The 3D extension of High Efficiency Video Coding (3D-HEVC) introduce Depth Modeling Mode (DMM) and 35 conventional intra modes to enhance the quality of coding, while bringing unacceptable computational complexity during the process of rough mode decision (RMD) and most probable mode (MPM). In this paper, we proposed a fast mode decision algorithm for texture map and depth map coding based on gradient information. Firstly, analyzing the characteristics of predict units (PU) in different intra mode applications that obtain the lowest RD-cost,then extracting the gradient information of PU to classify the PU into three types of gradient blocks and selecting appropriate candidate modes for the PU,thereby avoiding search each mode in coding process and skip the process of RMD and MPM early. Experimental results show that the proposed algorithm can achieve average 30.6% time saving with negligible reduction of coding performance.

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem.

For the problem that the fixed equipment grouping on the semiconductor packaging line cannot achieve the dynamic matching with the processing capacity of the processing task, this paper applies the graph theory method to the equipment grouping, and uses the topological structure of the graph theory to establish the equipment matrix. Also it uses the adjacency matrix to generate the relationship matrix between devices. And the device is grouped with closed position constraints and matching constraints between device types and processing types. A semiconductor bonding device grouping model based on processing task matching is established. The grouping model is simulated under different processing tasks, and it is verified that the equipment grouping model can realize dynamic grouping.