In order to obtain favorable communication performance, impulsive noise needs to be suppressed which is a major interference in scenarios such as broadcasting system and power line communication. In this paper, a time-domain impulsive noise suppression method is combined with deep learning, whose parameters are calculated by neural networks. The parameters of the denoising method are of great significance because they have a direct influence on the performance of impulsive noise suppression. By constructing a proper training set, trained neural networks can avoid the huge cost compared with traversal algorithm for determining parameters. In the meanwhile, the networks are able to estimate adaptive parameters under various noise environment avoiding fixed parameters. Furthermore, The computing resources of terminal devices are usually insufficient for a large number of calculations. Therefore, the noise suppression algorithm embedded in terminal equipment is expected to be with low complexity and achieve superior effects. Simulation experiments are carried out concerning signal to interference plus noise ratio and data rate. Simulation results of the deep learning based impulsive noise suppression scheme validate that neural networks have favorable performance in predicting parameters for the denoising algorithm.

The high proportion of distributed photovoltaics (PVs) connected to distribution substation areas causes complex operation of the medium- and low-voltage distribution network. New power electronic devices represented by soft open point (SOP) can achieve a flexible interconnection between distribution substation areas. In this paper, a cooperative operation optimization method for medium- and low-voltage distribution networks considering flexible interconnected distribution substation areas is proposed. Firstly, interval affine is used to model the resource aggregation and obtain the flexibility region constraints of distribution substation areas. Then, a multi-scale cooperative operation framework for medium- and low-voltage distribution networks is constructed. The medium-voltage distribution network adopts the centralized method to solve and issue operation strategy instructions. The distribution substation area generates a voltage-reactive power adaptive regulation curve and performs intra-day rolling control. Finally, the case studies show that the proposed method has efficient operation strategy of medium- and low-voltage distribution networks and can alleviate the voltage fluctuation caused by high integration of PVs.

Hardware Trojans refer to additional logic maliciously implanted by attackers in integrated circuits (ICs). Because of the potential security threat of hardware Trojans, they have attracted extensive attention to security issues. As a formal verification method, property checking has been proved to be a powerful solution for hardware Trojan detection. However, existing property-checking methods are limited by the unity of security properties and the model explosion problem of formal models. The limitations above hinder the practical applications of these methods. To alleviate these challenges, we propose an effective property-checking method for hardware Trojan detection. Specifically, we establish the formal model based on the principle of finite state machine (FSM), and the method can alleviate the model explosion problem. For property writing, we extract the core behavior characteristics of hardware Trojans and then generate properties for the verification of certain types of hardware Trojans. Experimental results demonstrate that our approach is applicable to detect information leakage and denial of service (DoS) hardware Trojans by verifying security properties.

Recently, frequent data movement between computing units and memory during floating-point arithmetic has become a major problem for scientific computing. Computing-in-memory (CIM) is a novel computing paradigm that merges computing logic into memory, which can address the data movement problem with excellent power efficiency. However, the previous CIM paradigm failed to support double-precision floating-point format (FP64) due to its computing complexity. This paper presents a novel all-digital CIM macro-DCIM-FF to complete FP64 based fused multiply-add (FMA) operation for the first time. With 16 sub-CIM cells integrating digital multipliers to complete mantissa multiplication, DCIM-FF is able to provide correct rounded implementations for normalized/denormalized inputs in round-to-nearest-even mode and round-to-zero mode, respectively. To evaluate our design, we synthesized and tested the DCIM-FF macro in 55-nm CMOS technology. With a minimum power efficiency of 0.12 mW and a maximum computing efficiency of 26.9 TOPS/W, we successfully demonstrated that DCIM-FF can run the FP64-based FMA operation without error. Compared to related works, the proposed DCIM-FF macro shows significant power efficiency improvement and less area overhead based on CIM technology. This work paves a novel pathway for high-performance implementation of an FP64-based matrix-vector multiplication (MVM) operation, which is essential for hyperscale scientific computing.

This study demonstrates the achievement of robust Cu-Cu bonding strength through thermocompression bonding (TCB) under a formic acid (FA) atmosphere. When subjected to sintering at 300 °C for 1 min under FA, sintering joints exhibit an average shear strength of 50.9 MPa. This strength further increases to an average of 131 MPa when the sintering duration is extended to 20 min at the same temperature under FA. Molecular dynamics simulations are employed to model the sintering behavior of copper particles of various sizes and thus understand the diffusion mechanism. The analysis of mean square displacement (MSD) and radial distribution function from these simulations suggests that the presence of small particles aids in the sintering of large ones. A copper paste, formulated by mixing micron-sized copper particles with organic solvents, is utilized in a series of experiments to explore different sintering methodologies aimed at enhancing the mechanical integrity of the sintering joints while simultaneously addressing issues associated with copper particle oxidation. Innovative strategies, including redox processes, are applied to improve the shear strength of the sintering joints and to minimize the detrimental effects of oxidation on the copper particles. Results indicate that preoxidation, which was used to form a nano surface structure, and using an FA atmosphere, remarkably enhance the shear strength of the Cu-Cu joints created via TCB. The findings of this research are pivotal for the advancement of rapid Cu-Cu bonding techniques using micron-scale copper pastes and can have profound implications for the development of future electronic packaging and interconnection technologies.

With the development of high-density integrated chips, low-k dielectric materials are used in the back end of line (BEOL) to reduce signal delay. However, due to the application of fine-pitch packages with high-hardness copper pillars, BEOL is susceptible to chip package interaction (CPI), which leads to reliability issues such as the delamination of interlayer dielectric (ILD) layers. In order to improve package reliability, the effect of CPI at multi-scale needs to be explored in terms of package integration. In this paper, the stress of BEOL in the flip-chip chip-scale packaging (FCCSP) model during thermal cycling is investigated by using the finite-element-based sub-model approach. A three-dimensional (3D) multi-level finite element model is established based on the FCCSP. The wiring layers were treated by the equivalent homogenization method to ensure high prediction accuracy. The stress distribution of the BEOL around the critical bump was analyzed. The cracking risk of the interface layer of the BEOL was assessed by pre-cracking at a dangerous location. In addition, the effects of the epoxy molding compound (EMC) thickness, polyimide (PI) opening, and coefficient of thermal expansion (CTE) of the underfill on cracking were investigated. The simulation results show that the first principal stress of BEOL is higher at high-temperature moments than at low-temperature moments, and mainly concentrated near the PI opening. Compared with the oxide layer, the low-k layer has a higher risk of cracking. A smaller EMC thickness, lower CTE of the underfill, and larger PI opening help to reduce the risk of cracking in the BEOL.