Regulation of Polymer Configurations Enables Green Solvent-Processed Large-Area Binary All-Polymer Solar Cells With Breakthrough Performance and High Efficiency Stretchability Factor

With the great potential of the all-polymer solar cells for large-area wearable devices, both large-area device efficiency and mechanical flexibility are very critical but attract limited attention. In this work, from the perspective of the polymer configurations, two types of terpolymer acceptors PYTX-A and PYTX-B (X = Cl or H) are developed. The configuration difference caused by the replacement of non-conjugated units results in distinct photovoltaic performance and mechanical flexibility. Benefiting from a good match between the intrinsically slow film-forming of the active materials and the technically slow film-forming of the blade-coating process, the toluene-processed large-area (1.21 cm²) binary device achieves a record efficiency of 14.70%. More importantly, a new parameter of efficiency stretchability factor (ESF) is proposed for the first time to comprehensively evaluate the overall device performance. PM6:PYTCl-A and PM6:PYTCl-B yield significantly higher ESF than PM6:PY-IT. Further blending with non-conjugated polymer donor PM6-A, the best ESF of 3.12% is achieved for PM6-A:PYTCl-A, which is among the highest comprehensive performances.     

一款基于APA300的创新型FPGA实验板

基于Actel公司Flash型FPGA芯片APA300设计并实现一款创新型FPGA实验板.该实验板包括2片互通的APA300、丰富的外围设备和常用外部设备端口,还提供与外部电路连接的扩展接口,能够开展多种创新性实验,并可作为科学预研的平台.在详细介绍了实验板各部分的实现原理和电路连接图后,给出了实验板的两个典型创新性实验示例:协处理器密码机和MP3播放器.     

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.     

Content-based image authentication: current status, issues, and challenges

With today’s global digital environment, the Internet is readily accessible anytime from everywhere, so does the digital image manipulation software; thus, digital data is easy to be tampered without notice. Under this circumstance, integrity verification has become an important issue in the digital world. The aim of this paper is to present an in-depth review and analysis on the methods of detecting image tampering. We introduce the notion of content-based image authentication and the features required to design an effective authentication scheme. We review major algorithms and frequently used security mechanisms found in the open literature. We also analyze and discuss the performance trade-offs and related security issues among existing technologies.     

Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models

Surrogate models are used to dramatically improve the design efficiency of numerical aerodynamic shape optimization, where high-fidelity, expensive computational fluid dynamics (CFD) is often employed. Traditionally, in adaptation, only one single sample point is chosen to update the surrogate model during each updating cycle, after the initial surrogate model is built. To enable the selection of multiple new samples at each updating cycle, a few parallel infilling strategies have been developed in recent years, in order to reduce the optimization wall clock time. In this article, an alternative parallel infilling strategy for surrogate-based constrained optimization is presented and demonstrated by the aerodynamic shape optimization of transonic wings. Different from existing methods in which multiple sample points are chosen by a single infill criterion, this article uses a combination of multiple infill criteria, with each criterion choosing a different sample point. Constrained drag minimizations of the ONERA-M6 and DLR-F4 wings are exercised to demonstrate the proposed method, including low-dimensional (6 design variables) and higher-dimensional problems (up to 48 design variables). The results show that, for surrogate-based optimization of transonic wings, the proposed method is more effective than the existing parallel infilling strategies, when the number of initial sample points are in the range from N _v to 8N _v (N _v here denotes the number of design variables). Each case is repeated 50 times to eliminate the effect of randomness in our results.     

Quantum hydrodynamic simulation of discrete-dopant fluctuated physical quantities in nanoscale FinFET

Impact of the discrete dopants on device performance is crucial in determining the behavior of nanoscale semiconductor devices. Atomistic quantum mechanical device simulation for studying the effect of discrete dopants on device's physical quantities is urgent. This work explores the physics of discrete-dopant-induced characteristic fluctuations in 16-nm fin-typed field effect transistor (FinFET) devices. Discrete dopants are statistically positioned in the three-dimensional channel region to examine associated carrier's characteristic, concurrently capturing “dopant concentration variation” and “dopant position fluctuation”. An experimentally validated quantum hydrodynamic device simulation was conducted to investigate the potential profile and threshold voltage fluctuations of the 16-nm FinFET. Results of this study provide further insight into the problem of fluctuation and the mechanism of immunity against fluctuation in 16-nm devices.     

Supporting early pruning in top-k query processing on massive data

This paper analyzes the execution behavior of “No Random Accesses” (NRA) and determines the depths to which each sorted file is scanned in growing phase and shrinking phase of NRA respectively. The analysis shows that NRA needs to maintain a large quantity of candidate tuples in growing phase on massive data. Based on the analysis, this paper proposes a novel top-k algorithm Top-K with Early Pruning (TKEP) which performs early pruning in growing phase. General rule and mathematical analysis for early pruning are presented in this paper. The theoretical analysis shows that early pruning can prune most of the candidate tuples. Although TKEP is an approximate method to obtain the top-k result, the probability for correctness is extremely high. Extensive experiments show that TKEP has a significant advantage over NRA.