RTD Response Time Estimation in the Presence of Temperature Variations and Its Application to Semiconductor Manufacturing

2008 ◽  
Vol 57 (2) ◽  
pp. 406-412 ◽  
Author(s):  
Woei Wan Tan ◽  
Reginald F. Y. Li ◽  
Ai Poh Loh ◽  
Weng Khuen Ho
Keyword(s):  
Response Time ◽  
Semiconductor Manufacturing ◽  
Time Estimation ◽  
Temperature Variations

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

A FUZZY-NEURAL FLUCTUATION SMOOTHING RULE FOR SCHEDULING JOBS WITH VARIOUS PRIORITIES IN A SEMICONDUCTOR MANUFACTURING FACTORY

2009 ◽  
Vol 17 (03) ◽  
pp. 397-417 ◽  
Author(s):  
TOLY CHEN ◽  
YU-CHENG LIN
Keyword(s):  
Cycle Time ◽  
Semiconductor Manufacturing ◽  
Optimal Solution ◽  
Time Estimation ◽  
Pareto Optimal Solution ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
Fluctuation Smoothing

A fuzzy-neural fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs with various priorities in a semiconductor manufacturing factory. The fuzzy-neural fluctuation smoothing rule is modified from the well-known fluctuation smoothing rule by improving the accuracy of estimating the remaining cycle time of a job, which is done by applying Chen's fuzzy-neural approach with multiple buckets. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study. According to experimental results, incorporating a more accurate remaining cycle time estimation mechanism did improve the scheduling performance especially in reducing the average cycle times. Besides, the fuzzy-neural fluctuation smoothing rule was also shown to be a Pareto optimal solution for scheduling jobs with various priorities in a semiconductor manufacturing factory.

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