US semiconductor manufacturer selects Ovivo's UPW system

2016 ◽  
Vol 2016 (4) ◽  
pp. 4
Keyword(s):  
Semiconductor Manufacturer

Enhanced Failure Analysis (FA) of Organic Contamination Using Submicron Simultaneous IR and Raman Spectroscopy: Breakthrough Developments of Optical Photothermal IR (O-PTIR)

2020 ◽  
Author(s):  
D. Jay Anderson ◽  
Mustafa Kansiz ◽  
Michael Lo ◽  
Eoghan Dillon ◽  
Curtis Marcott
Keyword(s):  
Raman Spectroscopy ◽  
Failure Analysis ◽  
Semiconductor Manufacturing ◽  
Rapid Identification ◽  
Organic Contamination ◽  
Premature Failure ◽  
Specific Element ◽  
Ir And Raman Spectroscopy ◽  
Semiconductor Manufacturer ◽  
Ir And Raman

Abstract Rapid identification of organic contamination in the semi and semi related industry is a major concern for research and manufacturing. Organic contamination can affect a system or subsystem’s performance and cause premature failure of the product. As an example, in February 2019 the Taiwan Semiconductor Manufacturing Company (TMSC), a major semiconductor manufacturer, reported that a photoresist it used included a specific element which was abnormally treated, creating a foreign polymer in the photoresist resulting in an estimated loss of $550M [1].

Assessment on Copper C194 Mechanical Properties Variations in Typical Semiconductor Assembly Production Line

2012 ◽  
Vol 576 ◽  
pp. 523-526
Author(s):  
Abdullah Rashid Amirul ◽  
Saad Nor Hayati ◽  
Bulan Abdullah
Keyword(s):  
Mechanical Properties ◽  
Production Line ◽  
Defect Group ◽  
Control Group ◽  
Lead Frame ◽  
High Expectation ◽  
Bond Yield ◽  
Defect Groups ◽  
Semiconductor Manufacturer ◽  
Assembly Production

Process variation is inevitable for any production line regardless of the industry. The trend for smaller, lighter yet multifunctional devices has created high expectation for the semiconductor manufacturer to produce more robust and highly reliable devices. One way to achieve this is by assessing the variance performance of the assembly production. In this study, the mechanical properties of copper alloy C194 used as the lead frame for particular IC device have been investigated. Samples from control and defect groups been subjected to hardness (Rockwell test) and tensile (Instron test) while the optical microscopy used to verify the microstructure. The result shows that the hardness and tensile of the defect group is relatively lower than the control group while the elongation of the defect group is almost 10% higher. This finding is very useful to be shared with the process owner so that in-depth investigation on the lead frame material consistency or the temperature range optimization can be carried out to prevent such variations that contribute to the inconsistence wire bond yield performance.

scholarly journals Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 1051-1056 ◽  
Author(s):  
Lukas Lingitz ◽  
Viola Gallina ◽  
Fazel Ansari ◽  
Dávid Gyulai ◽  
András Pfeiffer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lead Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Time Prediction ◽  
Semiconductor Manufacturer

A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory

2012 ◽  
Vol 2 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Cycle Time ◽  
Back Propagation ◽  
Principal Component ◽  
Time Estimation ◽  
Cycle Times ◽  
Forecasting Performance ◽  
Semiconductor Manufacturer ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

scholarly journals Use of Best Value Model to Achieve Sustainability: A Case Study on a Semiconductor Manufacturer

2016 ◽  
Vol 145 ◽  
pp. 746-751
Author(s):  
Rick Corea ◽  
Dean Kashiwagi ◽  
Dhaval Gajjar ◽  
Sylvia Romero
Keyword(s):  
Best Value ◽  
Semiconductor Manufacturer ◽  
Value Model
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