Performance projections and design optimization of planar double gate SOI MOSFETs for logic technology applications

In this paper, by investigating the influence of source/drain extension region engineering (also known as gate-source/drain underlap) in nanoscale planar double gate (DG) SOI MOSFETs, we offer new insights into the design of future nanoscale gate-underlap DG devices to achieve ITRS projections for high performance (HP), low standby power (LSTP) and low operating power (LOP) logic technologies. The impact of high-kappa gate dielectric, silicon film thickness, together with parameters associated with the lateral source/drain doping profile, is investigated in detail. The results show that spacer width along with lateral straggle can not only effectively control short-channel effects, thus presenting low off-current in a gate underlap device, but can also be optimized to achieve lower intrinsic delay and higher on-off current ratio (I-on/I-off). Based on the investigation of on-current (I-on), off-current (I-off), I-on/I-off, intrinsic delay (tau), energy delay product and static power dissipation, we present design guidelines to select key device parameters to achieve ITRS projections. Using nominal gate lengths for different technologies, as recommended from ITRS specification, optimally designed gate-underlap DG MOSFETs with a spacer-to-straggle (s/sigma) ratio of 2.3 for HP/LOP and 3.2 for LSTP logic technologies will meet ITRS projection. However, a relatively narrow range of lateral straggle lying between 7 to 8 nm is recommended. A sensitivity analysis of intrinsic delay, on-current and off-current to important parameters allows a comparative analysis of the various design options and shows that gate workfunction appears to be the most crucial parameter in the design of DG devices for all three technologies. The impact of back gate misalignment on I-on, I-off and tau is also investigated for optimized underlap devices.

Uncertainty and Sensitivity Analysis in Aircraft Operating Costs in Structural Design Optimization

The paper focuses on the development of an aircraft design optimization methodology that models uncertainty and sensitivity analysis in the tradeoff between manufacturing cost, structural requirements, andaircraft direct operating cost.Specifically,ratherthanonlylooking atmanufacturingcost, direct operatingcost is also consideredintermsof the impact of weight on fuel burn, in addition to the acquisition cost to be borne by the operator. Ultimately, there is a tradeoff between driving design according to minimal weight and driving it according to reduced manufacturing cost. Theanalysis of cost is facilitated withagenetic-causal cost-modeling methodology,andthe structural analysis is driven by numerical expressions of appropriate failure modes that use ESDU International reference data. However, a key contribution of the paper is to investigate the modeling of uncertainty and to perform a sensitivity analysis to investigate the robustness of the optimization methodology. Stochastic distributions are used to characterize manufacturing cost distributions, andMonteCarlo analysis is performed in modeling the impact of uncertainty on the cost modeling. The results are then used in a sensitivity analysis that incorporates the optimization methodology. In addition to investigating manufacturing cost variance, the sensitivity of the optimization to fuel burn cost and structural loading are also investigated. It is found that the consideration of manufacturing cost does make an impact and results in a different optimal design configuration from that delivered by the minimal-weight method. However, it was shown that at lower applied loads there is a threshold fuel burn cost at which the optimization process needs to reduce weight, and this threshold decreases with increasing load. The new optimal solution results in lower direct operating cost with a predicted savings of 640=m2 of fuselage skin over the life, relating to a rough order-of-magnitude direct operating cost savings of $500,000 for the fuselage alone of a small regional jet. Moreover, it was found through the uncertainty analysis that the principle was not sensitive to cost variance, although the margins do change.

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes.A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.

Virtual Engineering Centre - Examples of Virtual Prototyping and Multidisciplinary Design Optimization

The requirement for the use of Virtual Engineering, encompassing the construction of Virtual Prototypes using Multidisciplinary Design Optimisation, for the development of future aerospace platforms and systems is discussed. Some of the activities at the Virtual Engineering Centre, a University of Liverpool initiative, are described and a number of case studies involving a range of applications of Virtual Engineering illustrated.

Design, optimization and characterisation of polymeric microneedle arrays prepared by a novel laser-based micromoulding technique

Design and evaluation of a novel laser-based method for micromoulding of microneedle arrays from polymeric materials under ambient conditions. The aim of this study was to optimise polymeric composition and assess the performance of microneedle devices that possess different geometries.

Multi-Fidelity Multidisciplinary Whole Engine Thermo-Mechanical Design Optimization

Traditionally, the optimization of a turbomachinery engine casing for tip clearance has involved either twodimensional transient thermomechanical simulations or three-dimensional mechanical simulations. This paper illustrates that three-dimensional transient whole-engine thermomechanical simulations can be used within tip clearance optimizations and that the efficiency of such optimizations can be improved when a multifidelity surrogate modeling approach is employed. These simulations are employed in conjunction with a rotor suboptimization using surrogate models of rotor-dynamics performance, stress, mass and transient displacements, and an engine parameterization.

