Co-optimization Method Research Articles

Mitigating the Impact of Mask Absorber Error on Lithographic Performance by Lithography System Holistic Optimization

The non-ideal mask absorber can cause an increase in critical dimension error (CDE) and decrease in process window (PW). However, the random mask absorber errors induced during mask fabricating and measuring are not considered in computational lithography. The problem cannot be neglected as the continuous scaling of lithography technology node. In this work, for the first time to our knowledge, a source, numerical aperture (NA), and process parameters co-optimization (SNPCO) method is developed to reduce the CDE induced by absorber errors and improve the PW. First, the source is represented by Zernike polynomials to balance computational burden and flexibility of source. Then a weighted cost function containing CDE and PW that incorporates the influences of absorber errors is created. Finally, a statistical optimization method is used to optimize the lithographic system parameters. Simulations of 1D mask pattern show that for the system with extreme absorber errors, the pattern errors of the proposed method are reduced by 62.1% and 58.9%, and the PWs are increased by 40.3% and 36.4%, respectively. The results illustrate that this method is effective in mitigating the CDE caused absorber errors and improving process robustness.

Robust planning-operation co-optimization of energy hub considering precise model of batteries’ economic efficiency

Abstract The increasing penetration of renewable energy in integrated energy system (IES) makes it necessary to consider the uncertainty and an appropriate investment and operation for energy storage systems (ESS). In this paper, we propose a robust planning-operation co-optimization method for Energy Hub (EH) considering the sizing problem and precise model of batteries’ economic efficiency, in which batteries’ total cost and the lifetime loss cost is mainly considered. The problem is formulated as a two-stage robust optimization model, in which the second-stage problem has integer variables. To solve the problem, dual norms are used to transform the uncertain constraints into robust counterparts, then the problem is rearranged and can be solved by Benders Decomposition. Through case studies, the benefits on the flexibility and economy of EH brought by precise ESS model is analyzed. And the consideration of lifetime factor and robust optimization strengthen the economy and reliability of EH, respectively.

Co-optimization of ethnic-pattern segmentation based on hierarchical patch matching

The segmentation of ethnic patterns is vital for digital analyses of ethnic cultures. Although existing image segmentation methods can properly segment natural images, they cannot preserve the main structure of the image. They also require many user interactions to segment ethnic patterns. This paper presents a co-optimization method that segments ethnic patterns using hierarchical patch matching. Exploiting the repetitive characteristics of ethnic patterns, the method first automatically detects all similar patterns using a global patch match. Second, the relative orientation between similar patterns is estimated by a local patch match, and an accurate dense correspondence is constructed by a constrained patch match. Finally, the pre-segmentation of ethnic patterns is co-optimized to preserve their main structures. Our method can segment all similar ethnic patterns into separate elements with dense correspondence. Besides reducing the user interaction and improving the segmentation accuracy, the proposed method improves the quality of ethnic pattern digital analysis such as vectorization. Experiments demonstrated the validity of our method.

Co-placement optimization in sensor-reusable cyber-physical digital microfluidic biochips

Digital microfluidic biochips (DMFBs) facilitate modern healthcare applications. One of the most important applications is point-of-care (POC) clinical diagnosis which is directly related to human health. However, biochemical experiments are usually error-prone, which makes monitoring intermediate results during bioassay execution be required to ensure the correctness of POC clinical diagnosis. To tackle this problem, cyber-physical digital microfluidic biochips with integrated sensors have been proposed and have attracted attention recently. In order to fully utilize the sensors for detection, the reusability of sensors should be taken into consideration during the module placement stage of cyber-physical DMFBs synthesis flow. Moreover, excessive actuation of electrodes may cause reliability degradation and this issuse should also be taken care of during module placement stage. In order to deal with the aforementioned problems, this paper presents the first co-optimization method for both module and sensor placement. Experimental results show that the proposed method can effectively minimize bioassay completion time while meeting all constraints from sensors and electrodes.

