Joined initiatives around uncertainty management

An industrial decision process supported by quantitative modelling (safety and reliability, physical design of facilities or processes, economic optimisation, environmental impact, etc.) quickly faces a wide diversity of uncertainties, imprecision, errors or alea affecting all data or numerical models. Beyond a terminological heterogeneity that is explicable by historical separation of the fields involved (such as metrology, reliability,separation of the fields involved (such as metrology, reliability, statistics, numerical analysis, ...), this papers introduces a generic and applied approach to uncertainty, derived from years of experience and recently shared by different applied research groups. This methodology is composed of four main steps that enable to distinguish some classical steps in modelling: specification of a criterion of interest to be assessed by a numerical representation of the problem containing uncertain variables (step A), identification and quantification of the sources of uncertainty (step B), propagation of uncertainty through the numerical representation (step C) and ranking of the uncertainties by sensitivity analysis (step D). This approach aims at giving a consistent and industrially realistic framework for practical mathematical modelling, assumingly restricted to quantitative and quantifiable uncertainty. Within this framework, various mathematical settings are possible; however, themixed deterministic–probabilistic setting appears to be central in present-day industrial applications and is the core of this paper. This paper introduces the current status of applied research in this field and points out different initiatives (software, research community) that are dealing with this topic.     

DOE/Opt: a system for design of experiments, response surfacemodeling, and optimization using process and device simulation

Rapid modeling and optimization of manufacturing processes, devices, and circuits are required to support modern integrated circuit technology development and yield improvement. We have prototyped and applied an integrated system, called DOE/Opt, for performing Design of Experiments (DOE), Response Surface Modeling (RSM), and Optimization (Opt). The system to be modeled and optimized can be either physical or simulation based. Within the DOE/Opt system, coupling to external simulation or experimental tools is achieved via an embedded extension language based on Tcl. The external problem then appears to DOE/Opt as a model with user defined inputs and outputs. DOE/Opt is used to generate splits for experiments, to dynamically build and evaluate regression models from experimental runs, and to perform nonlinear constrained optimizations using either regression models or embedded executions. The intermediate regression modeling can appreciably accelerate the optimization task when simulation or physical experiments are expensive. The primary application of DOE/Opt has been to process optimization using coupled process and device simulation. DOE/Opt has also been applied to process and device simulator tuning, and to aid in device characterization. Such a DOE/Opt system is expected to augment the use of TCAD tools and to utilize data collected by CIM systems in support of process synthesis. We have demonstrated the application of the system to process parameter determination, simulator tuning, process control modeling, and statistical process optimization. We are extending the system to more fully support emerging device design and process synthesis methodologies     

Planning the e-Scrap Reverse Production System Under Uncertainty in the State of Georgia: A Case Study

Due to legislative requirements, environmental concerns, and market image, the disposition of end-of-life e-scrap is attracting tremendous attention in many parts of the world today. Effective management of returned used product flows can have a great impact on the profitability and resulting financial viability of associated e-scrap reverse production systems. However, designing efficient e-scrap reverse production systems is complicated by the high degree of uncertainty surrounding several key factors. Very few examples of this complex design problem are documented in the academic literature. This paper contributes as analysis of a new, large-scale application that designs an infrastructure to process used televisions, monitors, and computer central processing units (CPUs) in the state of Georgia in the U.S. The case study employs a scenario-based robust optimization model for supporting strategic e-scrap reverse production infrastructure design decisions under uncertainty. A mixed integer linear programming (MILP) model is used to maximize the system net profit for specified deterministic parameter values in each scenario, and then a min–max robust optimization methodology finds a robust solution for all of the scenarios.     

System-level reliability assessment of mixed-signal convergent microsystems

The next-generation convergent microsystems, based on system-on-package (SOP) technology, require up-front system-level design-for-reliability approaches and appropriate reliability assessment methodologies to guarantee the reliability of digital, optical, and radio frequency (RF) functions, as well as their interfaces. Systems approach to reliability requires the development of: i) physics-based reliability models for various failure mechanisms associated with digital, optical, and RF Functions, and their interfaces in the system; ii) design optimization models for the selection of suitable materials and processing conditions for reliability, as well as functionality; and iii) system-level reliability models understanding the component and functional interaction. This paper presents the reliability assessment of digital, optical, and RF functions in SOP-based microsystems. Upfront physics-based design-for-reliability models for various functional failure mechanisms are presented to evaluate various design options and material selection even before the prototypes are made. Advanced modeling methodologies and algorithms to accommodate material length scale effects due to enhanced system integration and miniaturization are presented. System-level mixed-signal reliability is discussed thorough system-level reliability metrics relating component-level failure mechanisms to system-level signal integrity, as well as statistical aspects.     

