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This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large. [ABSTRACT FROM AUTHOR]

Chip test escapes can pass the complete test procedure but fail prematurely in system applications. Statistical testing methods can screen chip test escapes by analyzing test data without additional physical measurements. This paper proposes a screening method to reduce chip test escapes using multi-correlation analysis of parameters. This method takes the strength of locational correlation or structural correlation as criterion information. The main steps of the method are as follows: first, quantifying the strength of locational correlation or structural correlation of the chip parameters, then verifying the statistical distribution of the characterization and setting the screening limit, and finally, screening the chip test escapes. Silicon carbide junction barrier Schottky diode (JBS) measurement data are used to verify the method. The results show that when the location information of the chip on the wafer is known, the screening effect based on locational correlation is significantly better than the method specified in AEC Q101. Without additional physical measurements, screening methods based on structural correlation can further identify the remaining chip test escapes in single-parameter screening. This screening method can be used to supplement the existing measurement technology to reduce the cost of packaging, testing, and aging testing. [ABSTRACT FROM AUTHOR]

Amorphous InGaZnO (a-IGZO) is a candidate material for thin-film transistors (TFTs) owing to its large electron mobility and good uniformity over large area. a-IGZO TFTs drain current models are essential for further pushing both a-IGZO TFTs technology and circuit design. In this paper, we propose a simple physical-based and analytical model of the drain current of a-IGZO TFTs. The model is valid in both non degenerate and degenerate conduction and it accounts for deep interface states, tail localized states, and free delocalized band states. The model is validated with the measurements of both coplanar and staggered a-IGZO TFTs. It provides key physical and material parameters of the transistor and, owing to its class C∞ formulation, it can be straightforwardly implemented in circuit simulators. [ABSTRACT FROM PUBLISHER]