Wen-You Jhu, Ming-Yuan Chen, Hsien-Chie Cheng, Tao-Chih Chang, Yu-Min Lin, Ching-Kuan Lee, Hao-Che Kao
To meet the needs of millimeter wave (mmWave) communication applications in the 5G generation [1], it is necessary to integrate internal electronic components such as antennas, power amplifiers, and transceivers. Due to limited internal space constraints, there is an increasing demand for heterogeneous integration (HI) technologies like System-in-Package (SiP) [2], Antenna in Package (AiP) [3], and Antenna on Chip (AoC) [4]. However, HI technology faces several technical challenges, including warpage, thermal performance, and reliability that need to be addressed. Among them, the warpage caused by the coefficients of thermal expansion (CTE) mismatch between different components is particularly important. If the warpage is too large, it will lead to a deviation in the placement position during the subsequent antenna placement and chip bonding process, resulting in signal interference, empty soldering and short circuits, which can adversely impact the reliability verification process and product yield. Since the technology integrates several functional dies into a single package, the distribution of circuits in the redistribution layer (RDL) is extremely complex and has a dramatic impact on warpage behavior. Therefore, it is extremely important to accurately understand the thermomechanical behavior of RDL. In the past, most studies have used the finite element analysis (FEA) method to establish a process warpage model and verify it with experiments [4,5]. However, this method requires extensive knowledge of mechanics, finite element analysis skills, and considerable time for modeling and analysis. Due to the increasing complexity of packaging structures, development times are becoming longer. In order to shorten the time to market, it is necessary to develop a more efficient and accurate warpage analysis method for evaluating the process-induced warpage of package.
The purpose of this study is to investigate the warpage behavior during the fabrication process of an AiP module which is designed by industrial technology research institute (ITRI) using a multi-chip fan-out wafer level packaging (FOWLP) architecture. To achieve this goal, a process simulation framework combining FEA, effective modeling, and the element death/birth method is introduced. Meanwhile, a process warpage prediction model is constructed by combining process simulation and artificial neural network (ANN) algorithms to evaluate the package warpage quickly and effectively for design optimization [6-8]. Package warpage is strongly influenced by the RDL in the structure because it contains a large amount of complex copper (Cu) trace with a high Young's modulus and CTE. In order to effectively grasp the thermomechanical properties of RDL, the geometrical characteristics of complex Cu traces and vias in electronic computer-aided design (ECAD) are captured by the manual trace mapping technique, and the equivalent material properties of RDL are calculated through FEA [9]. It is worth noting that this method can treat RDL as an orthotropic as well as an anisotropic material constitutive assumption, and can obtain a complete elastic material matrix for more detailed and accurate process-warpage simulation [10]. To verify the effectiveness of this effective modeling method, axial tensile and heating tests are carried out on the RDL, and the displacement results are compared with those using the conventional volume-averaging method. Table 1 presents the displacement comparison results. Clearly, the method combined with the mapping technique provides better results than the volume-averaging method. This is due to the fact that the volume-averaging method ignores the effects of in-plane heterogeneity and material anisotropy on the mechanical properties. After calculating the equivalent material properties of the RDL, the RDL and the dielectric layer are further equivalent to a single block to reduce the complexity of the finite element model and improve the calculation efficiency while still maintaining the solution accuracy, as shown in Fig. 1. The 3D FE model of the multi-chip FOWLP structure is shown in Fig. 2 and the fabrication process of multi-chip FOWLP is shown in Fig. 3. The main process steps are the RDL process (Cu plating, and dielectric material coating), Die bonding, underfill curing, molding material curing, grinding, secondary RDL process and de-carrier, respectively. The validity of the numerical analysis model will be verified by comparison with experimental measurements.
Through parametric analysis, the influence of various geometric and material factors in FOWLP on process-induced warpage is explored, and key factors affecting warpage behavior are identified. These key factors will be further used to construct the database required for the warpage prediction model. The prediction accuracy and training performance of a prediction model depend on the degree of adaptation of its hyperparameters. Selecting appropriate hyperparameters can not only prevent overfitting, but also improve model convergence and prediction accuracy. Therefore, this study will adjust the hyperparameters in the predictive model through the Taguchi method-based hyperparameter optimization [11, 12]. Through the use of a reliable prediction model, developers are able to assess warpage effectively and efficiently during the initial design phase, which will enable them to optimize the design to reduce warpage, and facilitate subsequent processes such as the secondary RDL process.