Nominal Process Condition Research Articles

Adjusting plant operating conditions to widen multivariate specification regions for incoming raw materials – An optimization framework

Decision tools to determine the acceptability of an incoming lot of raw materials prior to its purchase from a supplier is essential to ensure efficient quality control. They allow mitigating the propagation of the raw material variability through the process and, ultimately, in the final product quality. Establishing multivariate specifications regions for raw material properties to achieve high and consistent end product quality using latent variable models is key. However, these specifications are typically designed assuming the process operates at nominal conditions. The objective of this work is to enlarge the raw material acceptance region to accept materials having a wider range of properties, and possibly at a lower cost, while meeting final product quality requirements. Using a sequential multi-block partial least squares model (SMB-PLS), the design of multivariate specification regions is combined with optimization of process operating conditions to better cope with fluctuations in raw material properties. An economic criterion is used to assess whether accepting lower cost materials, at the expense of adjusting plant operation, which incurs additional costs, is economically justified. The approach is illustrated using a grinding-flotation plant simulator where the objective is to assess the profitability of different lots of raw materials (i.e. ores). The impact of measurement noise is also investigated. The case studies show that the raw material processability is improved since the number of correctly accepted lots is increased by 7.3 % in comparison with maintaining nominal process conditions.

Process monitoring of electron-beam based writing of semiconductor mask patterns

As mask capabilities advance to support optical lithography, mask-making processes become more complex and therefore require more sophisticated monitoring to ensure manufactured masks are defect-free. We present a methodology that aggregates process signatures from writings of different mask patterns under nominal process conditions and detects anomalous process behaviors during writings of new mask patterns. The new approach was demonstrated using simulated electron beam writing of integrated circuit patterns subjected to inconsistent electron beam dosage and spot size. Anomalous process behaviors were detected and tracked quantitatively even if new mask patterns were unlike past mask patterns.

Insight into ion exchange column dynamics through application of an analytical model of system performance

ABSTRACT The Direct Feed Low-Activity Waste flowsheet provides for the early production of immobilized low-activity waste (LAW) by feeding waste directly from Hanford tank farms to the Hanford Tank Waste Treatment and Immobilization Plant (WTP) for vitrification. Prior to the transfer of feed to the WTP LAW Vitrification Facility, tank waste supernatant will be pretreated by the Tank Side Cesium Removal (TSCR) system to meet the WTP LAW waste acceptance criteria (<3.18E-5 Ci 137Cs/mole of Na). This pretreatment will remove cesium from the waste stream using ion exchange (IX). The selected media for IX is crystalline silicotitanate (CST), product number IONSIV-R9140-B, manufactured by Honeywell UOP LLC (Des Plaines, IL). Testing was conducted to improve the understanding of the parameters that impact dynamic ion-exchange performance and to better define IX processing using Low-Activity Waste Pretreatment System (LAWPS) prototypic IX unit operations. Column testing at full height scale was conducted to gain insight into ion-exchange column dynamics and evaluate the application of an analytical model on system performance. Nominal process conditions used 5.6 M Na simulant processed in a lead/lag column format containing 1.15 L 18 × 15 mesh CST in 2.54-cm-diameter columns, 226 cm tall CST beds. Two process flowrates were tested: 1.30 and 1.82 bed volumes (BV) per hour. This work demonstrated that an analytical method can provide a reasonable estimate of the expected performance within the bounds of the assumptions employed for the analytical solution.

Improved fractional filter IMC–PID controller design for enhanced performance of integrating plus time delay processes

ABSTRACTIn this paper, an improved fractional filter IMC–PID controller was proposed to enhance the performance of integrating processes after identifying the optimum higher order fractional IMC filter structure. The identification involves a systematic procedure based on the minimisation of integral absolute error (IAE). The present design also considers different approximations for time delay. The tuning parameters are obtained based on the prefixed robustness (maximum sensitivity, Ms). The present method is compared with recent methods in the literature and its superiority is demonstrated with performance measures IAE and total variation (TV). Enhanced output performance is obtained for nominal process conditions; for process parameter variations and for noise in the measurement. Robustness analysis is performed using complementary sensitivity function and parametric uncertainty bounds. Also, the fragility analysis is carried out to check the sensitivity of closed loop system for variation in controller parameters.

