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PREDICTION EQUATIONS AND CHAOS THEORY PHASE-SPACE MODELING FOR A SEMICONDUCTOR MANUFACTURING PERFORMANCE IMPROVEMENT
No full textSemiconductor industry's Automatic Test Equipment's (ATE) unpredictable
performance in testing microchip units is partly caused by variability, an inherent manufacturing process characteristic that inspires this research. The research
objective is to explore the degree of variability in ATE performance and model the behavior to improve equipment capacity forecasting in the semiconductor
manufacturing industry. A relationship-based research was designed using five different ATEs dataset comprising 6 variables: Production Time (PT), Idling Time (IT), Repair Time (RT), Awaiting Technician Time (AWTT), Setup Time (ST) and Engineering Time (ET) parameters as input variables, and Production Yield (PY) as
output variable. Initially, a manufacturing simulation algorithm imitating a 12-hour
shift process running 10000 microchip units with 3. 80 seconds/unit process time
and normalized IT, RT, AWTT, ST, and ET was developed and executed in Arena simulation software to establish ATE performance variability. Secondly, the Chaos
Theo1y Phase-Space diagrams were developed to visualize chronologically ordered
ATE performance variability in an arbitrary phase-space domain. Thirdly, Linear
and Polynomial Regression methods using scatterplot, homoscedasticity, normality, p, F-statistic and R Square curve fitting assessments were used to develop and
validate 29 prediction equations which link equipment usage (input) to production yield (output). All these equations were solved to acquire maximum Production Yield. Fourthly, the resulting model is validated using the industry's Machine Utilization (MU) capacity equation, with 10% initial Protective Capacity, buffer capacity for production. The manufacturing simulation analysis demonstrated a manufacturing bottleneck where 84 out of 10826 microchip units spent up to 5 .3 591 minutes in the process against average 0.7474 minutes with 95.23% ATE utilization rate. The Phase-Space diagrams analysis illustrated that PY vs. PT and PY vs. ET diagrams imitate period two attractor of chaos theory or predictable (non-chaotic) patterns. IT, RT, AWTT, and ST demonstrate varying degree of chaotic (unpredictable) equipment performance. Linear and Polynomial Regression analysis shows PY vs. PT behaves Linearly Positive, where PT increases, PY increases. PY vs. AWTT, RT, ST, and ET, act Linearly Negative, where AWTT, RT, ST, and ET increases, PY decreases. PY vs. IT exhibits chaotic behavior. By solving these equations to acquire Maximum PY, more accurate figures like Idling Time Maximum can be substituted into the capacity model. Together with the Protective Capacity removal from MU, 9% - 10% Run Rate increase can be achieved equipment-wide, which improves equipment capacity forecast. The 'AcknowledgeVisualize-Model-Validate' model initially developed for the semiconductor-testing industry can be applied to other industry's process variability
EXPERIMENTAL STUDY OF EFFECT ON DIFFERENT TOOL ROTATIONAL AND TRANSVERSE SPEED FOR UNDERWATER FRICTION STIR WELDING (UFSW) OF AA 5083 ALUMINUM ALLOY
No full textFriction Stir Welding (FSW) is an advanced solid state joining process to join metals without reaching its melting point. The quality and strength of the joint is dependent on welding conditions such as tool rotational, transverse speed and tool geometry to generate the required amount of heat during the process. This research conducted to study and obtains the suitable rotational and transverse speed for the best quality of joining by the UFSW. The research involved in design and fabricated the customized fixture to retrofit the support system on the table of the universal milling machine for conducting the experiments. The experiment was conducted in underwater and dry condition with different tool rotational and transverse speed. The objectives of this research are to study and investigate the effect of the different temperature produced during the welding process on the quality of the joining in underwater condition. The result from this research indicated the temperature distribution and properties of AA 5083 welded specimens are highly influenced by UFSW method. It is also shown the influence of the water tends to decrease the heat input and thus avoiding the deterioration of the mechanical properties of the joint. Hence, by controlling the tool rotational and transverse speed, the joint can achieve good mechanical properties without creating any defect. It is also found that the combination of the tool rotational and transverse speeds 1700 RPM @ 67mm/min exhibited superior mechanical and tensile properties for UFSW. Besides, the fracture location ofUFSW was found in Weld Nugget Zone (WNZ) while the FSW fracture location was found in Heat Affected Zone (HAZ). In addition to that, the microstructure of these two methods also has the differences as the grain size in UFSW is much smaller compared to the FSW in each region