Connectivity-based optimization of vehicle route and speed for improved fuel economy

Traditionally, vehicle route planning problem focuses on route optimization based on traffic data and surrounding environment. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP) problem, to optimize vehicle route and speed simultaneously using both traffic data and vehicle characteristics to improve fuel economy for a given expected trip time. The required traffic data and neighbouring vehicle dynamic parameters can be collected through the vehicle connectivity (e.g. vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, etc.) developed rapidly in recent years. A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem. It is able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) with a Simulink powertrain model, is developed to validate the proposed VMMP method. Four simulation studies, based on a real traffic network, are conducted for validating the proposed VMMP: (1) ideal traffic environment without traffic light and jam for studying the fuel economy improvement, (2) traffic environment with traffic light for validating the proposed traffic light penalty model, (3) traffic environment with traffic light and jam for validating the proposed adaptive real-time optimization strategy, and (4) investigating the effect of different powertrain platforms to fuel economy using two different vehicle platforms. Simulation results show that the proposed VMMP method is able to improve vehicle fuel economy significantly. For instance, comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%.

A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle

The transmission system together with power management will simultaneously affect the fuel economy of the plug-in hybrid electric vehicle (PHEV) with automated mechanical transmission (AMT). This paper strives to make three contributions to realize systematic design and optimization of the transmission and power management. Firstly, a design of experiment method is proposed to redesign the current transmission system of the PHEV with Optimal Latin Hypercube Design algorithm. Then, a co-optimization method is proposed to insight into the preferable speed ratios of the redesigned transmission system with multi-island genetic and dynamic programming algorithms. Finally, a Pontryagin’s Minimum Principle-based self-identification controller is proposed to realize adaptive power management control, based on a significant finding that the constant solution of the co-state from off-line iteration optimization can be approximately identified by the mean value of the co-state with time-moving in the self-identification controller. Results demonstrate that the current 6-speed ratio AMT can be reduced to 4, the fuel economy of the redesigned transmission system can be improved by 2.9% compared to the current transmission system and the self-identification controller can further improve the fuel economy of the PHEV compared to the conventional PI controller.

The synergistic role of renewable energy integration into the unit commitment of the energy water nexus

In recent years, significant attention has been given to renewable energy integration within the context of global climate change. In the meantime, the energy-water nexus literature has recognized that the electricity & water infrastructure that enables the production, distribution, and consumption of these two precious commodities is intertwined. While these two issues may seem unrelated, their resolution is potentially synergistic in that renewable energy technologies not only present low CO2 emissions but also low water-intensities as well. Therefore, renewable energy integration has the potential to address both sustainability concerns. And yet, renewable energy integration studies have yet to methodologically consider an integrated energy-water infrastructure. Many of these works rely on a coupled unit commitment-economic dispatch simulation. Recently, a simultaneous co-optimization method has been contributed for the economic dispatch of networks that include water, power, and co-production facilities. This paper builds upon this foundation with the development of the corresponding unit commitment problem. It demonstrates the optimization on several case studies inspired by Singapore & the Middle East. It concludes that renewable energy simultaneously reduces CO2 emissions and water withdrawals. Furthermore, it shows how water storage can help alleviate binding co-production constraints, flatten production profiles and reduce production cost levels.

A Multi-Time Scale Co-Optimization Method for Sizing of Energy Storage and Fast-Ramping Generation

Power systems need to have sufficient generation capacity to support the demand at all times. In addition, dispatchable generation resources should be capable of adjusting their power output on a short term basis not only to alleviate uncertainties of nondispatchable generation and load fluctuations but also to correct for forecast errors. This paper presents a long-term planning approach to co-optimize capacities of energy storages and fast-ramping generation. We model and integrate the capability of the storage to provide multiple services for the system. Our formulation takes into account wind generation and demand forecast errors as well as short-term fluctuations. A stochastic optimization problem is formulated consisting of hourly and intrahour time scales. The approach determines the optimal size of newly deployed generation and storage resources to provide adequate generation capacity and ramping needed to support hourly demand. Additionally, our method ensures that the system is capable of following the net load in intrahour time intervals, as well as mitigating the impact of short-term wind power and load fluctuations. In this formulation, power balance, network security, and system ramping capability are stochastic constraints being modeled as chance constraints. A 3-bus and the IEEE 24-bus test systems are studied to show the effectiveness of the proposed approach.