Infrared thermal imaging for melt pool analysis in SLM: a feasibility investigation

ABSTRACTMelt pool dimension can help to relate process parameters and build part quality in selective laser melting (SLM) process. In this study, a near-infrared thermal imager (about 670 nm spectral range) was employed to collect powder layer thermal signal in SLM machine using nickel-based alloy as raw powder material. Radiant temperature distribution at different build heights has been acquired and melt pool sizes have been analysed. The major findings are as follows: (1) It is possible to estimate melt pool dimension based on the identified radiant liquidus temperature and appropriate thermal imager setting, but it is difficult to obtain true temperature. (2) At nominal process conditions of 600 mm/s beam speed and 180 W beam power for Inconel 718 powder, the melt pool has a length of about 0.36 mm and a width of about 0.21 mm. Build height seems to have little effect on melt pool dimensions.

Improving Operation of a 2.5kW SOFC Power System with Supervisory Control

Operation of the SOFC power system can be improved by upgrading the low-level controllers with a supervisory controller (see Figure 1). In this work a simple, model-free supervisory optimizer that adjusts references for the low level controllers is proposed. The optimization problem for the SOFC power system is formulated with the cost function depending on the electrical efficiency of the system and deviations of the system temperatures from their limits. Electrical efficiency is calculated as the ratio of the net electrical power to the total power obtained by burning the fuel. The dominant parasitic losses of the system are assumed to be mainly related to the air blower consumption. Based on efficiency analysis, which demands a number of simulations on SOFC power system model, the references for the burner lambda and stack outlet and inlet air temperatures are selected as optimization variables. The optimization problem is solved by using the extremum-seeking approach where optimum is sought directly on the process. An advantage of this approach is that it does not need a model of the system and exceeds the weakness of model-based optimizers, which assume nominal process condition all the time and do not take into account the degradation processes in fuel cells. Its disadvantage is it needs to probe the system at all time by imposing small variations on manipulated variables and delivers a convergence speed which is somewhat slower compared to the model based approach. A simple algorithm is designed to perform the model-free optimization. The algorithm is additionally augmented to incorporate changes in the current load in order to increase the convergence speed of the optimization at large load changes. The efficiency of the optimizer is governed by properly tuned parameters of the optimization algorithm. Proposed supervisory optimizer is assessed on a simulated detailed physical model of a 2.5 kW SOFC power system by applying large step current load changes. Simulation results indicate that the optimizer improves efficiency of the system by approximately 10 % compared to the low-level controllers and keeps the system temperatures within the safe ranges (see Figure 2). Due to its simplicity the optimizer appears appropriate for practical applications. Figure 1

Fast Lithographic Mask Optimization Considering Process Variation

As nanometer technology advances, conventional optical proximity correction (OPC) that minimizes the edge placement error (EPE) at the nominal process condition alone often leads to poor process windows. To improve the mask printability across various process corners, process-window OPC optimizes EPE for multiple process corners, but often suffers long runtime, due to repeated lithographic simulations. This paper presents an efficient process variation (PV)-aware mask optimization framework, namely PVOPC, to simultaneously minimize EPE and PV band with fast convergence. The PVOPC framework includes EPE-sensitivity-driven dynamic fragmentation, PV-aware EPE modeling, and correction with three new EPE-converging techniques and a systematic subresolution-assisted feature insertion algorithm. Experimental results show that our approach efficiently achieves high-quality EPE and PV band results.

