Preliminary Study on Effect of Chemical Composition Alteration on Elastic Recovery and Stress Recovery of Nitrile Gloves

Nitrile gloves are widely used in the medical and automobile field due to its superiority in hypo-allergic component and chemical resistance over natural latex gloves. However, poor elastic recovery of nitrile glove to compressive force also creates an aesthetic issue for customers with high levels of wrinkling after removing from glove box. This paper demonstrates the preliminary study on the varies chemical composition such as crosslinking agents, sulphur and zinc oxide, the accelerator agent added during curing process, and the rubber filler Titanium Dioxide, on the elastic recovery and stress relaxation in nitrile gloves manufacturing. These chemical were studied at different concentration level comparing the high and low level versus the normal production range. Due to the inconsistency in the analysis technique on the surface imaging, the elastic recovery result was unable to be quantified and was not conclusive at this point. The cross linking agents, sulphur and zinc oxide, and the accelerator agent, played a significant role in the mechanical strength of the gloves. Increment of these chemicals result in higher tensile strength, but a reduction in the elasticity of the materials in which causes a lesser elongation at break percentage for the gloves. Both cross-linkers demonstrate different behaviour where higher sulphur content, provide higher stress relaxation (SR%) yet zinc oxide shows otherwise

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson’s correlation coefficient, r

Preliminary Study on Effect of Chemical Composition Alteration on Elastic Recovery and Stress Recovery of Nitrile Gloves

Nitrile gloves are widely used in the medical and automobile field due to its superiority in hypo-allergic component and chemical resistance over natural latex gloves. However, poor elastic recovery of nitrile glove to compressive force also creates an aesthetic issue for customers with high levels of wrinkling after removing from glove box. This paper demonstrates the preliminary study on the varies chemical composition such as crosslinking agents, sulphur and zinc oxide, the accelerator agent added during curing process, and the rubber filler Titanium Dioxide, on the elastic recovery and stress relaxation in nitrile gloves manufacturing. These chemical were studied at different concentration level comparing the high and low level versus the normal production range. Due to the inconsistency in the analysis technique on the surface imaging, the elastic recovery result was unable to be quantified and was not conclusive at this point. The cross linking agents, sulphur and zinc oxide, and the accelerator agent, played a significant role in the mechanical strength of the gloves. Increment of these chemicals result in higher tensile strength, but a reduction in the elasticity of the materials in which causes a lesser elongation at break percentage for the gloves. Both cross-linkers demonstrate different behaviour where higher sulphur content, provide higher stress relaxation (SR%) yet zinc oxide shows otherwise

A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective

Integrated circuits (IC) are fabricated on a wafer through stacked layers of circuit patterns. To ensure proper functionality, the overlay of each pattern layer must be within the tolerance. Inspecting each wafer’s overlay is unrealistic and impractical. Hence, wafers are selectively inspected at metrology stations through sampling strategies. With virtual metrology (VM), the metrology quality of the uninspected wafers can be estimated. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a VM to estimate the overlay of the photolithography process is envisioned. Past researches on overlay VM leveraged fault detection and classification (FDC) data to estimate the overlay errors. As such, for fabs in the progress of completing their FDC development for photolithography equipment, a different modeling approach is required to realize an overlay VM that sustains the production line until FDC data can be leveraged for VM. With practical gaps that must be addressed in real fabs, this paper focuses on realizing an overlay VM for the photolithography process without leveraging FDC data. Therefore, the objectives of this paper are two folds: First, to identify the research challenges towards realizing the overlay VM. Second, to propose the future research perspectives of the envisioned overlay VM. Based on the future research perspectives, a two-steps overlay VM modeling approach utilizing data mining techniques is proposed toward realizing the envisioned overlay VM system. The proposed approach first classifies the process stability at the wafer lot level, and subsequently, performs overlay error estimations for wafers in the wafer lots classified with stable process. Linear regression models are proposed to perform overlay error estimations in this work to augment the interpretability of the overlay VM

The Implementation of a Smart Sampling Scheme C2O Utilizing Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) is an enabling technology capable of performing virtual inspection on the metrology quality of wafers. Instead of physically acquiring the metrology measurements, VM applies conjecture models on the process data of wafers to estimate the measurements of the targeted metrology variables. Prior works on overlay VM system utilized fault detection and classification (FDC) data as the process data for the conjecture models. Hence, when FDC data are unavailable owing to FDC system enhancement works, FDC-based VM models would be rendered inefficacious. During such events, a competent VM system using a different modeling approach is required to sustain the production line until FDC data resumes availability and FDC-based VM reaches production state. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a novel wafer lot-level modeling approach for overlay VM was proposed in our prior work. Using the proposed modeling, a smart sampling scheme was also designed in the same work. The smart sampling scheme consists of two conjecture tasks, with the first task classifies the overlay quality of the wafers, and the second task estimates the overlay errors of the wafers classified with normal overlay quality. The abnormal ones are diverted to the physical metrology station. In this paper, the implementation of a smart sampling system, C2O, using the designed scheme and its experimental results are presented. The experimental results showed that C2O is capable to achieve a true positive rate (TPR) of 71.34% for the classification task and mean absolute scaled error (MASE) of 9.59 for the regression task. The obtained results set the baseline to measure the efficacy of future enhancement works, which have been enlisted and underway to augment the performance of the system so that its competency meets the requirements of real fab