Plasma diagnosis by recognizing in situ data using a modular backpropagation network

Kim, Byungwhan; Kim, Sungmo

doi:10.1016/s0169-7439(02)00137-5

Cited by 13 publications

(3 citation statements)

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“…The BPNN has been widely used to model complex plasma cleaning process in wafer manufacturing process [8,9], and it is the most extensively used learning algorithm that includes nodes with continuously differentiable activation functions. In this study, we employ a three-layered plasma cleaning BPNN that includes an input layer, a hidden layer and an output layer (shown as Figure 3).…”

Section: Plasma Cleaning Bpnn With Taguchi Methodsmentioning

confidence: 99%

“…The RMSE of plasma cleaning BPNN is governed by many training factors [9]. The plasma cleaning BPNN simulation was performed using MATLAB ® software.…”

Section: Plasma Cleaning Bpnn With Taguchi Methodsmentioning

confidence: 99%

“…Lee and Pan [12] applied Taguchi parameter design on plasma cleaning and reported that the effectiveness of plasma cleaning can increase the wire bonding strength of PBGA. In addition, ANN combined with genetic algorithm (GA) has been widely used to model complex plasma cleaning process and search for the optimal process parameters in wafer manufacturing process [8,9]. Su & Chiang [24] presented an integrated technique, using the back-propagation neural network (BPNN) to explore empirical models between wire bonding process parameters and strength response, and to optimize the process through certain parameter setting using GA for IC wire bonding process.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Plastic Ball Grid Array Assembly Yield: A Case Study

Hung¹,

Huang²

2006

Journal of the Chinese Institute of Industrial Engineers

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The plasma cleaning process cleaning the residual containments in the assembly processes is one of the major processes in the plastic ball grid arrays (PBGA) assembly processes. Especially, when there is any micron particle existed before wire bonding and molding, parts of nonconforming will increase. With the rapid growth in production, yield improvement to reduce the production cost is essential for PBGA industry. In fact, the plasma cleaning process before wire bonding and molding both enhances the PBGA yield and reduces the production cost effectively. This study combines neural network with Taguchi method to structure a well-trained prediction model, further searches for the optimal plasma cleaning parameter design through genetic algorithm, and finally enhances the process yield and production quality in a central Taiwan PBGA company.

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Section: Plasma Cleaning Bpnn With Taguchi Methodsmentioning

confidence: 99%

“…The RMSE of plasma cleaning BPNN is governed by many training factors [9]. The plasma cleaning BPNN simulation was performed using MATLAB ® software.…”

Section: Plasma Cleaning Bpnn With Taguchi Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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