Modeling, optimization, and cost analysis of an IGCC plant with a membrane reactor for carbon capture

This article presents a theoretical study on the integration of a membrane reactor (MR) for carbon capture into an integrated gasification combined cycle (IGCC) plant. First‐principles, simplified systems‐level models for the individual IGCC units and the MR are introduced for their subsequent plantwide integration. The integrated plant model is then used for simulation studies that assume different MR characteristics. Using this model, an optimization problem is formulated to analyze the MR effects when adding it to the IGCC plant, while satisfying all of the process constraints in streams and performance variables. The solution of this optimization problem indicates improvements in the original case studies, including capital cost savings as high as $18 million for the optimal case under nominal process conditions. To determine the cost implications of inserting the MR into the IGCC plant, a differential cost analysis is performed taking into account major plant capital and operating costs. This analysis considers the same amount of coal and power generation for cases with and without the MR. The results of this analysis based on a present value of annuity calculation show break even costs for the MR within the feasible range for membrane fabrication, even for short membrane lifetimes. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1568–1580, 2016

Fast Level-Set-Based Inverse Lithography Algorithm for Process Robustness Improvement and Its Application

Inverse lithography technology (ILT) is one of the promising resolution enhancement techniques (RETs), as the advanced integrated circuits (IC) technology nodes still use the 193 nm light source. Among all the algorithms for ILT, the level-set-based ILT (LSB-ILT) is a feasible choice with good production result in practice. However, existing ILT algorithms optimize masks at nominal process condition without giving sufficient attention to the process variations, and thus the optimized masks show poor performance with focus and dose variations. In this paper, we put forward a new LSB-ILT algorithm for process robustness improvement with fast convergence. In order to account for the process variations in the optimization, we adopt a new form of the cost function by adding the objective function of process variation band (PV band) to the nominal cost. We also adopt the hybrid conjugate gradient (CG) method to reduce the runtime of the algorithm. We perform experiments on ICCAD 2013 benchmarks and the results show that our algorithm outperforms the top two winners of the ICCAD 2013 contest by 6.5%. We also adopt the attenuated phase shift mask (att-PSM) in the experiment with test cases from industry. The results show that our new algorithm has a fast convergence speed and reduces the process manufacturability index (PMI) by 38.77% compared with the LSB-ILT algorithm without the consideration of PV band.

Efficient and Robust Synthesis of Phase-Shifting Masks in Optical Lithography

Phase-shifting masks (PSMs) are extensively used in semiconductor industry to push the limit of optical lithography. Their synthesis is generally performed via an optimization method under the nominal process condition, and the synthesis process is time-consuming. In this work, we apply a statistical strategy to optimize the average performance with respect to process fluctuations to enhance the robustness of the synthesized PSM pattern, and employ a cascadic multigrid algorithm to improve its computational efficiency. Moreover, we investigate and discuss the impact of the initial pattern on the synthesized result.

Impact of pressure losses in small-sized parabolic-trough collectors for direct steam generation

Using PTC (parabolic-trough solar collectors) for industrial thermal processes in the temperature range up to 300 °C is not new, but in recent years there is a boosted interest in this type of concentrating solar technology. One of the problems that arise when designing PTC solar fields is how to deal with the pressure losses which are critical when producing saturated steam directly in the collectors. Depending on the characteristics of the collector, mainly on the receiver diameter, and on the nominal process conditions defined, a solar field configuration can be feasible or not. This paper presents a sensitivity analysis done using a software tool developed to study the thermo-hydraulic behaviour of PTC systems using water-steam as heat transfer fluid. In the case study presented, a small-sized PTC designed for industrial process heat applications is considered, which has a focal length of 0.2 m, an aperture area of 2 m2, and its receiver pipe has an inner diameter of 15 mm. Varied process conditions are inlet water pressure, temperature, and mass flow rate, solar irradiance and incidence angle of solar radiation. Results show that working pressure definition is particularly critical to make feasible or not the direct steam generation in solar collectors.

Model based hybrid proximity effect correction scheme combining dose modulation and shape adjustments

The authors present a general approach to combine model-based dose modulations and shape modifications into a hybrid proximity effects correction (PEC) scheme for electron beam lithography. The authors simplify this scheme significantly by using an appropriate dose correction strategy. This allows us to use an existing optical proximity correction tool for the shape adjustments. This hybrid PEC scheme is demonstrated by computing corrections for simple test patterns as well as a more complex pattern. The model used corresponds to an electron multibeam tool with an acceleration voltage of 50 kV. It predicts resist contours from a written dose distribution. The authors evaluate the quality of the results both for nominal process conditions and in the presence of process variations. The results are compared against the corresponding results for a correction using only dose modulation. The authors also use the hybrid scheme to compensate intentional overexposure by shape adjustments and include these results ...