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Continuous research endeavors on hard turning (HT), both on machine tools and cutting tools, have made the previously reported daunting limits easily attainable in the modern scenario. This presents an opportunity for a systematic investigation on finding the current attainable limits of hard turning using a CNC turret lathe. Accordingly, this study aims to contribute to the existing literature by providing the latest experimental results of hard turning of AISI 4340 steel (69 HRC) using a CBN cutting tool. An orthogonal array was developed using a set of judiciously chosen cutting parameters. Subsequently, the longitudinal turning trials were carried out in accordance with a well-designed full factorial-based Taguchi matrix. The speculation indeed proved correct as a mirror finished optical quality machined surface (an average surface roughness value of 45 nm) was achieved by the conventional cutting method. Furthermore, Signal-to-noise (S/N) ratio analysis, Analysis of variance (ANOVA), and Multiple regression analysis were carried out on the experimental datasets to assert the dominance of each machining variable in dictating the machined surface roughness and to optimize the machining parameters. One of the key findings was that when feed rate during hard turning approaches very low (about 0.02mm/rev), it could alone be most significant (99.16%) parameter in influencing the machined surface roughness (Ra). This has, however also been shown that low feed rate results in high tool wear, so the selection of machining parameters for carrying out hard turning must be governed by a trade-off between the cost and quality considerations.

Multiobjective design optimization of IGBT power modules considering power cycling and thermal cycling

Insulated-gate bipolar transistor (IGBT) power modules find widespread use in numerous power conversion applications where their reliability is of significant concern. Standard IGBT modules are fabricated for general-purpose applications while little has been designed for bespoke applications. However, conventional design of IGBTs can be improved by the multiobjective optimization technique. This paper proposes a novel design method to consider die-attachment solder failures induced by short power cycling and baseplate solder fatigue induced by the thermal cycling which are among major failure mechanisms of IGBTs. Thermal resistance is calculated analytically and the plastic work design is obtained with a high-fidelity finite-element model, which has been validated experimentally. The objective of minimizing the plastic work and constrain functions is formulated by the surrogate model. The nondominated sorting genetic algorithm-II is used to search for the Pareto-optimal solutions and the best design. The result of this combination generates an effective approach to optimize the physical structure of power electronic modules, taking account of historical environmental and operational conditions in the field.

An efficient genetic algorithm for the design optimization of cold-formed steel portal frame buildings

The design optimization of a cold-formed steel portal frame building is considered in this paper. The proposed genetic algorithm (GA) optimizer considers both topology (i.e., frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables. Previous GAs in the literature were characterized by poor convergence, including slow progress, that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is shown to be an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Thus, population diversity is maintained and premature convergence is reduced significantly. Through benchmark examples, it is shown that the efficient GA proposed generates optimal solutions more consistently. A parametric study was carried out, and the results included. They show significant variation in the optimal topology in terms of pitch and frame spacing for a range of typical column heights. They also show that the optimized design achieved large savings based on the cost of the main structural elements; the inclusion of knee braces at the eaves yield further savings in cost, that are significant.

Advanced design optimization of cold-formed steel portal frame buildings

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The objective function is based on the cost of the members for the main frame and secondary members (i.e., purlins, girts, and cladding for walls and roofs) per unit area on the plan of the building. A real-coded niching genetic algorithm is used to minimize the cost of the frame and secondary members that are designed on the basis of ultimate limit state. It iis shown that the proposed algorithm shows effective and robust capacity in generating the optimal solution, owing to the population's diversity being maintained by applying the niching method. In the optimal design, the cost of purlins and side rails are shown to account for 25% of the total cost; the main frame members account for 27% of the total cost, claddings for the walls and roofs accounted for 27% of the total cost.

Building information modeling based building design optimization for sustainability

Environmental problems, especially climate change, have become a serious global issue waiting for people to solve. In the construction industry, the concept of sustainable building is developing to reduce greenhouse gas emissions. In this study, a building information modeling (BIM) based building design optimization method is proposed to facilitate designers to optimize their designs and improve buildings’ sustainability. A revised particle swarm optimization (PSO) algorithm is applied to search for the trade-off between life cycle costs (LCC) and life cycle carbon emissions (LCCE) of building designs. In order tovalidate the effectiveness and efficiency of this method, a case study of an office building is conducted in Hong Kong. The result of the case study shows that this method can enlarge the searching space for optimal design solutions and shorten the processing time for optimal design results, which is really helpful for designers to deliver an economic and environmental friendly design scheme.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions.