Filling of very deep, wide trenches by BenzoCycloButene polymer

A process for deep trench filling by BenzoCycloButene (BCB) polymer is explored. Deep trenches with 100-μm depth and different aspect ratios from 1.4 to 20 have been successfully filled by BCB. Besides, chemical mechanical polishing (CMP) of BCB is studied with the main goals of smoothing surface topography of substrate after BCB filling and removing excess BCB coating which may be necessary in some applications. Removal rate for BCB, V _RR, of about 0.24 μm/min has been achieved for hard cured BCB films using acid slurry. After CMP, the BCB layer showed a roughness of about 1.36 nm (Rq, measured by atomic force microscopy, AFM).     

Using variable reduction strategy to accelerate evolutionary optimization

In this study, we introduce a novel approach of variable reduction and integrate it into evolutionary algorithms in order to reduce the complexity of optimization problems. We develop reduction processes of variable reduction for derivative unconstrained optimization problems (DUOPs) and constrained optimization problems (COPs) with equality constraints and active inequality constraints. Variable reduction uses the problem domain knowledge implied when investigating optimal conditions existing in optimization problems. For DUOPs, equations involving derivatives are considered while for COPs, we discuss equations expressing the equality constraints. From the relationships formed in this way, we obtain relationships among the variables that have to be satisfied by optimal solutions. According to such relationships, we can utilize some variables (referred to as core variables) to express some other variables (referred to as reduced variables). We show that the essence of variable reduction is to produce a minimum collection of core variables and a maximum number of reduced variables based on a system of equations. We summarize some application-oriented situations of variable reduction and stress several important issues related to the further application and development of variable reduction. Essentially, variable reduction can reduce the number of variables and eliminate equality constraints, thus reducing the dimensionality of the solution space and improving the efficiency of evolutionary algorithms. The approach can be applied to unconstrained, constrained, continuous and discrete optimization problems only if there are explicit variable relationships to be satisfied in the optimal conditions. We test variable reduction on real-world and synthesized DUOPs and COPs. Experimental results and comparative studies point at the effectiveness of variable reduction.     

Cohort intelligence algorithm for discrete and mixed variable engineering problems

Abstract

In this study, the performance of an emerging socio inspired metaheuristic optimization technique referred to as Cohort Intelligence (CI) algorithm is evaluated on discrete and mixed variable nonlinear constrained optimization problems. The investigated problems are mainly adopted from discrete structural optimization and mixed variable mechanical engineering design domains. For handling the discrete solution variables, a round off integer sampling approach is proposed. Furthermore, in order to deal with the nonlinear constraints, a penalty function method is incorporated. The obtained results are promising and computationally more efficient when compared to the other existing optimization techniques including a Multi Random Start Local Search algorithm. The associated advantages and disadvantages of CI algorithm are also discussed evaluating the effect of its two parameters namely the number of candidates, and sampling space reduction factor.     

Evolutionary Optimization of Machining Processes

Optimization of machining processes plays a key role in meeting the demands for high precision and productivity. The primary challenge for machining process optimization often stems from the fact that the procedure is typically highly constrained and highly non-linear, involving mixed-integer-discrete-continuous design variables. Additionally, machining process models are likely discontinuous, non-explicit, or not analytically differentiable with the design variables. Traditional non-linear optimization techniques are mostly gradient-based, posing many limitations upon application to today’s complex machining models. Genetic Algorithms (GAs) has distinguished itself as a method with the potential for solving highly non-linear, ill-behaved complex machining optimization problems. Unlike traditional optimization techniques, GAs start with a population of different designs and use direct search methods stochastically and deterministically toward optimal and feasible direction. However, GAs still has its own drawbacks when it is applied to machining process optimization, including the lack of efficiency due to its binary representation scheme for continuous design variables, a lack of local fine-tuning capabilities, a lack of a self-adaptation mechanism, and a lack of an effective constraint handling method. A novel and systematic evolutionary algorithm based on GAs is presented in this paper in the areas of problem representation; selection scheme; genetic operators for integer, discrete, and continuous variables; constraint handling method; and population initialization to overcome the underlying drawbacks. The proposed scheme has been applied to two machining problems to demonstrate its superior performance.     