Process window modeling using focus balancing technique

In this work, we present a methodology for process window capable optical proximity correction (OPC) compact model building which requires only one nominal process condition empirical data for model calibration, and enables full and predictable extrapolation to any process condition within focus-exposure matrix. In order to ensure modeling success, a focus and dose balancing techniques are used during model calibration. The model optimization method is based on a stepwise fitting methodology where staged optimization of the OPC model components is used. Model components are added during the OPC model calibration starting with more physical components, such as mask and optics, followed by resist components. In each optimization stage, a component is optimized using global regression methods. The optimized parameters regressed in a small range about their optimal values during subsequent model component optimization. The effectiveness of this approach in terms of accurate correction, process window interpolation and extrapolation were compared with conventional fitting methods through computational experiments. Prediction of measured verification patterns were used to assess the calibrated model quality.

Calibration of physical resist models for simulation of extreme ultraviolet lithography

We have calibrated a physical resist model for extreme ultra-violet (EUV) lithography, and discuss model calibration and validation over a larger set of structures. The study is conducted on an extensive data set, collected at imec, for ShinEtsu resist SEVR-59 exposed on the ASML EUV alpha demo tool (ADT). The data set included more than a thousand measured feature widths (critical dimensions or CD) on wafer and mask. We address practical aspects of the calibration, such as the speed of calibration and selection of calibration input. The model is calibrated by simultaneously fitting 12 process windows of features with different mask CD (32, 36, 40 nm), orientation (horizontal, vertical), and pitch (dense, isolated). The smallest feature size at nominal process condition is a 32 nm CD at a dense pitch of 64 nm. Mask CD metrology was used to fit the model versus actually measured mask CD's. Cross-sectional scanning electron microscopy information was included in the calibration, to tune the simulated resist loss and sidewall angle. The achieved calibration root-mean-squared (RMS) error is ∼1.0 nm. We discuss the elements that were essential to obtain a well calibrated model. We discuss the impact of 3-D mask effects on the Bossung tilt. We demonstrate that a correct representation of the flare level during the calibration is key in order to achieve a high CD predictability at various flare levels. Although the model calibration is performed on a limited subset of the measurement data collected on 12 different patterns (one dimensional structure process windows), its accuracy is validated on a large number of patterns used to calibrate models for optical proximity correction―several hundred different feature types, at nominal dose and focus conditions. These were not included in the calibration; validation RMS results as small as 1 nm can be reached. Furthermore, we study the model's extendibility to two-dimensional end of line structures.

Machine learning for inverse lithography: using stochastic gradient descent for robustphotomask synthesis

Inverse lithography technology (ILT) synthesizes photomasks by solving an inverse imagingproblem through optimization of an appropriate functional. Much effort on ILT isdedicated to deriving superior masks at a nominal process condition. However, the lowerk1 factor causes the mask to be more sensitive to process variations. Robustness to majorprocess variations, such as focus and dose variations, is desired. In this paper, we considerthe focus variation as a stochastic variable, and treat the mask design as a machinelearning problem. The stochastic gradient descent approach, which is a useful tool inmachine learning, is adopted to train the mask design. Compared with previous work,simulation shows that the proposed algorithm is effective in producing robust masks.

True process variation aware optical proximity correction with variational lithography modeling and model calibration

Optical proximity correction (OPC) is one of the most widely used resolution enhancement techniques (RET) in nanometer designs to improve subwavelength printability. Conventional model-based OPC assumes nominal process conditions without considering process variations because of the lack of variational lithography models. A simple method to improve OPC results under process variations is to sample multiple process conditions across the process window, which requires long run times. We derive a variational lithography model (VLIM) that can simulate across the process window without much run-time overhead compared to the conventional lithography models. To match the model to experimental data, we demonstrate a VLIM calibration method. The calibrated model has accuracy comparable to nonvariational models, but has the advantage of taking process variations into consideration. We introduce the variational edge placement error (VEPE) metrics based on the model, a natural extension to the edge placement error (EPE) used in conventional OPC algorithms. A true process-variation aware OPC (PVOPC) framework is proposed used the VEPE metric. Due to the analytical nature of VLIM, our PVOPC is only about 2 to 3× slower than the conventional OPC, but it explicitly considers the two main sources of process variations (exposure dose and focus variations) during OPC. Thus our post-PVOPC results are much more robust than the conventional OPC ones, in terms of both geometric printability and electrical characterization under process variations.