Robust torque optimization for BLDC spindle motors

A robust design for torque optimization of brushless DC spindle motors applied in a hard disk drive, using the Taguchi method, is described and illustrated in this paper. The optimal design process takes into consideration noises that arise in the manufacturing process, such as manufacturing tolerances for the stator tooth shape and variation of the rotor magnet magnetization distribution due to the magnetization fixture and process. The objective of the optimal design using the combined Taguchi's design of experiment (DOE) and finite-element analysis (FEA) approach is to ensure that the spindle motor torque performance is insensitive to the noise, with moderate computational effort. The optimization is realized by a simulation and analysis tool that integrates Taguchi's DOE with the FEA. In this paper, the design optimization process is described and the results are presented     

Physics-based electron device modelling and computer-aided MMICdesign

On overview on the state of the art and future trends in physics-based electron device modelling for the computer-aided design of monolithic microwave ICs is provided. After a review of the main physics-based approaches to microwave modeling, special emphasis is placed on innovative developments relevant to circuit-oriented device performance assessment, such as efficient physics-based noise and parametric sensitivity analysis. The use of state-of-the-art physics-based analytical or numerical models for circuit analysis is discussed, with particular attention to the role of intermediate behavioral models in linking multidimensional device simulators with circuit analysis tools. Finally, the model requirements for yield-driven MMIC design are discussed, with the aim of pointing out the advantages of physics-based statistical device modeling; the possible use of computationally efficient approaches based on device sensitivity analysis for yield optimization is also considered     

A complete procedure for the design and optimization of arbitrarily shaped integrated lens antennas

This paper proposes a new design methodology of arbitrarily shaped integrated lens antennas (ILAs). First, we describe the design principles and numerical techniques of the optimization iterative loop. The starting lens shape, deduced from a general synthesis method based on geometrical optics principles, is optimized so that the radiation pattern of the ILA complies with an arbitrary amplitude-shaped template. The optimization procedure is local and is based on a multidimensional conjugate gradient method. Then, the capabilities and potentialities of this approach are demonstrated numerically using two examples. In the first one, an ILA radiating a secant-squared beam in one principal plane and a flat-top beam in the orthogonal plane is designed for indoor communications in V-band. In the second example (Gaussian/flat-top beam), we show for the first time that the joint optimization of the feed together with the lens shape is a very promising design methodology. Finally, our design tools are validated experimentally in Q-band through the synthesis and optimization of a peculiar small shaped ILA (28 mm/spl times/28 mm/spl times/15 mm) fed by an aperture-coupled microstrip patch antenna.     

DOE-Based Extraction of CMP, Active and Via Fill Impact on Capacitances

Chemical-mechanical polishing (CMP), active and via fills have become indispensable aspects of semiconductor manufacturing. CMP fills are used to reduce metal thickness variations due to chemical-mechanical polishing. Via fills are used to improve neighboring via printability and reliability of low-k and ultra low-k dielectrics. Active region fills are used for STI CMP uniformity and stress optimization. Although modern parasitic extraction tools accurately handle grounded fills and regular interconnects, such tools use only rough approximations to assess the capacitance impact of floating fills, such as assuming that floating fills are grounded or that each fill is merged with neighboring ones. To reduce such inaccuracies, we provide a design of experiments (DOE) which complements what is possible with existing extraction tools. Through the proposed DOE set, a design or mask house can generate normalized fill tables to correct for the inaccuracies of existing extraction tools when floating fills are present. Golden interconnect capacitance values can be updated using these normalized fill tables. Our proposed DOE enables extensive analyses of fill impacts on coupling capacitances. We show through 3-D field solver simulations that the assumptions used in extractors result in significant inaccuracies. We present analyses of fill impacts for an example technology and also provide analyses using the normalized fill tables to be used in the extraction flow for three different standard fill algorithms. We also extend our analyses and methodology to via fills and active region fills, which have more recently been introduced into semiconductor design-manufacturing methodologies and for which sufficient understanding is still lacking.     

