An effective system for parameter optimization in photolithography process of a LGP stamper

In the current thin-film transistor liquid crystal display industry, the light guide plate (LGP) of the backlight module has become thinner and smaller, and the backlight module needs to be illuminated uniformly and effectively. The parameter setting for the photolithography process of a LGP stamper often relies on the engineers’ experiences by means of trial-and-error or design of experiment to obtain a suitable and more reliable process parameter setting, which requires a large amount of time, manpower, and cost. This research proposes a novel two-stage optimization system for photolithography process integrating the Taguchi method, back-propagation neural networks, genetic algorithms, particle swarm optimization, and related technologies to effectively generate optimal process parameter settings. The first stage is to reduce the process variance. The second stage is to find the final optimal process parameter settings for the best quality specification. Experimental results show that the proposed system can create the best process parameters which not only meet the quality specification for the micro-dots on the photoresist, but also effectively enhance the overall process stability.     

Integration of stamper fabrication and design optimization of LCD Light guides using silicon-based microfeatures

This study applies a novel fabrication process that combines anisotropic wet etching of silicon-on-insulator (SOI) wafers with electroforming to manufacture precision stampers. Micron-sized features, such as trapezoidal grooves and truncated pyramidal prisms, can be fabricated and distributed accurately. Because feature geometry and distribution can be accurately realized using the proposed scheme, design optimization of light guide plates (LGPs) becomes realistic. By observing the illumination characteristics of light emitting diode (LED) edge-lit LGPs, the distribution pattern of the LGP is transformed into a parameter design with seven anchor spacing and the spacing modulation amplitude of the micro features adjacent to LEDs. The proposed fuzzy optimization scheme manipulates distribution parameters to obtain an LGP design with high illumination uniformity. The design of a 3.5-inch LED edge-lit LGP is used as an illustrative example. The optical software program TracePro is applied to simulate luminance performance of BLM. The optimization converges rapidly and provides the optimum design with an average brightness of 2,266 (nit) and uniformity of 90% without use of diffusive sheets. Thus work demonstrates the feasibility and effectiveness of the proposed scheme.     

Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Mobile application (app) design is an expanding research area, with user experience (UX) as its core. UX encompasses all aspects of human–computer interaction, and thus the optimization of UX has multiple objectives. Quality characteristics related to UX are subjective and even subconscious; moreover, there exists interdependence among UX quality characteristics. However, very little attention has been focused on these issues when optimizing UX based on multiple objectives. In this paper, a fuzzy analytic network process (ANP)-based Taguchi method is proposed for optimizing UX in mobile app design. First, design patterns and UX quality characteristics are determined. Subsequently, a Taguchi experiment is designed and carried out, and then signal-to-noise (S/N) ratios are calculated. A fuzzy ANP is adopted to derive the preference weights for the UX quality characteristics. Based on these weights, the S/N ratios are converted into a multiperformance characteristic index (MPCI) by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finally, according to the MPCI, the significant design patterns are identified by using the analysis of variance, and the optimal design is obtained by using the response table and response graph. A mobile health app design was presented to illustrate the proposed approach. The results suggest that the proposed approach can effectively manage the interdependence among the subjective and even subconscious UX quality characteristics in the optimization process, and be used as a universal robust design approach to optimize UX in mobile app design.     

Conservative multi-objective optimization considering design robustness and tolerance: a quality engineering design approach

Methods of multi-objective optimization are proposed to account for tolerance of design variable and variation in problem parameter. The post-optimization effort is initiated from deterministic Pareto-optimal solutions that were obtained from NSGA-II. The successive process to determine search directions and step sizes toward conservative multi-objective solutions was conducted by design of experiments to determine the worst design that had the highest constraint violation. The signal-to-noise (S/N) ratio was also employed to represent the robustness of constrained objective functions under parameter variation. Structural optimization was explored to accommodate both design tolerance and parameter variation and further apply S/N ratio in conservative multi-objective optimization.     

The implementation of neural network for semiconductor PECVD process

In semiconductor manufacturing, the monitoring system has been developed very excellently and can be used for comprehensively collecting the historical data of process information and quality characteristics of equipment. However, due to the high turnover rate of personnel and the great variance in manufacturing process, the previous control technique by using intuition and experience of engineers for manufacturing process parameter settings to achieve good product quality is no longer appropriate. Therefore, this research establishes a quality predictor for analyzing the relationship between manufacturing process parameter setting and final product quality in the plasma-enhanced chemical vapor deposition (PECVD) of semiconductor manufacturing by applying the back-propagation neural network (BPNN) algorithm and Taguchi method. The experimental data are categorized into 500 pieces of training data and 150 pieces of verifying data. The proposed analysis method for using in the PECVD process of semiconductor manufacturing is verified by comparing the predicted film thickness of SiO₂ and the predicted refractive index of silicon dioxide films with the measured data. According to the comparison result, the proposed model has an excellent prediction capability of final product quality and can be applied in process control for related manufacturing fields.     

Optimization of optical design for developing an LED lens module

In this study, a procedure for optimization of an LED lens module design based on 3 LED light sources was divided into two phases. For preliminary optimization of the dimensions of the LED lens module in Stage I, an optical analysis with orthogonal arrays and TracePro (an optical design package) combined with analysis of variance was conducted to investigate relationships between the multiple optical quality characteristics (viewing angle and average illuminance) and dimension parameters and find the initial optimal parameter combination of the LED lens module. In Stage II, the initial optimal parameter combination determined in Stage I was employed to develop an orthogonal array L₂₅(5⁶) for optical simulation. The experimental data of the orthogonal array were used to train and test the back-propagation neural network to develop an optical quality predictor, which was integrated into the genetic algorithms and the particle swarm optimization in order to find the optimal parameter combination that conformed to optical quality. From the experimental results, the proposed optimization procedure contributes to a precise viewing angle to achieve the goal of optical quality and improved the average illuminance in development of the product. The procedure to optimize the optical design developed in this study can be applied to design all types of LED lens modules and improve the optical design and technology of the LED lens industry.     

Artificial neural network based approach for dynamic parameter design

The Taguchi parameter design method has been recognized as an important tool for improving the quality of a product or a process. However, the statistical methods and optimization procedures proposed by Taguchi have much room for improvement. For instance, the two-step procedure proposed by Taguchi may fail to identify an optimum design condition if an adjustment parameter does not exist, the optimal setting of a design parameter is determined only among the levels included in the parameter design experiment, and, for the dynamic parameter design, the signal parameter is assumed to follow a uniform rather than a general distribution. This paper develops an artificial neural network based dynamic parameter design approach to overcome the shortcomings of the Taguchi and existing alternative approaches. First, an artificial neural network is trained to map the relationship between the characteristic, design, noise and signal parameters. Second, Latin hypercube samples of the signal and noise parameters are obtained and used to estimate the slope between the signal parameter and characteristic as well as the variance of the characteristic at each set of design parameter settings. Then, the dynamic parameter design problem is formulated as a nonlinear optimization problem and solved to find the optimal settings of the design parameters using sequential quadratic programming. The effectiveness of the proposed approach is illustrated with an example.     

Improving the lighting performance of a 3535 packaged hi-power LED using genetic programming,quality loss functions and particle swarm optimization

The lighting performance of a 3535 packaged hi-power LED (light-emitting diode) is mainly influenced by its geometric design and the refractive properties of its materials. In the past, engineers often determined the settings of the geometric parameters and selected the refractive properties of the materials through a trial-and-error process based on the principles of optics and their own experience. This procedure was costly and time-consuming, and its use did not ensure that the settings of the design parameters were optimal. Therefore, this study proposed a hybrid approach based on genetic programming (GP), Taguchi quality loss functions, and particle swarm optimization (PSO) to solve the multi-response parameter design problems. The feasibility and effectiveness of the proposed approach was demonstrated by a case study on improving the lighting performance of an LED. The confirmation results showed that all of the key quality characteristics of an LED fulfill the required specifications, and the comparison found that the proposed hybrid approach outperforms the traditional Taguchi method in solving this multi-response parameter design problem. The proposed hybrid approach can be extended to solve parameter design problems with multiple responses in various application fields.     

Parameter optimization for an ICP deep silicon etching system

The paper aims at investigating the parameter optimization of silicon micro- and nano-sized etching by an inductive coupled plasma-reactive ion etching system. The source power and the SF₆ gas pressure are two main parameters that dominate etching. A pre-test is conducted to estimate the process window of the SF₆ gas pressure at some given source powers. The process window is a parameter range in which the etching result is acceptable but may not be the best. In order to achieve excellent etching quality, the Taguchi experimental method is applied to evaluate parameters and find their optimum conditions. With the source power and SF₆ gas pressure being set into the process window, four parameters, which are the substrate temperature, the bias power, the gas cycle time and the C₄F₈ gas flow rate, are evaluated and optimized for micro- and nano-sized etching. An impressive result, 200-nm-diameter pillar array with the pitch of 400 nm, is realized.     

