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Inverse lithography technologies (ILTs) are critical for improving the imaging performance of lithography in advanced technology nodes. Pixel-based source optimization (SO), as an efficient part of ILTs, can be implemented via heuristic approaches to achieve high-performance lithographic imaging. In this paper, a SO approach based on a combination of the particle-swarm optimization and genetic algorithms (PSO–GA) is proposed to determine the optimal intensity distribution of the source via iterations. The pixelated source can be decoded into the optimized variables of the merit functions in the SO model. The proposed PSO–GA algorithm, as a high-efficiency hybrid algorithm, can transform the discrete SO problem into the optimal search solution for the merit function, thereby inversely enhancing the lithographic-imaging performance. In the forward-imaging model in the lithography, the extraction of the mask's effective diffraction spectrum is implemented to calculate the layout of resist patterns. The simulation results highlight the superior performance of the proposed approach in achieving pixelated SO over the traditional GA and PSO algorithm in terms of convergence capacity.

As an effective resolution enhancement technology, source optimization (SO) is considered key for significantly improving the image quality of optical lithography at advanced nodes. To solve the problem of unsatisfactory SO performance, it is necessary to combine it with optimization algorithms. In this study, an SO method based on a hybrid genetic algorithm is proposed to achieve an acceptable source shape in the imaging process for optical lithography. To overcome the problems of local optima and the small search scope, an update strategy that uses particle swarm optimization and the tabu list method from the tabu search algorithm are utilized to enhance the optimization performance. Meanwhile, different feature patterns were employed as the input of the optimization model. These simulation results show that the proposed SO method exhibits dominant optimization performance for SO in optical lithography. [ABSTRACT FROM AUTHOR]

As an effective resolution enhancement technology, source optimization (SO) is considered key for significantly improving the image quality of optical lithography at advanced nodes. To solve the problem of unsatisfactory SO performance, it is necessary to combine it with optimization algorithms. In this study, an SO method based on a hybrid genetic algorithm is proposed to achieve an acceptable source shape in the imaging process for optical lithography. To overcome the problems of local optima and the small search scope, an update strategy that uses particle swarm optimization and the tabu list method from the tabu search algorithm are utilized to enhance the optimization performance. Meanwhile, different feature patterns were employed as the input of the optimization model. These simulation results show that the proposed SO method exhibits dominant optimization performance for SO in optical lithography.

Summary: A Monte Carlo source model using PENELOPE was developed to investigate different tritiated metals in order to design a better radioisotope source for betavoltaic batteries. The source model takes into account the self‐absorption of beta particles in the source which is a major factor for an efficient source design. The average beta energy, beta flux, source power output, and source efficiency were estimated for various source thicknesses. The simulated results for titanium tritide with 0° and 90° angular distributions of beta particles were validated with experimental results. The importance of the backscattering effect due to isotropic particle emission was analyzed. The results showed that the normalized average beta energy increases with the source thickness, and it reaches peak energy depending on the density and the specific activity of the source. The beta flux and power output also increase with increasing source thickness. However, the incremental increase in beta flux and power output becomes minimal for higher thicknesses, as the source efficiency decreases significantly at higher thicknesses due to the self‐absorption effect. Thus, a saturation threshold is reached. A low‐density source material such as beryllium tritide provided a higher power output with higher efficiency. A maximum power output of approximately 4 mW/cm3 was obtained for beryllium tritide with SiC. A form factor approach was used to estimate the optimum source thickness. The optimum source thickness was found near the thickness where the peak beta particle average energy occurs. [ABSTRACT FROM AUTHOR]

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We shed light on the optimization of lithographic metrics for the semi-isolated dark field two-bar logic building block. Under standard D90Y illumination, this building block suffers from large mask three-dimensionalinduced relative focus-dependent critical dimension (CD) asymmetries. Such behavior limits its overlapping process window (oPW) and gives rise to untenable full wafer CD uniformity and intrafield pattern shifts. We found that besides a Ta absorber thickness reduction and illumination, pupil optimization is necessary to fully remove these CD asymmetries. The pupil optimization is achieved by relating the aerial image decomposition (here, symmetrization and balancing of intensities across the diffracted orders) with lithographic metrics for each pupil plane location. The resulting pupil allows us (i) to lift the focus-dependent CD asymmetries and (ii) to co-optimize a number of lithographic metrics, such as oPW, contrast, nontelecentricity, and pattern shift. The importance of subsidiary conditions (e.g., symmetry of the pupil, required depth-of-focus) will be discussed. [ABSTRACT FROM AUTHOR]

Source optimization is one of the key techniques for achieving higher resolution without increasing the complexity of mask design. An efficient source optimization approach is proposed on the basis of particle swarm optimization. The pixelated sources are encoded into particles, which are evaluated by using the pattern error as the fitness function. Afterward, the optimization is implemented by updating the velocities and positions of these particles. This approach is demonstrated using three mask patterns, including a periodic array of contact holes, a vertical line/space design, and a complicated pattern. The pattern errors are reduced by 69.6%, 51.5%, and 40.3%, respectively. Compared with the source optimization approach via genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. Compared with the source optimization approach via gradient descent method, the proposed approach does not need the calculation of gradients, and it has a strong adaptation to various lithographic models, fitness functions, and resist models. The robustness of the proposed approach to initial sources is also verified. [ABSTRACT FROM AUTHOR]

Background: In extreme ultraviolet lithography, maximum printable feature density is limited by stochastic defectivity. One of the methods to reduce it is the optimization of the aerial image via source mask optimization. To guide this optimization, we need to know which aerial image metric predicts defectivity. Feature variability is linked to defectivity. Aim: Find which aerial image metric best predicts variability and defectivity. Approach: We construct seven pupils that vary aerial image metrics [normalized image log slope (NILS), depth-of-focus (DoF), maximum intensity] in a controlled way. We measure variability and defectivity after development and after two different etch processes and correlate them to aerial image metrics. Results: Stochastic critical dimension uniformity (CDU) is best predicted by NILS. Systematic CDU is determined by mask roughness through spatial frequency, with low frequencies differentiating outer and inner sigma pupils. Defectivity is best predicted by maximum intensity for missing holes and minimum intensity for merging holes. NILS and maximum intensity are strongly correlated. DoF has minimal impact. Conclusions: Optimization toward maximum intensity reduces defectivity. Outer sigma pupils reduce variability. Increasing numerical aperture in next-generation EUV lithography is expected to reduce missing hole defects. [ABSTRACT FROM AUTHOR]

An optimized source has the ability to improve the process window during lithography in semiconductor manufacturing. Source optimization is always a key technique to improve printing performance. Conventionally, source optimization relies on mathematical–physical model calibration, which is computationally expensive and extremely time-consuming. Machine learning could learn from existing data, construct a prediction model, and speed up the whole process. We propose the first source optimization process based on autoencoder neural networks. The goal of this autoencoder-based process is to increase the speed of the source optimization process with high-quality imaging results. We also make additional technical efforts to improve the performance of our work, including data augmentation and batch normalization. Experimental results demonstrate that our autoencoder-based source optimization achieves about 105 × speed up with 4.67% compromise on depth of focus (DOF), when compared to conventional model-based source optimization method. [ABSTRACT FROM AUTHOR]