Low K芯片引线键合工艺计算机仿真与参数优化

Low K材料具有较高的脆性,在芯片封装测试过程中容易被损坏,因而Cu／low—K的结构的引入对芯片的封装工艺提出了很大的挑战。文章中提出引线键合工艺中冲击阶段的近似数学模型,由计算机仿真结果证实该理论模型的合理性。通过对计算机仿真结果的分析得到优化的LowK芯片引线键合工艺参数设置范围,实验设计的优化结果表明本研究提出的计算机仿真优化方法是有效的。     

Multidisciplinary Electronic Package Design and Optimization Methodology Based on Genetic Algorithm

A new multidisciplinary design and optimization methodology in electronics packaging is presented. A genetic algorithm combined with multidisciplinary design and multiphysics analysis tools are used to optimize key design parameters. This methodology is developed to improve the electronic package design process by performing multidisciplinary design and optimization at an early design stage. To demonstrate its capability, the methodology is applied to a ball grid array (BGA) package design. Multidisciplinary criteria including thermal, thermal strain, electrical, electromagnetic leakage, and cost are optimized simultaneously. A simplified routability analysis criterion is used as a constraint. The genetic algorithm is used for systematic design optimization. The present methodology can be applied to electronics product design at various packaging levels.     

Probabilistic prediction of wiring demand and routing requirementsfor high density interconnect substrates

Estimation of wiring and routability requirements for high density substrates is crucial for the development of new technologies, design, CAD tools, and optimization. This paper describes a new approach to wireability estimation that goes beyond Rent's rule. This approach depends on data flow and placement information available at early stages of the design process. Bus and random wiring are modeled explicitly. Excellent overall agreement is demonstrated between our predictions and published wiring data for two MCM systems. The relationship between placement and wireability will be presented through an optimization study by taking wiring parameters and their distributions as metrics. The application of this approach to the estimation of the required signal layers will be demonstrated     

A novel approach to the design of complex heat transfer systems:portable computer design-a case study

The paper describes a novel approach to solving heat transfer problems in geometrically complex systems. In the present approach, instead of simulating the detailed complex geometry, templates of components and devices are assumed rather than working on actual components. By varying the template dimensions a systematic study on the effects of geometrical configurations on cooling airflow and heat transfer is made possible. The application of the approach is illustrated drawing examples from heat transfer analysis of portable computers. The purpose of the study is to develop a methodology whereby the packaging designer is freed from the task of performing detailed numerical analysis. Currently available numerical analysis tools such as computational fluid dynamics (CFD) codes are given a role in an undertaking to create databases from which fast design codes are developed     

Tools for the computer-aided design of multigigahertzsuperconducting digital circuits

The realization of large integrated circuits depends upon the application of computer-aided design (CAD) tools. This paper summarizes the results of a survey of CAD tools targeting superconducting digital electronics. Five categories of tools: circuit simulators, circuit optimizers, layout tools, inductance estimators, and logic simulators are discussed in detail. Within each category, a comparison of several currently available CAD tools is presented, and a tool which has been adapted for use or developed at the University of Rochester is discussed in greater detail. In addition, tools for timing analysis as well as integrated design environments that permit the effective data interchange among various tools and support libraries of design models are discussed. Future tools for timing optimization, automated logic synthesis, and automated layout synthesis are shown to be necessary for the design of superconducting circuits at the very large scale of integration (VLSI) level of integration. Trends regarding changes in the requirements for effective CAD tools are discussed, and expected improvements to existing tools and features of new tools currently under development are presented     

A practical methodology for the statistical design of complex logicproducts for performance

Contradictory trends in the industrial design environment have increased uncertainty while decreasing the tolerance to uncertainty. Worst case design techniques, still widely used in industry, do not provide the accuracy required to design under these conditions. On the other hand, statistical design techniques do provide a significant improvement in accuracy, by virtue of their “circuit adaptive” behavior, but at a substantial cost in computational effort. One practical solution to improving the accuracy of worst case design without sacrificing efficiency is considered here. It integrates an efficient statistical circuit simulator with worst case design tools into a hierarchical performance design process. It employs two stages of worst case analysis, calibrated with statistical circuit simulation, serving as filters to screen out circuits that easily meet their performance requirements. This focuses the use of statistical circuit simulation on those circuits for which the improved accuracy provides significant benefit. This methodology has been applied with outstanding results in design and manufacturing     

The integration of simulation and response surface methodology forthe optimization of IC processes

This paper describes a methodology that can be used for the optimization of semiconductor processes. This is achieved by integrating the design of experiments and Response Surface Methodology (RSM) with process and device simulation tools. Software for automating multiple simulations has been implemented and interfaced to RS/1 to assist in the manual design and analysis of experiments and the subsequent optimization procedures. The procedure is illustrated through the optimization of part of an MOS process with multi-parameter optimization being performed by the introduction of composite responses and sensitivity analysis. These simulated results are also compared with experimental measurements     