Application of fuzzy-based Taguchi method to the optimization of extrusion of magnesium alloy bicycle carriers

By using fuzzy-base Taguchi method, this study investigates the optimal process parameters that maximize multiple performance characteristics index (MPCI) for hot extrusion of AZ31 and AZ61 magnesium alloy bicycle carriers. The larger-the-better quality characteristics of flattening strength and T-slot fracture strength as well as the smaller-the-better quality characteristic of extrusion load is considered in the MPCI. Through MPCI inference model, a manufacture method with less extrusion load and better mechanical properties under hot extrusion can be obtained. The signal-to-noise (S/N) ratios of the three quality characteristics??flattening strength, T-slot fracture strength and extrusion load??are calculated for the products based on experimental results. And the S/N ratio serves as the input variable to fuzzy control unit, and MPCI is a single output variable. The obtained MPCI is used to analyze optimal process parameters. This study finds combination of process parameters that optimizes MPCI, and conducts confirmatory experiments to prove the accuracy of optimal combination of process parameters thus selected. Finally, mechanical properties of AZ31, AZ61 magnesium alloy and A6061 aluminum alloy bicycle carriers are tested to identify differences among these three materials.     

The optimal process mean problem: Integrating predictability and profitability into an experimental factor space

For complex manufacturing systems, process or product optimization can be instrumental in achieving a significant economic advantage. To reduce costs associated with product non-conformance or excessive waste, engineers often identify the most critical quality characteristics and then use methods to obtain their ideal parameter settings. The optimal process mean problem is one such statistical method; it begins with the assumption of the characteristic parameters, whereby the ideal settings are determined based upon the tradeoff among various processing costs. Unfortunately, however, the ideal parameter settings for a characteristic mean can be unpredictable, as it is directly influenced by changes in the process variability, tolerance, and cost structure. In this paper, a method is proposed that relates the optimal process mean to the ideal settings through experimental design. With the method, one may gain greater predictability of the new optimal process mean when the process conditions are altered. The methodology is illustrated for a process with multiple mixed quality characteristics; such an optimal process mean problem is seldom treated in the literature.     

Application of SVR,Taguchi loss function,and the artificial bee colony algorithm to resolve multiresponse parameter design problems: a case study on optimizing the design of a TIR lens

Multiresponse parameter design problems have become increasingly important and have received considerable attention from both researchers and practitioners since there are usually several quality characteristics that must be optimized simultaneously in most modern products/processes. This study applies support vector regression (SVR), Taguchi loss function, and the artificial bee colony (ABC) algorithm to develop a six-staged procedure that resolves these common and complicated parameter design problems. SVR is used to model the mathematical relationship between input control factors and output responses, and the ABC algorithm is used to find the optimal control factor settings by searching the well-constructed SVR models in which the Taguchi loss function is applied to evaluate the overall performance of a product/process. The feasibility and effectiveness of the proposed approach are demonstrated via a case study in which the design of a total internal reflection (TIR) lens is optimized while fabricating an MR16 light-emitting diode lamp. Experimental results indicate that the proposed solution procedure can provide highly robust design parameter settings for TIR lenses that can be directly applied in real manufacturing processes. Comparisons with the Taguchi method reveal that the Taguchi method is an undesirable and inappropriate method for resolving multiple-response parameter design problems, while the ABC algorithm can search the solution spaces in continuous domains modeled via SVR instead of in the limited discrete experiment levels, thus finding a more robust design than that obtained by the traditional analysis of variance. Consequently, the proposed integrated approach in this study can be considered feasible and effective and can be popularized as a useful tool for resolving general multiresponse parameter design problems in the real world.     

Toward a robust optimal point selection: a multiple-criteria decision-making process applied to multi-objective optimization using response surface methodology

During the multi-objective optimization process, numerous efficient solutions may be generated to form the Pareto frontier. Due to the complexity of formulating and solving mathematical problems, choosing the best point to be implemented becomes a non-trivial task. Thus, this paper introduces a weighting strategy named robust optimal point selection, based on ratio diversification/error, to choose the most preferred Pareto optimal point in multi-objective optimization problems using response surface methodology. Furthermore, this paper proposes to explore a theoretical gap—the prediction variance behavior related to the weighting. The ratios Shannon’s entropy/error and diversity/error and the unscaled prediction variance are experimentally modeled using mixture design and the optimal weights for the multi-objective optimization process are defined by the maximization of the proposed measures. The study could demonstrate that the weights used in the multi-objective optimization process influence the prediction variance. Furthermore, the use of diversification measures, such as entropy and diversity, associated with measures of error, such as mean absolute percent error, was determined to be useful in mapping regions of minimum variance within the Pareto optimal responses obtained in the optimization process.

