Optical Proximity Correction Research Articles

Genetic algorithm-based optical proximity correction for DMD maskless lithography.

We present an optical proximity correction (OPC) method based on a genetic algorithm for reducing the optical proximity effect-induced pattern distortion in digital micromirror device (DMD) maskless lithography. Via this algorithm-assisted grayscale modulation of the initial mask at the pixel level, the exposure pattern can be enhanced significantly. Actual exposure experiments revealed that the rate of matching between the final exposure pattern and the mask pattern can be increased by up to 20%. This method's applicability to complex masks further demonstrates its universality for mask pattern optimization. We believe that our algorithm-assisted OPC could be highly helpful for high-fidelity and efficient DMD maskless lithography for microfabrication.

Evaluation of convolutional neural network for fast extreme ultraviolet lithography simulation using imec 3 nm node mask patterns

BackgroundMask 3D (M3D) effects distort diffraction amplitudes from extreme ultraviolet masks. In our previous work, we developed a convolutional neural network (CNN) that very quickly predicted the distorted diffraction amplitudes from input mask patterns. The mask patterns were restricted to Manhattan patterns.AimWe verify the potentials and the limitations of CNN using imec 3 nm node (iN3) mask patterns.ApproachWe apply the same CNN architecture in the previous work to mask patterns, which mimic iN3 logic metal or via layers. In addition, to study more general mask patterns, we apply the architecture to iN3 metal/via patterns with optical proximity correction (OPC) and curvilinear via patterns. In total, we train five different CNNs: metal patterns w/wo OPC, via patterns w/wo OPC, and curvilinear via patterns. After the training, we validate each CNN using validation data with the above five different characteristics.ResultsWhen we use the training and validation data with the same characteristics, the validation loss becomes very small. Our CNN architecture is flexible enough to be applied to iN3 metal and via layers. The architecture has the capability to recognize curvilinear mask patterns. On the other hand, using the training and validation data with different characteristics will lead to large validation loss. The selection of training data is very important for obtaining high accuracy. We examine the impact of M3D effects on iN3 metal layers. A large difference is observed in the tip to tip (T2T) critical dimension calculated by the thin mask model and thick mask model. This is due to the mask shadowing effect at T2T slits.ConclusionsThe selection of training data is very important for obtaining high accuracy. Our test results suggest that layer specific CNN could be constructed, but further development of CNN architecture could be required.

Implicit function characterization of the curvilinear mask to realize parametric optical proximity correction with a neighborhood parallel tabu search.

The curvilinear mask structures provide significant benefits in improving lithographic resolution. Curvilinear masks, as opposed to rectilinear masks, have a wider range of structure types that can be used precisely to correct the contour of diffraction at sharp technological nodes. However, the curvilinear structure also makes the inverse design of mask in optical proximity correction (OPC) flow difficult. The current OPC of curvilinear masks uses pixel-based inverse optimization, which is extremely computationally intensive and takes up a lot of design data storage space. This paper proposes an implicit function to represent a large number of curve types with a small number of parameters to reduce computational complexity and the R&D cycle. Therefore, the ultra-high dimensional pixel-based OPC problem is transformed into a low-dimensional parameter search problem in the critical diffraction area of the mask pattern. The tabu search algorithm and neighborhood parallel computing strategy are then used to quickly search for optimal characterized parameters. The results of the simulation show that the parametric curvilinear OPC method achieves a higher image fidelity than that of rectilinear OPC. At the same time, it addresses the shortcomings of the traditional pixelated curvilinear mask OPC method, including high computational complexity, low manufacturability, and storage space occupancy.

Designing diffractive optical elements for shaping partially coherent beams by proximity correction.

We propose a new method to design diffractive optical elements (DOE) for shaping partially coherent beams. The diffraction patterns of a DOE under a certain partially coherent beam can be modeled by the convolution of the coherent diffraction pattern and the inherent degree of coherent function. Two basic types of diffraction anomalies induced by partially coherent beams are discussed, including line-end shortening and corner rounding. A proximity correction (PC) method similar to the optical proximity correction (OPC) technique in lithography is used to compensate for these anomalies. The designed DOE exhibits good performance in partially coherent beam shaping and noise suppression.

P‐7.5: Analysis and research on the improvement of OPC compensation aperture ratio

In this paper, we aim to improve the opening rate of color film substrate by proximity exposure mechanism, and carry out OPC (Optical Proximity Correction) at the corners of the pattern on the photo mask version, and DOE (design of experiments) to verify the optimal solution under the existing equipment and process conditions. The final result is that the color film black matrix pattern is closer to the design, and the substrate opening rate is increased by 1‐4%, which increases the product transmission rate and enhances the competitiveness of the product.

Layout pattern analysis and coverage evaluation in computational lithography.