Pass-transistor based implementations of threshold logic gates for WOS filtering

This paper presents a systematic procedure to implement threshold functions by using a pass-transistor network. A main feature of the threshold gates (TGs) produced by this technique is that they do not exhibit the fan-in limitations usual when other implementation techniques are used. Thus, they are especially useful for Weighted Order Statistical (WOS) filters because the binary filters required are threshold functions which usually present a high total sum of weights. A WOS filter with its binary filters implemented as pass-transistor TGs is demonstrated in an standard 0.35μm CMOS technology at 3.3V. The filter shows a sample frequency well over 100MHz at the nominal process condition and it is cheaper, faster and consumes less power than a conventional approach.

Application of the vacuum/steam/vacuum surface intervention process to reduce bacteria on the surface of fruits and vegetables

A vacuum/steam/vacuum (VSV) surface intervention process has previously been developed for poultry and hot dogs. The process uses a brief exposure to vacuum to remove surface air and water to expose bacteria. After a short treatment with saturated steam (0.1 s), a second vacuum treatment evaporatively cools the surface, resulting in the destruction of bacteria with little or no thermal damage. The VSV surface intervention process has also been applied to fruits and vegetables. Optimization methods were used with cantaloupes, grapefruits, and beets to determine process conditions for steam temperature, steam time, vacuum time, and number of cycles to destroy bacteria with the constraint of little or no thermal damage. Inoculated Listeria innocua was used for the cantaloupe and grapefruit studies and total aerobic plate count (APC) was used for the beet study. Bacteria destruction ranged from 2.5 log cfu/ml APC for beets to almost 4 log L. innocua for grapefruits. The process was successfully applied to other fruits and vegetables such as papayas, mangoes, avocados, kiwis, carrots, cucumbers, and peaches, using the nominal process conditions found with cantaloupes, grapefruits, and beets. Applying the process to bananas, cauliflower, broccoli, and peppers resulted in thermal or mechanical damage. The total process time was 0.5–1.2 s, depending on the number of cycles and the process time per cycle. Assuming that these results with APC and L. innocua are indicative of the treatment of naturally present pathogens, this surface intervention process should ensure that fruits and vegetables suitable for this process will reach the consumer having greatly reduced levels of bacterial contamination.

Design and implementation of differential cascode voltage switch with pass-gate (DCVSPG) logic for high-performance digital systems

In this paper, a new high-speed circuit technique called differential cascode voltage switch with pass-gate (DCVSPG) logic tree is presented. The circuit technique is designed using a pass-gate logic tree in DCVSPG instead of the nMOS logic tree in the conventional DCVS circuit, which eliminates the floating node problem. By eliminating the floating node problem, the DCVSPG becomes a new type of ratioless circuit, and it also provides superior performance with less power dissipation and better silicon area tradeoff. The basic DCVSPG design technique, the methodology for optimization, and synthesis of the pass-gate logic tree are described. The standard cell library development taking advantage of the dual-rail outputs of DCVSPG gates is also discussed. The performance comparisons with other existing pass-gate circuit techniques [complimentary pass-transistor logic (CPL), double pass-transistor logic (DPL), and swing restored pass-transistor logic (SRPL)] are presented. For more robust design, the DCVSPG with inverter buffers is also the best choice. A Viterbi macro design using the DCVSPG circuit technique is demonstrated. The process that the design is based upon is a 0.5-/spl mu/m CMOS technology with 0.25-/spl mu/m effective channel length and five layers of metal. This macro can run up to 500 MHz at the nominal process condition. In comparison with other existing dynamic circuit techniques, the results also clearly show that the dynamic DCVSPG has the superior power-delay performance.