Computational Lithography Techniques and Optimization Methods

Computational lithography employs numerical and algorithmic approaches to design photomasks and illumination sources that counteract diffraction, interference and process variability in semiconductor patterning. Core techniques include optical proximity correction, which adapts mask features at sub-wavelength scales to mitigate proximity effects; source-mask optimisation, which co-designs illumination and mask shapes to maximise resolution and process window; and inverse lithography technology, which formulates mask synthesis as an inverse imaging problem solved via iterative or learning-based solvers. Recent advances integrate compressive sensing to exploit pattern sparsity, level-set methods for robust curve evolution and machine-learning models to accelerate convergence. Evolutionary and swarm-intelligence algorithms further enhance global search efficiency for full-chip optimisation. These methods are critical for extending optical lithography beyond 7 nm nodes and for enabling extreme ultraviolet lithography, underpinning the production of next-generation microelectronics, photonic devices and advanced displays with high yield and reproducibility.

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Research from all publishers

Researchers have applied social-learning particle swarm optimisation to extreme ultraviolet lithography, coupling a rigorous thick-mask electromagnetic model with swarm intelligence. This hybrid framework achieved up to 95 % reduction in pattern error across various target layouts, demonstrating enhanced depth of focus and process robustness.

A covariance matrix adaptation evolution strategy has been developed for source-mask optimisation at sub-20 nm nodes, using adaptive search covariance updates and sparse point-source representations. This method accelerated full-chip convergence and yielded superior imaging performance compared with conventional gradient-based algorithms.

In inverse lithography, model-driven convolutional neural networks have been introduced to produce high-quality initial mask patterns by unfolding iterative solvers into trainable architectures. Combined with a subsequent gradient-based refinement, this approach improves computational efficiency by an order of magnitude while preserving critical imaging fidelity for dense layouts.

Computational Lithography Techniques and Optimization Methods publication trend

The graph below shows the total number of articles in computational lithography techniques and optimization methods across all publications each year (not limited to Nature Index journals).

Technical terms

Optical Proximity Correction (OPC): Adjustment of mask features, including sub-resolution assist features, to compensate for diffraction and interference in optical lithography and improve pattern fidelity.

Source Mask Optimisation (SMO): Joint optimisation of the illumination source and mask geometry to enhance resolution, process latitude and depth of focus in advanced lithography.

Inverse Lithography Technology (ILT): A computational framework treating mask design as an inverse imaging problem, solved via iterative optimisation or data-driven models to generate precise mask contours from target aerial images.

Compressive Sensing: A mathematical strategy that reconstructs signals or images from undersampled measurements by leveraging sparsity in a chosen basis, reducing computational cost in mask and source design.

Extreme Ultraviolet (EUV) Lithography: A next-generation lithography technique using 13.5 nm wavelength radiation, requiring advanced computational models to address mask topography, coherence and diffraction effects.

References

1. Fast optical proximity correction method based on nonlinear compressive sensing.. Optics Express (2018).
2. Lithographic source optimization based on adaptive projection compressive sensing.. Optics Express (2017).
3. Model-driven convolution neural network for inverse lithography.. Optics Express (2018).
4. Source mask optimization using the covariance matrix adaptation evolution strategy.. Optics Express (2020).
5. Robust level-set-based inverse lithography.. Optics Express (2011).
6. Source mask optimization for extreme-ultraviolet lithography based on thick mask model and social learning particle swarm optimization algorithm.. Optics Express (2021).