghoul, M.E.; Linholm, L.W.; Wilson, Charles L.

doi:10.1109/icmts.1989.39309

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Advanced data analysis techniques based on machine learning [43], neural nets [44] and expert systems [45] are being investigated. They can address user goals in a more direct and consistent manner with varying degrees of success.…”

Section: 31mentioning

confidence: 99%

Review of semiconductor microelectronic test structures with applications to infrared detector materials and processes

Kopanski¹,

Schuster²

1993

Semicond. Sci. Technol.

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The impact of microelectronic test structures, as they have been apDlied to silicon integrated circuits (ICS) and gallium arsenide monolithic microwave integrated circuits (MMICS), is reviewed. General principles for the use of test structures with possible applications to infrared (IR) detector technology based on HgCdTe and other materials are emphasized. The uses of test structures for Si and GaAs, test chip design methodology and some examples of how test structures have been applied for process control and to increase yield are discussed. Specific test structures and techniques that have been applied to IR detectors are also reviewed. The basic design considerations and measurements possible with each class of test structure are discussed. The important experience of the Si and GaAs industries, applicable to IR detectors, is that significant yield improvement is possible with improved process control using test structures. Increased research efforts to expand the apolications of test structures to IR detector manufacture are indicated. ' *Contribution of the National Institute oi Standards and Technology: not subject to copyright.

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Section: 31mentioning

confidence: 99%

Review of semiconductor microelectronic test structures with applications to infrared detector materials and processes

Kopanski¹,

Schuster²

1993

Semicond. Sci. Technol.

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Artificial Intelligence Techniques for Analysis: Expert Systems and Neural Networks

Griesmer¹,

Kierstead

Rothman

1993

Microelectronics Manufacturing Diagnostics Handbook

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Development of Smart Semiconductor Manufacturing: Operations Research and Data Science Perspectives

Khakifirooz

Fathi

2019

IEEE Access

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With advances in information and telecommunication technologies and data-enabled decision making, smart manufacturing can be an essential component of sustainable development. In the era of the smart world, semiconductor industry is one of the few global industries that are in a growth mode to smartness, due to worldwide demand. The important opportunities that can boost the cost reduction of productivity and improve quality in wafer fabrication are based on the simulations of actual environment in Cyber-Physical Space and integrate them with decentralized decision-making systems. However, this integration faced the industry with novel unique challenges. The stream of the data from sensors, robots, and Cyber-Physical Space can aid to make the manufacturing smart. Therefore, it would be an increased need for modeling, optimization, and simulation for the value delivery from manufacturing data. This paper aims to review the success story of smart manufacturing in semiconductor industry with the focus on data-enabled decision making and optimization applications based on operations research and data science perspective. In addition, we will discuss future research directions and new challenges for this industry.

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A neural network approach for classifying test structure results

Cited by 3 publications

References 9 publications

Review of semiconductor microelectronic test structures with applications to infrared detector materials and processes

Review of semiconductor microelectronic test structures with applications to infrared detector materials and processes

Artificial Intelligence Techniques for Analysis: Expert Systems and Neural Networks

Development of Smart Semiconductor Manufacturing: Operations Research and Data Science Perspectives

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