Frame weight and anti-fatigue co-optimization of a mining dump truck based on Kriging approximation model

Abstract In order to reduce frame redundant weight caused by the traditional experience design and delay the crack initiation occurrences at local positions during early service period, a structural weight and lifetime co-optimization method for frame of a mining dump truck based on Kriging approximation model was proposed. Considered as the most sensitive factor to the frame fatigue life and weight, the thickness of several steel plates was chosen as design variables in terms of un-allowed change of structure shape, while achieving weight reduction and improving fatigue life were together regarded as the optimization targets. An experiment design with 20 sample points obtained by the Latin hypercube sampling method was conducted to figure out the sensitivity of design variables, whose response values were acquired through repeated simulations. Those data were supposed to set up the approximation model constructed based on the Kriging interpolation technique and its fitting precision was certified by comparison of the finite element computational results and the approximation model calculated ones. The non-dominated sorting genetic algorithm II (NSGA-II) was utilized to optimize the thickness of the steel plates based on the approximation model. The tolerance between the results of the simulation and the approximation model was less than 1% when using the optimal design variables and the weight of the optimized frame was lessened by 22.3% while the minimum fatigue life and maximum static stress were only decreased by 3.8% and 4.6%, compared with the initial frame. These optimized results were acceptable for frame lightweight almost without expense of fatigue life and static strength.

Multidisciplinary Design Optimization of Crankshaft Structure Based on Cooptimization and Multi-Island Genetic Algorithm

The feasibility design method with multidisciplinary and multiobjective optimization is applied in the research of lightweight design and NVH performances of crankshaft in high-power marine reciprocating compressor. Opt-LHD is explored to obtain the experimental scheme and perform data sampling. The elliptical basis function neural network (EBFNN) model considering modal frequency, static strength, torsional vibration angular displacement, and lightweight design of crankshaft is built. Deterministic optimization and reliability optimization for lightweight design of crankshaft are operated separately. Multi-island genetic algorithm (MIGA) combined with multidisciplinary cooptimization method is used to carry out the multiobjective optimization of crankshaft structure. Pareto optimal set is obtained. Optimization results demonstrate that the reliability optimization which considers the uncertainties of production process can ensure product stability compared with deterministic optimization. The coupling and decoupling of structure mechanical properties, NVH, and lightweight design are considered during the multiobjective optimization of crankshaft structure. Designers can choose the optimization results according to their demands, which means the production development cycle and the costs can be significantly reduced.

Design of a Novel UWB Omnidirectional Antenna Using Particle Swarm Optimization

A UWB E-plane omnidirectional microwave antenna is designed and fabricated for IEEE 802.11a communication system and microwave magnetron source system as a radiation monitor. A cooptimization method based on particle swarm optimization (PSO) algorithm and FDTD software is presented. The presented PSO algorithm is useful in many industrial microwave applications, such as microwave magnetron design and other techniques with a high power level. The maximum measured relative bandwidth of 65% is achieved for the proposed antenna after a rapid and efficient optimization. Furthermore, the measured antenna polarization purity reaches about 20 dB at the communication C band. The PSO algorithm is a powerful candidate for microwave passive component design.

Parametric source-mask-numerical aperture co-optimization for immersion lithography

Source mask optimization (SMO) is a leading resolution enhancement technique in immersion lithography at the 45-nm node and beyond. Current SMO approaches, however, fix the numerical aperture (NA), which has a strong impact on the depth of focus (DOF). A higher NA could realize a higher resolution but reduce the DOF; it is very important to balance the requirements of NA between resolution and the DOF. In addition, current SMO methods usually result in complicated source and mask patterns that are expensive or difficult to fabricate. This paper proposes a parametric source-mask-NA co-optimization (SMNO) method to improve the pattern fidelity, extend the DOF, and reduce the complexity of the source and mask. An analytic cost function is first composed based on an integrative vector imaging model, in which a differentiable function is applied to formulate the source and mask patterns. Then, the derivative of the cost function is deduced and a gradient-based algorithm is used to solve the SMNO problem. Simulation results show that the proposed SMNO can achieve the optimum combination of parametric source, mask, and NA to maintain high pattern fidelity within a large DOF. In addition, the complexities of the source and mask are effectively reduced after optimization.