A pseudo-sensitivity based discrete-variable approach to structural topology optimization with multiple materials

An algorithm has been developed which uses material as a discrete variable in multi-material topology optimization and thus provides an alternative to traditional methods using material interpolation and level-set approaches. The algorithm computes ‘pseudo-sensitivities’ of the objective and constraint functions to discrete material changes. These are used to rank elements, based on which a fraction of elements are selected for material ID modification during the optimization iteration. The algorithm is of general applicability and avoids frequent matrix factorizations so that it is applicable to large structural problems. In addition to the conventionally used evolutionary and morphogenesis approaches for iteration, a new iteration scheme of ‘resubstitution’ which combines the two approaches is presented. The application and functioning of the algorithm is demonstrated through case studies and comparisons with a few benchmark problems, showing its capability in providing mass-optimal topologies under stiffness constraints for various structural problems where multiple materials are considered.     

Wafer-level multilayer integration of RF passives with thick BCB/metal interlayer connection in silicon-based SiP

This paper presents an integration technology for RF passives using benzocyclobutene (BCB)/metal multilayer interconnection for system-in-package applications. This technology has been specially developed for RF subsystem packages in which a thick polymer, BCB (more than 15 μm thick), is adopted as dielectric with lossy silicon as substrate for its excellent characteristics. Both dry-etch BCB and photosensitive BCB are applied in this work, and their processes are briefly introduced and compared. An RF power divider, an MIM capacitor, different types of RF inductors as well as a coupled microstrip based band-pass filter are fabricated and measured at wafer level. The results show good electrical performances, and accordingly the passives are well applicable in RF band. Moreover, the subsystem models including monolithic chips connected with passives are presented.     

Improving machine dynamics via geometry optimization

The central thesis of this paper is that the dynamic performance of machinery can be improved dramatically in certain cases through a systematic and meticulous evolutionary algorithm search through the space of all structural geometries permitted by manufacturing, cost and functional constraints. This is a cheap and elegant approach in scenarios where employing active control elements is impractical for reasons of cost and complexity. From an optimization perspective the challenge lies in the efficient, yet thorough global exploration of the multi-dimensional and multi-modal design spaces often yielded by such problems. Moreover, the designs are often defined by a mixture of continuous and discrete variables—a task that evolutionary algorithms appear to be ideally suited for. In this article we discuss the specific case of the optimization of crop spraying machinery for improved uniformity of spray deposition, subject to structural weight and manufacturing constraints. Using a mixed variable evolutionary algorithm allowed us to optimize both shape and topology. Through this process we have managed to reduce the maximum roll angle of the sprayer by an order of magnitude, whilst allowing only relatively inexpensive changes to the baseline design. Further (though less dramatic) improvements were shown to be possible when we relaxed the cost constraint. We applied the same approach to the inverse problem of reducing the mass while maintaining an acceptable roll angle—a 2% improvement proved possible in this case.     

An optimality criteria based method for discrete design optimization taking into account buildability constraints

This paper presents a heuristic design optimization method specifically developed for practicing structural engineers. Practical design optimization problems are often governed by buildability constraints. The majority of optimization methods that have recently been proposed for design optimization under buildability constraints are based on evolutionary computing. While these methods are generally easy to implement, they require a large number of function evaluations (finite element analyses), and they involve algorithmic parameters that require careful tuning. As a consequence, both the computation time and the engineering time are high. The discrete design optimization algorithm presented in this paper is based on the optimality criteria method for continuous optimization. It is faster than an evolutionary algorithm and it is free of tuning parameters. The algorithm is successfully applied to two classical benchmark problems (the design of a ten-bar truss and an eight-story frame) and to a practical truss design optimization problem.     

An efficient multiobjective differential evolution algorithm for engineering design

Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.     

