# Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology
## 2 Literature Review
[9] adopted LR and ridge regression for developing the VM prediction model base on a dataset collected from CVD, thermal oxidation, coating, and lithography process. The VM target is a particular quality performance after lithography. The results show that ridge regression outperforms LR in most cases for the dataset used. [PERSON] et al. [10] proposed a semi-supervised support vector regression (SS-SVR) method based on self-training. A semiconductor manufacturing dataset was collected from the photo process of a South Korean semiconductor manufacturing company. The experimental results show that the proposed SS-SVR method demonstrated excellent performance for a real world semiconductor manufacturing dataset. [PERSON] et al. [11] proposed an adaptive methodology based on the group method of data handling (GMDH) type polynomial neural networks for predicting the material removal rate for the chemical-mechanical planarization process in semiconductor fabrication. The validation results report improved accuracy in comparison with several candidate methods. Some VM-related research deals with applications in TFT-LCD. [PERSON] et al. [12] proposed a novel quality prognostics scheme (QPS) for plasma sputtering in TFT-LCD manufacturing processes. The QPS consists of a conjecture model which can estimate the current-lot quality based on the current lot processing parameters and a prediction model which can predict the quality of the next lot according to the current lot conjecture value and the quality measurement data of several previous lots. NN and weighted moving average algorithms were applied to construct the QPS. The test results show that the QPS can be feasible and can conjecture/predict the product quality efficiently and effectively. [PERSON] et al. [13] evaluated various VM algorithms, including BPNN, simple recurrent neural networks (SRNN), and MR. According to the test in fifth generation TFT-LCD chemical-vapor deposition processes, the results show that both one-hidden-layered BPNN and SRNN VM algorithms achieve acceptable conjecture accuracy and meet the real-time requirements. [PERSON] et al. [14] defined the VM automation level, proposed the concept of automatic virtual metrology (AVM), and developed an AVM system which has been successfully deployed in a fifth-generation TFT-LCD factory in Chi Mei Optoelectronics (CMO), Taiwan. [PERSON] et al. [15] proposed an AVMS architecture which can be a useful reference for TFT-LCD manufacturing companies. In addition to semiconductor and TFT-LCD manufacturing, applications of VM in other industries can be found in the literature. [PERSON] et al. [16] proposed an approach to apply the AVM system factory-wide in wheel machining automation to achieve the total inspection of all the precision items of wheel machining automation in a mass production environment. [PERSON] et al. [17] applied VM for inspecting machining precision of machine tools. The test results of machining standard workpieces and cellphone shells of two three-axis CNC machines show that the proposed approach of applying VM to accomplish total precision inspection of machine tools is promising. [PERSON] et al. [18] proposed a scheme to apply AVM for carbon-fiber manufacturing to achieve online and real-time total inspection. [PERSON] et al. [19] developed a novel co-training technique, namely partial Bayesian co-training (PBCT) for a particular semi-supervised learning problem. PBCT was validated on three industrial applications: Gun-drilling, inkjet printing, and low-noise amplifier. The experimental results show that under a reduction of labeled data by up to 50%, a robust estimation is still attainable. The problem that the present study addresses was not found in the literature. The review of the literature shows that VM has been studied in some industrial settings, including CCL, but the further application of integrating with parameter adjustment, which can provide an opportunity to reduce the number of setups or setup time, is not found in the literature.