Optimization of Test Pin-Count, Test Scheduling, and Test Access for NoC-Based Multicore SoCs

We present the first pin-count-aware optimization approach for test data delivery over a network-on-chip (NoC). Our approach addresses the key issue of optimal utilization of the limited I/O resources provided by automated test equipment (ATE) to keep test time and test cost under control. By co-optimizing core test scheduling and pin assignment to access points, test cost can be lowered by reducing test time for a given pin budget, or by reducing the number of test pins without impacting test time. To further improve resource utilization, we consider the use of MISRs for compacting the test responses of embedded cores. We show how the optimization framework can be extended to include power constraints during test application. Experimental results for ITC'02 test benchmarks demonstrate that pin-count-aware co-optimization leads to shorter test times for a given pin-count budget and fewer pins for a given test-time budget. Comparisons with dedicated bus-based test-access mechanism designs and a baseline heuristic method are presented; our results demonstrate the benefits of the co-optimization method for NoC-based test access. The results also highlight the advantages of the proposed use of output compaction and the impact of power constraints on the optimized test schedule and test-access architecture.

The impact of storage facility capacity and ramping capabilities on the supply side economic dispatch of the energy–water nexus

Clean energy and water are two essential resources that any society must securely deliver in order to develop sustainably. Recently, the energy and water infrastructure value chains have gained attention as a single interlinked system of global concern called the energy–water nexus. In light of these couplings, energy and water, as two valuable resources require co-optimization. Recently, one such simultaneous co-optimization method has been contributed for the economic dispatch of networks that include water, power and co-production facilities. This paper builds upon this foundation with the introduction of plant ramping behavior. Furthermore, it investigates the impact of electrical energy and water storage as a technology that can help to alleviate binding constraints and lead to more flat production and reduced cost levels. Three cases studies are presented; a base case, a second case inspired by Singapore's limited water storage availability, and a third case relevant to countries in the Middle East where water storage facilities can be readily constructed. Storage facilities are shown to reduce total operating costs by up to 38% and lead to less variable daily production suggesting that they have an important role to play in the optimization of the energy–water nexus.

Impact of realistic source shape and flexibility on source mask optimization

Source mask optimization (SMO) is widely used to make state-of-the-art semiconductor devices in high-volume manufacturing. To realize mature SMO solutions in production, the Intelligent Illuminator, which is an illumination system on a Nikon scanner, is useful because it can provide generation of freeform sources with high fidelity to the target. Proteus SMO, which employs co-optimization method and an insertion of validation with mask three-dimensional effect and resist properties for an accurate prediction of wafer printing, can take into account the properties of Intelligent Illuminator. We investigate an impact of the source properties on the SMO to pattern of a static random access memory. Quality of a source made on the scanner compared to the SMO target is evaluated with in-situ measurement and aerial image simulation using its measurement data. Furthermore, we discuss an evaluation of a universality of the source to use it in multiple scanners with a validation and with estimated value of scanner errors.

Simultaneous Optimization of Droplet Routing and Control-Pin Mapping to Electrodes in Digital Microfluidic Biochips

The number of independent input pins used to control the electrodes in digital microfluidic “biochips” is an important cost-driver in the emerging market place, especially for disposable PCB devices that are being developed for clinical and point-of-care diagnostics. However, most prior work on pin-constrained biochip design considers droplet routing and the assignment of pins to electrodes as independent problems. In this paper, we propose optimization methods to solve the droplet routing and pin-constrained design problems concurrently. First, we formulate the co-optimization problem involving droplet routing and pin-mapping. Next, we present an integer linear programming-based optimization method to solve the droplet-routing and the pin-mapping design problems concurrently. The proposed co-optimization method minimizes the number of control pins. We also present an efficient heuristic approach to tackle the co-optimization problem. These methods overcome a major drawback of a recently proposed method, which leads to infeasible solutions involving conflicts in the mapping of pins to electrodes in different droplet-routing stages. The effectiveness of the proposed co-optimization method is demonstrated for two commercial biochips and an experimental university chip for multiplexed in-vitro diagnostics.