In advanced semiconductor technology nodes, the model accuracy of optical proximity correction (OPC) is the key for integrated circuit (IC) chip mask tape out, yield ramp up, and product time-to-market. An accurate model means a small prediction error for the full chip layout. As the full chip layout usually has large pattern variety, an optimal pattern set with good coverage is desired during the model calibration process. Currently, no existing solutions can provide the effective metrics to evaluate the coverage sufficiency of the selected pattern set before a real mask tape out, which may potentially cause higher re-tape out cost and product time-to-market delay due to the multiple rounds of model calibration. In this paper, we construct the metrics to evaluate the pattern coverage before any metrology data is obtained. The metrics are based on either the pattern's intrinsic, numerical feature representation, or its potential model simulation behavior. Experimental results show a positive correlation between these metrics and lithographic model accuracy. An incremental selection method is also proposed based on the pattern simulation error. It reduces up to 53% of the model's verification error range. These pattern coverage evaluation methods can improve the efficiency of OPC model building, and are, in turn, beneficial to the whole OPC recipe development process.

Generation of inverse assist features using generative adversarial networks

Adding the assist feature in the optical proximity correction (OPC) procedure is crucial for improving the process window. The rule-based assist feature placement is very fast; however, the resultant process window still requires improvement. The model-based assist feature placement presents a better process window, but its iterative nature makes it extremely time-consuming. The assist features (AFs) that are optimized by using the inverse lithography technique perform best in terms of lithographic metrics such as the Depth of Focus, Mask Error Enhancement Factor, and Normalized Image Log Slope; these features can be added to the objective function. However, the inverse lithography technique is a pixel-level correction method, which is even more time-consuming. Consequently, it is typically used to provide guidance for rule-based AF placement. In this study, we design novel generative adversarial networks (GANs) that can generate AFs to increase the speed of assist feature placement while maintaining relatively high lithographic performance using inverse lithography techniques. The results demonstrate that the proposed generative adversarial network methods are three orders of magnitude faster than the inverse lithography techniques. Additionally, they exhibit lower edge placement errors and process variation bands when compared to the inverse lithography techniques.

Optical Proximity Correction Using Machine Learning Assisted Human Decision

Optical proximity correction (OPC) is a critical step in semiconductor manufacturing due to its high complexity and significant influence on the subsequent process steps. Conventional OPC using the Maxwell equation can become more and more challenging as a fully vectorized three-dimensional simulation is required for advanced technology nodes. Machine learning (ML) has been a promising alternative recently. This work proposes machine-learning-assisted human decision, which can be more in line with the clean room engineer's practice and can potentially surpass pure human decision and the pure machine learning approach. Using 10-step optimization in the photolithographic mask, the averaged mean square error (MSE) at the optimized cases are 6360 and 2101 for two-bar patterns and 7132 and 5931 for tri-line attackers when comparing pure human decision and ML-assisted human. The average MSEs at the first 3 steps are 26019 and 6023 for the two-bar pattern and 79979 and 7738 for the tri-line attacker when comparing pure ML and ML-assisted human. It is suggested that the strength of the ML-assisted human decision lies in the early-stage superiority over pure ML, flexibility, incorporating past experience, and a human sense that cannot be formulated concretely by statistical models.

A GPU-Enabled Level-Set Method for Mask Optimization

As the feature size of advanced integrated circuits keeps shrinking, resolution enhancement techniques (RETs) are utilized to improve the printability in the lithography process. Optical proximity correction (OPC) is one of the most widely used RETs aiming at compensating the mask to generate a more precise wafer image. In this article, we put forward a level-set-based OPC approach with high mask optimization quality and fast convergence. In order to suppress the disturbance of the condition fluctuation in the lithography process, we propose a new process window-aware cost function. Then, a novel momentum-based evolution technique is adopted, which demonstrates substantial improvement. We also propose a self-adaptive conjugate gradient method that promises a higher optimization stability and less consuming time. Moreover, the graphics processing unit (GPU) is leveraged for accelerating the proposed algorithm. We take the output masks from a machine learning-based mask optimization flow as the input and work as the postprocess to refine the quasi-optimized masks. Experimental results on ICCAD 2013 benchmarks show that our algorithm outperforms all previous OPC algorithms in both solution quality and runtime overhead.

Curvilinear mask optimization with refined generative adversarial nets

Inverse lithography technology (ILT) can optimize the mask to gain the best process window and image quality when the design dimension shrinks. However, as a pixel level correction method, ILT is very time-consuming. In order to make the ILT method useful in real mask fabrication, the runtime of ILT-based optical proximity correction mask must evidently decrease while keeping the good lithographic metric performance. Our study proposes a framework to obtain the curvilinear ILT mask with generative adversarial network (GAN). It is subsequently refined with the traditional ILT to exclude unexpected outliers generated by the GAN method. We design conditional GAN, reverse GAN (RGAN), and high discretion GAN (HDGAN) to generate curvilinear ILT mask. Their runtime and the performance are compared. Compared with the CILT method, the speed of GAN type methods with the afterward refinement is increased by an order of magnitude. The RGAN has a better performance in edge placement error and process variation band evaluation, and HDGAN has a better performance in the mask error enhancement factor evaluation. The designed RGAN and HDGAN are promising in actual application to generate the curvilinear mask. They can evidently decrease the runtime and have better lithographic metric performance.