Finite element minimization of curvature functionals with anisotropy

We consider the prescribed curvature problem including anisotropy effects. The functional setting inBV (Ω;{−1,1}) is convexified and regularized by strictly convex functionals which, in turn, are discretized by continuous piecewise linear finite elements. It is known that sequences of discrete minima converge to a continuous minimizer. We discuss an efficient implementation of the minimization procedure based on a constrained modified Newton algorithm. Several numerical examples illustrate performances of our algorithm. This work was partially supported by MURST (Progetto Nazionale “Analisi Numerica e Matematica Computazionale”) and CNR (IAN and Contracts 92.00833.01, 94.00139.01) of Italy.     

MicroGP—An Evolutionary Assembly Program Generator

This paper describes μGP, an evolutionary approach for generating assembly programs tuned for a specific microprocessor. The approach is based on three clearly separated blocks: an evolutionary core, an instruction library and an external evaluator. The evolutionary core conducts adaptive population-based search. The instruction library is used to map individuals to valid assembly language programs. The external evaluator simulates the assembly program, providing the necessary feedback to the evolutionary core. μGP has some distinctive features that allow its use in specific contexts. This paper focuses on one such context: test program generation for design validation of microprocessors. Reported results show μGP being used to validate a complex 5-stage pipelined microprocessor. Its induced test programs outperform an exhaustive functional test and an instruction randomizer, showing that engineers are able to automatically obtain high-quality test programs.     

Wafer-level BCB bonding using a thermal press for microfluidics

Benzocyclobutene (BCB) is a thermosetting polymer that can form microfluidics and bond top and bottom layers of the microfluidics at the same time, and yields high repeatability and high bonding strength. This paper reports using photosensitive BCB to fabricate microfluidics and to bond with a thermal press for 4 in. wafers. By optimizing the parameters for pattern development and using a three-stage temperature and pressure increment BCB bonding, we realize the whole wafer glass–Si or glass–glass bonding in thermal press without any crack. The wafer-level bonding shows a bonding percentage above 70%, a tensile stress above 4.94 MPa, and a bonding repeatability over 95%. Furthermore, the bonding is compatible with thick electrode integration, that microfluidics with 380 nm thick electrodes underneath can be well-bonded. Our bonding method much reduces the cost compared with bonding BCB in a wafer bonding machine. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Coupling of quantum angular momenta: an insight into analogic/discrete and local/global models of computation

In the past few years there has been a tumultuous activity aimed at introducing novel conceptual schemes for quantum computing. The approach proposed in (Marzuoli and Rasetti, 2002, 2005a) relies on the (re)coupling theory of SU(2) angular momenta and can be viewed as a generalization to arbitrary values of the spin variables of the usual quantum-circuit model based on ‘qubits’ and Boolean gates. Computational states belong to finite-dimensional Hilbert spaces labelled by both discrete and continuous parameters, and unitary gates may depend on quantum numbers ranging over finite sets of values as well as continuous (angular) variables. Such a framework is an ideal playground to discuss discrete (digital) and analogic computational processes, together with their relationships occurring when a consistent semiclassical limit takes place on discrete quantum gates. When working with purely discrete unitary gates, the simulator is naturally modelled as families of quantum finite states-machines which in turn represent discrete versions of topological quantum computation models. We argue that our model embodies a sort of unifying paradigm for computing inspired by Nature and, even more ambitiously, a universal setting in which suitably encoded quantum symbolic manipulations of combinatorial, topological and algebraic problems might find their ‘natural’ computational reference model.     

An intelligent moving object optimization algorithm for design problems with mixed variables, mixed constraints and multiple objectives

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables; a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints; and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder–Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method.     