     

基于粒子群算法的跳频信号参数估计*

针对基于时频分布的参数估计存在信噪比阈值和低信噪比下方差大的问题,提出了一种基于多峰优化粒子群算法的跳频信号参数估计新算法。该算法首先将跳频信号分解为时频原子的线性组合,然后由匹配原子获取跳频信号的参数估计。仿真结果表明,基于改进的物种形成粒子群算法能够搜索到与跳频信号分量相匹配的原子,与平滑伪魏格纳分布相比,提出的参数估计算法在低信噪比下具有较小的估计方差,更加适宜于电子战的实际应用。     

A hybrid intelligent approach for optimizing the fine-pitch copper wire bonding process with multiple quality characteristics in IC assembly

Gold is the primary material used for wire bonding in integrated circuit (IC) assembly. Owing to the high appreciation in the price of gold, copper (Cu) wire has become an important substitute material in order to save on manufacturing costs. However, an average of 40% in yield loss during IC assembly can be attributed to improper control of the Cu wire bonding process. To assure cost savings without losing yield, and ensure cost-effective IC assembly, optimization of the parameters for the Cu wire process is critical. This work proposes a hybrid intelligent approach to derive robust parameter settings for a fine-pitch Cu wire bonding process with multiple quality characteristics. The proposed methodology utilizes grey relational analysis and an entropy measurement method to convert the multiple responses into a single synthetic performance index without involving the subjective judgment of an engineer and causing unbalanced improvements of the responses. An integrated neural network model and genetic algorithm method is then applied to acquire the optimal parameter settings. The performance of this method is evaluated experimentally and the results compared with that of the response surface methodology and original parameter settings. The results confirm the feasibility and practicality of this strategy to improve production yield and process capability during Cu wire bonding.     

Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.     

基于参数方差调节萤火虫算法的三维路径规划

为了提高萤火虫算法大范围搜索时的速度和精度,提出了一种参数方差调节萤火虫算法。首先分析基本萤火虫算法,在此基础上提出了参数方差调节萤火虫算法的核心思想：计算种群亮度的方差评估种群的敛散性,根据进程调节参数,进而达到改进萤火虫算法的目的,并给出了算法的实现步骤和流程;然后在四个优化测试函数中将参数方差调节萤火虫算法与基本萤火虫算法、遗传算法、粒子群算法进行比较和分析,发现参数方差调节萤火虫算法在测试中能迅速的找到符合精度要求的解,且成功率是100%,具有较好的稳定性,较之其他算法优势明显;最后通过构建计算能量消耗的目标函数在有实际背景和地理参数的自主式水下潜器三维路径规划的仿真实验中应用参数方差调节萤火虫算法,在三维海底环境中规划出符合要求的路,从而证明了参数方差调节萤火虫算法在三维路径规划中的实用性。     

融合 DE-GWO 与 SVR 的文化意象预测模型

为更客观准确的量化文化特征与意象间的关系,提出一种融合混合灰狼优化算法(DE-GWO)与 支持向量回归(SVR) 的文化意象预测模型。首先,构建以多组意象词汇为基础的响堂山石窟造像的文化特征的 意象空间,并利用眼动追踪技术进行文化意象认知实验,获取被试生理认知数据并对其进行单因素方差分析, 进而得到文化意象预测模型的眼动指标参数数据集;其次,引入基于 DE 算法的差分进化策略以弥补 GWO 搜 索过程陷入停滞状态的问题;再次,利用改进后的 GWO 算法对 SVR 模型的参数 C 和 g 进行寻优;最后利用 构建的 DE-GWO-SVR 模型实现对文化意象认知的预测。为了进一步证明所构建模型的泛化性,采用 BP, ABC-SVR 和 DT 等 5 种模型进行对比实验,结果表明该模型对于文化意象认知的预测效果更好。     

Visualization of parameter space for image analysis

Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step--initialization of sampling--and the last step--visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler--a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.