Improvement of YX42° Cut LiTaO3 SAW Filters with Optical Proximity Effect Correction Method.

Due to the influence of the optical proximity effect (OPE), it is easy for a pattern of photoresistance to be inconsistent with a design pattern, thus damaging the performance of a SAW resonator. To solve this problem, this paper proposes an optimization method for SAW filters based on optical proximity correction (OPC). This method can avoid the tip discharge problem of SAW filters by suppressing the problem of rounding and shrinking of dummy electrode and electrode tail caused by OPE. This method increases the quality factor (Q) of the SAW resonator and thus decreases the insertion loss of the SAW filter. The filter increases the bandwidth by 1.8 MHz at -1.5 dB after applying the OPC method. Additionally, it improves the stability of the filter under high power conditions.

3D OPC method for controlling the morphology of micro structures in laser direct writing.

A 3D optical proximity correction (OPC) method for controlling the morphology of micro-structures in laser direct writing is proposed, considering both the optical proximity effect and nonlinear response of a thick-film photoresist. This method can improve the manufacturability and optical performance of devices, and can be used for most 3D micro\nano structures. Its application in the fabrication of a quadratic curvature microlens array shows that the shape of the lens is well controlled; that is, when the height of the lens is 5.25 µm, the average height error of the lens shape is less than 5.22%.

A Novel Optical Proximity Correction (OPC) System Based on Deep Learning Method for the Extreme Ultraviolet (EUV) Lithography

A Novel Optical Proximity Correction (OPC) System Based on Deep Learning Method for the Extreme Ultraviolet (EUV) Lithography

Can optical proximity correction solution be learned? The learning limit and a general learning framework

BackgroundOptical proximity correction (OPC) is an indispensable technology that has been propelling the advancement of computational lithography technology. To tightly control edge placement error (EPE) and maintain lithography process window, the demands on OPC computational resources and OPC turnaround time are growing rapidly with alarming acceleration. To tame the trend, machine learning technologies have been explored; however, an in-depth discussion on OPC solution learning limit is still lacking.AimWe aim to present an in-depth discussion on OPC solution learning limit and propose a general machine learning OPC framework that can be extended to curvilinear mask OPC technology.ApproachIn this study, we first investigate the machine learning OPC learning limit by examining noninverse lithography technology (non-ILT) OPC solution space characteristics inherited from edge segmentation and control point setting rules and then propose a general machine learning OPC framework that can take full advantage of deep convolution neural network (DCNN) learning capability while being able to preserve mask data high resolution.ResultsWith this machine learning OPC framework, we have achieved models with average absolute model error <1 nm for 14-nm metal layer. With single GPU, the average time for machine learning OPC models to produce results of 3840 nm × 3840 nm area is 8.74 ms for single channel input model and 12.65 ms for six channels input model.ConclusionsFor non-ILT OPC solution, there is an intrinsic learning limit inherited from edge segmentation rules. Machine learning OPC models should be content with learning low order OPC solutions. This intrinsic learning limit of non-ILT OPC solution may diminish for ILT OPC solution when the constraint on degrees of freedom of OPC solution is lifted. The machine learning OPC framework we proposed is general and extendable to curvilinear OPC technology.

Machine learning optical proximity correction with generative adversarial networks

BackgroundAlgorithmic breakthroughs in machine learning (ML) have allowed increasingly more applications developed for computational lithography, gradually shifting focus from hotspot detection to inverse lithography and optical proximity correction (OPC). We proposed a pixelated mask synthesis method utilizing deep-learning techniques, to generate after-development-inspection (ADI) contour and mask feature generation.AimConventional OPC correction consists of two parts, the simulation model which predicts the expected contour signal, and the correction script that modifies the actual layout. With practicality in mind, we collected modeling wafer data from scratch, then implemented ML models to reproduce conventional OPC actions, mask to contour prediction, and design to mask correction.ApproachTwo generative adversarial networks (GANs) were constructed, a pix2pix model was first trained to learn the correspondences between mask image and paired ADI contour image collected on wafer. The second model is embedded into machine learning mask correction (ML-OPC) framework, output mask is optimized through minimizing pixel difference between design target and simulated contour.ResultsTwo different magnification SEM image datasets were collected and studied, with the higher magnification showing better simulator pixel accuracy. Supervised training of the correction model provided a quick prototype mask synthesis generator, and combination of unsupervised training allowed mask pattern synthetization from any given design layout.ConclusionsThe experimental results demonstrated that our ML-OPC framework was able to mimic conventional OPC model in producing exquisite mask patterns and contours. This ML-OPC framework could be implemented across full chip layout.