A ranking selection-based particle swarm optimizer for engineering design optimization problems

Particle swarm optimization (PSO) algorithms have been proposed to solve optimization problems in engineering design, which are usually constrained (possibly highly constrained) and may require the use of mixed variables such as continuous, integer, and discrete variables. In this paper, a new algorithm called the ranking selection-based PSO (RSPSO) is developed. In RSPSO, the objective function and constraints are handled separately. For discrete variables, they are partitioned into ordinary discrete and categorical ones, and the latter is managed and searched directly without the concept of velocity in the standard PSO. In addition, a new ranking selection scheme is incorporated into PSO to elaborately control the search behavior of a swarm in different search phases and on categorical variables. RSPSO is relatively simple and easy to implement. Experiments on five engineering problems and a benchmark function with equality constraints were conducted. The results indicate that RSPSO is an effective and widely applicable optimizer for optimization problems in engineering design in comparison with the state-of-the-art algorithms in the area.     

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

 In the present paper a special bit-masking oriented data structure for an improved implementation of crossover and mutation operators in genetic algorithms is shown. The developed data structure performs evolutionary operators in two separate steps: crossover and mutation mask fill and a special boolean based function application. Both phases are optimized to reach a more efficient, fast and flexible genetic reproduction than standard implementations. The method has been powered adding a multi-layered, bit-masking oriented data structure and a boolean operation based control mixer, allowing special blended crossover operators obtained by superposition of the standard ones. Several examples of crossover schemes produced by these extended controls are presented. In addition, a special purpose crossover scheme, capable to process at the same time two distinct groups of design variables with separate crossover schemes is shown, in order to improve efficiency and convergence speed of some discrete/continuous optimization problems. Finally, to highlight further capabilities of the bit-masking approach, a special single-step version of an evolutionary direction operator is also illustrated.     

基于MSC Nastran的离散变量优化算法的实现

实际工程中存在大量的离散变量优化问题,基于MSC Nastran优化框架实现新的离散变量算法,有利于新算法本身的推广应用和解决大规模的实际复杂工程问题.通过修改MSC Nastran输入文件的方法实现离散变量的优化算法——GSFP算法.GSFP是基于广义形函数的离散变量优化算法,它将离散变量优化问题转化成连续变量优化问题,通过惩罚等措施使得最优设计结果最终收敛到离散解,该方法能够解决大规模的实际离散变量优化问题.最后以桁架截面选型优化为应用背景,给出GSFP算法实现的基本原理和方法.     

Bioparticle separation in non-Newtonian fluid using pulsed flow in micro-channels

Micro total analysis system (μTAS) devices frequently need to deal with bio-particle suspensions in solution and separation of certain bio-particles is often desirable. Separation of plasma from the whole blood has become increasingly popular in clinical diagnostics. Using the coupled volume of fluid (VOF) and the discrete phase (DP) governing equations for multiphase flow, the effect of Newtonian viscosity of water and the non-Newtonian shear-thinning viscosity of blood on particle separation under pulsed pressure condition is determined. It is observed that the red blood cells (RBC) accumulate at the front of the blood column while the opposite is observed in water. For the selected parameters, 20% of the plasma observed is to be separated from the whole blood, while in contrast, for a 98% diluted blood, 45% of the plasma can be separated. The present calculations can be adopted to design the flow parameters necessary for the instantaneous separation of plasma from the whole and the diluted blood for μTAS; thus reducing the number of experimental studies.     

Optimal design and motion control of biomimetic robotic fish

This paper is concerned with the design, optimization, and motion control of a radiocontrolled, multi-link, free-swimming biomimetic robotic fish based on an optimized kinematic and dynamic model of fish swimming. The performance of the robotic fish is determined by both the fish＇s morphological characteristics and kinematic parameters. By applying ichthyologic theories of propulsion, a design framework that takes into consideration both mechatronic constraints in physical realization and feasibility of control methods is presented, under which a multiple linked robotic fish that integrates both the carangiform and anguilliform swimming modes can be easily developed. Taking account of both theoretic hydrodynamic issues and practical problems in engineering realization, the optimal link-lengthratios are numerically calculated by an improved constrained cyclic variable method, which are successfully applied to a series of real robotic fishes. The rhythmic movements of swimming are driven by a central pattern generator （CPG） based on nonlinear oscillations, and up-and-down motion by regulating the rotating angle of pectoral fins. The experimental results verify that the presented scheme and method are effective in design and implementation.