Bacterial infection continues to be one of the biggest threats to human health. The therapeutic effect is significantly reduced as a result of the development of super-bacteria-resistant bacteria due to the overuse of antibiotics for conventional antimicrobial treatment. The emergence of drug-resistant bacteria makes it necessary to develop new antimicrobial treatments. Following the successful application of photothermal therapy in the field of oncology treatment, more and more researchers are applying it to antimicrobial treatments and achieving important results, especially in the treatment of drug-resistant bacterial infections. As a non-invasive anti-infection method, photothermal therapy has the advantages of broad-spectrum antimicrobial properties, a short treatment period, and low systemic impact. In the PTT, the antimicrobial efficacy strongly depends on the different laser properties and the choice of photothermal agents (PTAs). The purpose of this review is to discuss the most recent developments in photothermal nanomaterials that are antibacterial. We will delve into the antimicrobial principles and antimicrobial spectrum of different PTAs and provide a summary of current developments in synthesis, classification, structural features, physicochemical properties, and antibacterial performance research. The challenges and future prospects of enhanced antimicrobial performance and reduced cytotoxicity of PTAs are investigated. 

Nanomaterials-based photothermal therapies for antibacterial applications

Impedance spectroscopy and conduction mechanism analysis of bulk nanostructure Prussian blue pellets

Due to a variety of limitations on the system-on-chip (SoC), the microelectronics industry is now facing challenges and making slow progress in recent years. With architecture design and advanced packaging advantages, chiplet heterogeneous integration (CHI) systems have become a promising solution to long-lasting hardship. However, high power consumption in CHI systems generates massive heat and makes thermal design a demanding task. Therefore, an accurate tool for thermal simulation is indispensable in the design flow. In this article, a multiscale anisotropic thermal model is proposed for the CHI systems. It considers the feature-scale thermal conductivities of different materials to predict the package-scale steady-state temperature fields. Specifically, the local material composition and thermal conductivity of redistribution layers (RDLs) are extracted from design layout files by constructing an equivalent thermal conductivity algorithm of local feature structures. As for through silicon via (TSV) and bump arrays, the anisotropic distributions of thermal conductivity can also be derived with equivalent algorithms. Other structures are considered homogeneous blocks to significantly reduce the computational expense without losing the generality of the proposed model. Compared with the previous isotropic thermal model of CHI systems, the present multiscale anisotropic thermal model is proven to make temperature prediction and hotspot detection more reliable. With this tool, the reliability problems that are unpredictable and obscure for isotropic thermal models can be identified in advance, and more reasonable design space can be explored in the design flow of the CHI systems. 

A Multiscale Anisotropic Thermal Model of Chiplet Heterogeneous Integration System

Chiplet heterogeneous integration (CHI) is one of the important technology choices to continue Moore’s law. However, due to the characteristics of high power and low supply voltage in CHI systems, heavy currents need to flow through the power delivery network (PDN), and the Joule heating effect will result in the overall temperature increase of the CHI system. Meanwhile, the high temperature will cause the current as well as the performance of the system to degrade and a series of reliability problems will occur. In this article, an effective electrical-thermal coupling model is proposed to predict the steady-state temperature distribution of a 2.5-D CHI system considering the Joule heating effect and the temperature effect on the IR drop. The equivalent electrical conductivity model is also built up to describe the design features of the redistribution layer (RDL), bump, and through silicon via (TSV) structures based on the electrical-thermal duality. Furthermore, the governing equations for voltage distribution and temperature distribution are solved simultaneously by utilizing the finite volume method (FVM) with nonuniform mesh to realize the electrical-thermal co-simulation of the multiscale CHI system. The model application is further performed to investigate the influence of the model parameters on the voltage drop and temperature distribution of the CHI system. The verified systems and simulated results of the present investigation demonstrate the viability and accuracy of voltage and temperature field co-simulation and indicate that the new proposed electrical-thermal model is helpful in thermal and voltage drop analysis of packaging structures with the Joule heating effect and can be adopted to assist in the physical design optimization of 2.5-D CHI or 3-D heterogeneous stacked chips.

An Electrical–Thermal Co-Simulation Model of Chiplet Heterogeneous Integration Systems

At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them. When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria. This is because an inefficient center may result in unpredicted outcomes. Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers. We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure. They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices. The clustering accuracy was also conducted using Rand index criteria. All the algorithms computational complexity was analyzed and finally their computation time was also recorded. As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers. More specifically, the accuracy percentage of FECA is higher than the other techniques however, it‘s computational complexity and running time is moderately higher.

/pdf/efficient-intelligent-framework-for-selection-of-initial-ynojq8a3ql.pdf

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Efficient Transient Thermal Analysis of Chiplet Heterogeneous Integration

An efficient thermal model of chiplet heterogeneous integration system for steady-state temperature prediction

Cluster analysis is an important issue for machine learning and pattern recognition, and clustering algorithms are used to solve these problems. Owing to the disadvantage of the traditional clustering algorithms that need iteration calculation and do not adapt to clustering random distribution data, an improved clustering algorithm named automatic clustering algorithm based on data contained ratio is developed. The concept of data contained ratio is proposed in the improved algorithm, the number of clusters is automatically determined based on data contained ratio, and the cluster centres are obtained accordingly. Several group test data testify and demonstrate the validity and effectiveness of the improved clustering algorithm. In addition, the comparison between the traditional K-means clustering algorithm and improved clustering algorithm is presented. Finally, the improved clustering algorithm is used to recognise the flight trace. The results demonstrate that the improved clustering algorithm can be used for pattern recognition; it has high adaptability and robustness in clustering random distribution data set.

An improved clustering algorithm and its application

Thermal-induced warpage is a bottleneck problem in advanced packaging technology. In this article, a new way of predicting the warpage deformation is proposed for multi-chiplet heterogeneous integration system, where the system deformations are considered as the combined actions of several asymmetric tri-material assemblies based on the superposition principle. First, an analytical model for an asymmetric tri-material assembly is derived from the beam theory. The rotation and rigid translation behaviors are investigated for the asymmetrical structures, which are determined by both the geometrical structures and the material properties. Second, the finite element method (FEM) is adopted to validate the analytical model and obtain the sample data. Then, the unknown parameters in the analytical model are extracted by the random forest (RF) algorithm. Finally, a data-driven scale factor is introduced to modify the analytical model and improve its accuracy. The superposition warpage deformation of a heterogeneous integration system with 14 chiplets predicted by the present analytical model shows a good agreement with the FEM results with high efficiency. Therefore, the new thermal-induced warpage prediction method has a good potential of executing warpage analysis and package design of the multi-chiplet heterogeneous integration system in advanced packaging.

Two-Dimensional Thermal-Induced Warpage Prediction for Multi-Chiplet Heterogeneous Integration System in Advanced Packaging

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