Predictive compact model for stress-induced on-product overlay correction

On-product overlay (OPO) is an important indicator of device yield. In this work, we show that stressed thin films used in semiconductor manufacturing can be an important contributor to OPO at multiple length scales. Depending on the stress level, film thickness, and the mask design, the overlay impact can be a few nanometers for the exposure of the next lithography layer. A predictive compact model based on pattern density is developed to accurately predict this overlay impact. The model is then verified using short-loop dual damascene wafers with stress split. The predictive model opens a new opportunity for model-based mask correction during optical proximity correction to increase the overlay margin for subsequent lithography exposures.

P‐3.25: Synergistic effects between Optical Proximity Correction (OPC) and lithography process simulation in display backplane

In this paper, lithographic simulation tools, including Optical Proximity Correction (OPC), have been applied in designing backplane layout of displays. That will lead to better resolution of lower feature size of the lithography process in display manufacturing with wider process window.

DAMO: Deep Agile Mask Optimization for Full-Chip Scale

Continuous scaling of the very-large-scale integration system leaves a significant challenge on manufacturing; thus optical proximity correction (OPC) is widely applied in conventional design flow for manufacturability optimization. Traditional techniques conduct OPC by leveraging a lithography model but may suffer from prohibitive computational overhead. In addition, most of them focus on optimizing a single and local clip instead of addressing how to tackle the full-chip scale. In this article, we present DAMO, a high-performance and scalable deep-learning-enabled OPC system for full-chip scale. It is an end-to-end mask optimization paradigm that contains a deep lithography simulator (DLS) for lithography modeling and a deep mask generator (DMG) for mask pattern generation. Moreover, a novel layout splitting algorithm customized for DAMO is proposed, composed of DBSCAN clustering and KMeans++ clustering, to handle the full-chip OPC problem. Further, graph-based computation and parallelism techniques are proposed to deploy our GPU algorithms to accelerate computations. Extensive experiments show that DAMO outperforms state-of-the-art OPC solutions in both academia and industrial commercial toolkit.

Neural-ILT 2.0: Migrating ILT to Domain-Specific and Multitask-Enabled Neural Network

Optical proximity correction (OPC) in modern design closures has become extremely expensive and challenging. Conventional model-based OPC encounters performance degradation and large process variation, while aggressive approach, such as inverse lithography technology (ILT), suffers from large computational overhead for both mask optimization and mask writing processes. In this article, we developed Neural-ILT, an end-to-end learning-based OPC framework, which literally conducts mask prediction and ILT correction for a given layout in a single neural network, with the objectives of: 1) mask printability enhancement; 2) mask complexity optimization; and 3) flow acceleration. A domain-specific model pretraining recipe, which introduces the domain knowledge of lithography system, is proposed to help Neural-ILT achieving faster and better convergence. Quantitative results show that compared to the state-of-the-art (SOTA) learning-based OPC solutions and conventional OPC flows, Neural-ILT can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\times $ </tex-math></inline-formula> turnaround time (TAT) speedup and the best mask printability with relatively lower mask complexity. Based on the developed infrastructure, we further investigated the feasibility of handling multiple mask optimization tasks for different datasets within a common Neural-ILT platform. We believe this work could bridge well-developed deep learning toolkits to GPU-based high-performance lithographic computations to achieve groundbreaking performance boosting on various computational lithography-related tasks.

I-line photolithographic metalenses enabled by distributed optical proximity correction with a deep-learning model.

High pattern fidelity is paramount to the performance of metalenses and metasurfaces, but is difficult to achieve using economic photolithography technologies due to low resolutions and limited process windows of diverse subwavelength structures. These hurdles can be overcome by photomask sizing or reshaping, also known as optical proximity correction (OPC). However, the lithographic simulators critical to model-based OPC require precise calibration and have not yet been specifically developed for metasurface patterning. Here, we demonstrate an accurate lithographic model based on Hopkin's image formulation and fully convolutional networks (FCN) to control the critical dimension (CD) patterning of a near-infrared (NIR) metalens through a distributed OPC flow using i-line photolithography. The lithographic model achieves an average ΔCD/CD = 1.69% due to process variations. The model-based OPC successfully produces the 260 nm CD in a metalens layout, which corresponds to a lithographic constant k1 of 0.46 and is primarily limited by the resolution of the photoresist. Consequently, our fabricated NIR metalens with a diameter of 1.5 mm and numerical aperture (NA) of 0.45 achieves a measured focusing efficiency of 64%, which is close to the calculated value of 69% and among the highest reported values using i-line photolithography.