Gradient-based joint source polarization mask optimization for optical lithography

Ma, Xu; Dong, Lisong; Han, Chunying; Gao, Jie; Li, Yanqiu; Arce, Gonzalo R.

doi:10.1117/1.jmm.14.2.023504

Cited by 19 publications

(4 citation statements)

References 39 publications

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“…Hence, it allows for more design freedom than the sole mask or source optimization for PEC. This approach was first proposed by Rosenbluth et al [14] and then followed by research works on the pixelated gradient-based SMO algorithms [15][16][17][18][19][20][21][22][23]. A variety of computational workflows such as simultaneous, sequential, and hybrid SMO schemes has been proposed to solve the optimization problem [22].…”

Section: Optical Projection Lithography and Proximity Error Correctionmentioning

confidence: 99%

Topology optimization for optical microlithography with partially coherent illumination

Zhou

Lazarov

Sigmund

2016

Int. J. Numer. Meth. Engng

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SUMMARYThis article revisits a topology optimization design approach for micro-manufacturing and extends it to optical microlithography with partially coherent illumination. The solution is based on a combination of two technologies, the topology optimization and the proximity error correction in micro-/nano-lithography. The key steps include (i) modeling the physical inputs of the fabrication process, including the UV-light illumination source and the mask, as the design variables in optimization and (ii) applying physical filtering and heaviside projection for topology optimization, which correspond to the aerial image formulation and the pattern development processes, respectively. The proposed approach results in an effective source and a binary design mask, which can be sent directly to fabrication without additional post-processing steps for proximity error correction. Meanwhile, the performance of the device is optimized and robust w.r.t. process variations, such as dose / photo-resist variations and lens defocus. A compliant micro-gripper design example is considered to demonstrate the applicability of this approach. CCC Received . . .

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Section: Optical Projection Lithography and Proximity Error Correctionmentioning

confidence: 99%

Topology optimization for optical microlithography with partially coherent illumination

Zhou

Lazarov

Sigmund

2016

Int. J. Numer. Meth. Engng

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“…To enhance the performance of the pixelated SO methods, a set of algorithms have been proposed, including gradientbased [17], [18] and heuristic algorithms [19], [20]. In these methods, the pattern errors, as a generally utilized merit function in the iteration procedure, can be defined by calculating the cumulative sum of the difference between the resist pattern (RP) and the desired image with point-by-point.…”

Section: Introductionmentioning

confidence: 99%

Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

et al. 2023

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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 highefficiency 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.

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“…Traditional gradient-based methods of solving optimization problems, such as conjugate gradient (CG) [5,6], steepest descent (SD) [7], and gradient descent (GD) [8][9][10], have been used to solve the SMO problem. These methods are relatively efficient.…”

Section: Introductionmentioning

confidence: 99%

The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

et al. 2023

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Source mask optimization (SMO) is an effective method for improving the image quality of high-node lithography. Reasonable algorithm optimization is the critical issue in SMO. A GA-APSO hybrid algorithm, combining genetic algorithm (GA) and adaptive particle swarm optimization (APSO), was proposed to inversely obtain the global optimal distribution of the pixelated source and mask in the lithographic imaging process. The computational efficiency was improved by combining the GA and PSO algorithms. Additionally, the global search and local search were balanced through adaptive strategies, leading to a closer result to the global optimal solution. To verify the performance of GA-APSO, simple symmetric patterns and complex patterns were optimized and compared with GA and APSO, respectively. The results show that the pattern errors (PEs) of the resist image optimized by GA-APSO were reduced by 40.13–52.94% and 10.28–33.31% compared to GA and APSO, respectively. The time cost of GA-APSO was reduced by 75.91–87.00% and 48.43–58.66% compared to GA and APSO, respectively. Moreover, repeated calculation showed that the GA-APSO results were relatively stable. The results demonstrate the superior performance of GA-APSO in efficiency, accuracy, and repeatability for source and mask optimization.

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Gradient-based joint source polarization mask optimization for optical lithography

Cited by 19 publications

References 39 publications

Topology optimization for optical microlithography with partially coherent illumination

Topology optimization for optical microlithography with partially coherent illumination

Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

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