D优化的实验设计在IC工艺和器件优化中的应用

论述了实验设计技术与ＴＣＡＤ相结合用于半导体工艺和器件性能优化的新途径，它可以极大地减少开发和研制新工艺、新器件的时间和成本。     

Programmable Melt Electrowriting to Engineer Soft Connective Tissues with Prescribed,Biomimetic, Biaxial Mechanical Properties

Appropriate load-bearing function of soft connective tissues is provided by their nonlinear and often anisotropic mechanics. Recapitulating such complex mechanical behavior in tissue-engineered structures is particularly crucial, as deviation from native tissue mechanics can trigger pathological biomechanical pathways, causing adverse tissue remodeling and dysfunction. Here, a novel method combining computational modeling, melt electrowriting (MEW), and design of experiments (DOE) is reported to generate scaffolds composed of sinusoidal fibers with prescribed biaxial mechanical properties, recapitulating the distinct nonlinear, anisotropic stress–strain behavior of three model tissues: adult aortic valve, pediatric pulmonary valve, and pediatric pericardium. Finite element analysis is used to efficiently optimize scaffold architecture over a broad parameter space, representing up to 65 conditions, to define MEW print parameters to achieve polycaprolactone scaffolds with target mechanical properties. Architectural parameters are further optimized experimentally using DOE and regression to account for uncertainties involved in the simulation, yielding functional scaffolds with accurate, prescribed mechanics. The prescribed architecture also primarily governs the mechanics of hybrid structures generated by casting cell-laden fibrin hydrogel within the scaffolds. This high-fidelity approach recapitulates biaxial mechanical properties over a broad range of mechanical nonlinearity and anisotropy and is generalizable for programmed biofabrication in a variety of tissue engineering applications.     

An FIR Equalizer Design for Nonlinear Channels Using Hybrid GA and LMI

In this paper, a finite impulse response (FIR) equalizer for nonlinear discrete-time channels is designed by employing a hybrid genetic algorithm (GA) and linear matrix inequality (LMI) approach from an H_∞ perspective. The GA technique is utilized to linearize the nonlinear channel model, and the approximate error can be viewed as a state uncertainty. Then, the design of the FIR equalizer is transformed into LMIs, and the coefficients of the FIR equalizer can be obtained by solving an LMI optimization problem. Finally, numerical examples are included to illustrate the effectiveness of the proposed methodology.     

Genetic algorithms in optimization of system reliability

After initial production, improvements are often made to components of a system, to upgrade system performance; for example, when designing a later version or release. This paper presents an optimization model that identifies the types of component improvements and the level of effort spent on those improvements to maximize one or more performance measures (e.g., system reliability or availability) subject to constraints (e.g., cost) in the presence of uncertainty about the component failure rates. For each component failure mode, some possible improvements are identified along with their cost and the resulting improvement in failure rates for that failure mode. The objective function is defined as a stochastic function of the performance measure of interest-in this case, 5^th percentile of the mean time-between-failure distribution. The problem formulation is combinatorial and stochastic. Genetic algorithms are used as the solution method. Our approach is demonstrated on a case study of a personal computer system. Results and comparison with enumeration of the configuration space show that genetic algorithms perform very favorably in the face of noise in the output: they are able to find the optimum over a complicated, high dimensional, nonlinear space in a tiny fraction of the time required for enumeration. The integration of genetic algorithm optimization capabilities with reliability analysis can provide a robust, powerful design-for-reliability tool     

Design of computer controlled combustion engines

Globalization and growing new markets, as well as increasing emission and fuel consumption requirements, force the car manufacturers and their suppliers to develop new engine control strategies in shorter time periods. This can mainly be reached by development tools and an integrated hardware and software environment enabling rapid implementation and testing of advanced engine control algorithms.

The structure of a rapid control prototyping (RCP) system is explained, which allows fast measurement signal evaluation, and rapid prototyping of advanced engine control algorithms. A hardware-in-the-loop simulator for diesel engine control design is illustrated, simulation results for a 40 tons truck are presented. Providing efficient engine models for the proposed development tools, a dynamic local linear neural network approach is explained and applied for modelling the NO_x emission characteristics of a 1.9 l direct injection diesel engine. Furthermore the application of a RCP system is exemplified by the application of combustion pressure based closed-loop ignition timing control for a SI engine. Experimental results are shown for a 1.0 l SI engine on a dynamic engine test stand.