In this article, a high-efficient design method of through silicon via (TSV) array for thermal management of 3-dimensional (3-D) integrated system is developed based on the equivalent thermal conductivity (ETC) model and the particle swarm optimization algorithm. Due to the anisotropy of TSV, the ETC model along the vertical direction is established, and the finite element method (FEM) is utilized to validate the accuracy of the ETC model. The relative error of the peak temperature between FEM and ETC model is less than 1.27%, and the average computational time of ETC model is greatly decreased from 261 to 13.3 s. In addition, the TSV array for heat management of 3-D integrated system is investigated and designed by the developed method. According to the designed scheme, the peak and average temperatures of the FEM based on $6\times 6$ TSV array are 314.60 and 303.84 K, which will agree with the desired indexes (315 and 310 K). The temperature differences of top and bottom surfaces are all less than 4.3 K, which implies that the thermal distribution of the 3-D integrated system is homogeneous. Therefore, the developed design method can efficiently design TSV array to achieve relatively low and uniform thermal distribution, and it can be applied in the 3-D integrated heat management system.

Ultrawideband (UWB) technology is the potential indoor positioning method, which is based on the time of arrival (TOA) principle and Kalman filter (KF) algorithm. In this research, a high-precision positioning method is developed by the modified particle swarm optimization (PSO) algorithm to improve the indoor 3-D positioning accuracy. The modified PSO algorithm is utilized to determine the optimal parameters of KF algorithm according to the prepositioned correction points. In addition, the effects of the number of prepositioned correction points on the positioning accuracy are systematically investigated, and the positioning accuracy is the highest when the number of prepositioned correction points is 8. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the traditional positioning method are 21.28 and 9.96 cm, while those of the developed method are 19.02 and 8.45 cm, respectively. Therefore, the positioning accuracy can be improved by the developed method, and it has great potential in the high-accuracy positioning for the complex indoor environment.

In this article, an intelligent design method of thermal through silicon via (TTSV) for thermal management of a Chiplet-based system is proposed based on the finite element method (FEM), back propagation-neural network (BP-NN) model, and particle swarm optimization (PSO) algorithm. In order to analyze the effect of design parameters of TTSV (TTSV pitches, radius of TTSV unit, and thickness of oxide layer) on the thermal distribution of a Chiplet-based system, the detailed model is simulated by FEM. The mapping relationships between the design parameters and thermal distribution indices are described by BP-NN models according to the simulation data. In addition, according to the desired performance of the Chiplet-based system with TTSV, the multiobjective optimization criterion is formulated, and then the design parameters are optimized by the modified PSO algorithm. Based on the obtained design parameters, the FEM simulation is aimed at validating the effectiveness of the proposed method. The simulated peak temperatures (PTs) of three layers (340.81, 334.76, and 314.85 K) well agree with the desired ones (340, 335, and 315 K), which implies that the proposed method can effectively optimize the design parameters of TTSV to control the thermal distribution of Chiplet-based system. Therefore, the proposed intelligent design method can achieve the thermal management of the complex Chiplet-based system.

In order to meet the requirements of high performance, miniaturization, low cost, low power consumption and multi-function, three-dimensional (3D) integrated technology has gradually become a core technology. With the development of 3D integrated technology, it has been used in imaging sensors, optical integrated microsystems, inertial sensor microsystems, radio-frequency microsystems, biological microsystems and logic microsystems, etc. Through silicon via (TSV) is the core technology of a 3D integrated system, which can achieve vertical interconnection between stacked chips. In this paper, the development and progress of multi-physics simulation design for TSV-based 3D integrated systems are reviewed. Firstly, the electrical simulation design of TSV in a 3D integrated system is presented, including the lumped parameters model-based design and numerical computation model-based design. Secondly, the thermal simulation design of TSV in a 3D integrated system is presented based on the analytical model or numerical computation model. Thirdly, the multi-physics co-simulation design of TSV in a 3D integrated system is presented, including the thermal stress and electron thermal coupling simulation design. Finally, this paper is concluded, and the future perspectives of 3D integrated systems are presented, including the advanced integrated microsystems, the crossed and reconfigurable architecture design technology and the standardized and intelligent design technology.

The intelligent codesign strategy is proposed to balance the thermal management and cost control for 3-D integrated system with thermal through-silicon via (TTSV). The complex relationship between the thermal performance indexes and parameters of TTSV in the 3-D integrated system is investigated by the COMSOL Multiphysics software. According to the design logic, the row/column number of TTSV array and the distance of two TTSV unit are the global parameters, while the thickness of oxide layer, the radius of TTSV, and the height of oxide insulating layer are the local parameters. In order to improve the simulation efficiency, the modified neural network models are established based on the simulation data to replace the finite-element model. In the proposed codesign strategy, the thermal management and cost control are considered in the multiobjective evaluation function. According to the particle swarm optimization (PSO) algorithm, the multilevel PSO algorithm is developed to optimize the global and local parameters of TTSV. The effectiveness of the proposed codesign strategy is verified by six cases of two testbeds. The simulated temperature indexes are all lower than the desired targets (360, 320, and 20 K), and the number of TTSVs is decreased at the same time. Therefore, the proposed codesign strategy can not only realize the excellent thermal management but also achieve a compromise between thermal management and cost control.

In this research, a high-efficiency design method of the capacitive MEMS accelerometer is proposed. As the MEMS accelerometer has high precision and a compact structure, much research has been carried out, which mainly focused on the structural design and materials selection. To overcome the inconvenience and inaccuracy of the traditional design method, an orthogonal design and the particle swarm optimization (PSO) algorithm are introduced to improve the design efficiency. The whole process includes a finite element method (FEM) simulation, high-efficiency design, and verification. Through the theoretical analysis, the working mechanism of capacitive MEMS accelerometer is clear. Based on the comparison among the sweep calculation results of these parameters in the FEM software, four representative structural parameters are selected for further study, and they are l_e, n_f, l_f and w_PM, respectively. l_e and l_f are the length of the sensing electrode and fixed electrode on the right. n_f is the number of electrode pairs, and w_PM is the width of the mass block. Then, in order to reduce computation, an orthogonal design is adopted and finally, 81 experimental groups are produced. Sensitivity S_V and mass M_a are defined as evaluation parameters, and structural parameters of experimental groups are imported into the FEM software to obtain the corresponding calculation results. These simulation data are imported into neural networks with the PSO algorithm. For a comprehensively accurate examination, three cases are used to verify our design method, and every case endows the performance parameters with different weights and expected values. The corresponding structural parameters of each case are given out after 24 iterations. Finally, the maximum calculation errors of S_V and M_a are 1.2941% and 0.1335%, respectively, proving the feasibility of the high-efficiency design method.