Optical Proximity Correction Research Articles

Efficient curvilinear optical proximity correction using non-uniform B-spline curves

Efficient curvilinear optical proximity correction using non-uniform B-spline curves

Feature Vector Effectiveness Evaluation for Pattern Selection in Computational Lithography

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

A general and accurate pattern search method for various scenarios

As chip designs grow in complexity, the optical proximity correction (OPC) process becomes increasingly time-consuming. As a result, pattern search technology is becoming a foundation stone of many tasks for manufacturing, such as lithography simulation, hotspot detection, mask optimization, and so on. The most difficult challenge of pattern search is to locate clips by specific patterns within the layout accurately and efficiently. In this paper, we present a generalized pattern search method capable in diverse scenarios, including patterns with hollow shapes, shifting edges, and multi-layer situations. Experimental results show that our method outperforms commercial tools in pattern locating accuracy and handling search problems involving complex patterns which is not directly support by commercial tools yet.

Efficient measurement and optical proximity correction modeling to catch lithography pattern shift issues of arbitrarily distributed hole layer

Efficient measurement and optical proximity correction modeling to catch lithography pattern shift issues of arbitrarily distributed hole layer

Fast and high-fidelity EUV curvilinear mask optimization by distance-versus-angle signature.

Curvilinear mask has received much attention in recent years due to its ability to obtain better image quality in advanced nodes. A common method for optimizing curvilinear mask in optical proximity correction (OPC) flow is moving control points on the edge directly (MCED-based OPC), but it requires storing mass data. This paper uses distance-versus-angle signature (DVAS), a one-dimensional function, to represent a two-dimensional boundary of mask. To the best of our knowledge, DVAS and its second derivative are applied for the first time to reduce the design data storage space and guide the direction of optimization in EUV lithography systems. Compared with storing the change distances of the control point in both horizontal and vertical directions, directly storing the amplitude change of the DVAS can effectively reduce the storage space consumption. Simulation results demonstrate that unlike the MCED-based OPC method, the DVAS-based OPC method using second derivative achieves a lower pattern error and requires less time while reducing the storage space.

The study of lithographic variation in resistive random access memory

Reducing the process variation is a significant concern for resistive random access memory (RRAM). Due to its ultra-high integration density, RRAM arrays are prone to lithographic variation during the lithography process, introducing electrical variation among different RRAM devices. In this work, an optical physical verification methodology for the RRAM array is developed, and the effects of different layout parameters on important electrical characteristics are systematically investigated. The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments. The read resistance is more sensitive to the locations in the array (~30%) than SET/RESET voltage (<10%). The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%, whereas it reduces RRAM read resistance by 4×, resulting in a higher power and area consumption. As such, we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.

Method for optical proximity correction based ona vector imaging model.

Optical proximity correction (OPC) has become an indispensable step in integrated circuit manufacturing. It requires a huge amount of calculation to obtain a sufficiently accurate OPC model and implement pattern correction. In this paper, the authors proposed an edge-based OPC method built on a vector imaging model, where the analytical correlation between the cost function and movement of each edge segment is established by the chain rule. First, the mask pattern is segmented and downsampled to get the mask image in order to reduce the total data. Second, the aerial image, various parameters on each evaluating point, and the final cost value are obtained in proper sequence. In each part of the OPC process, the procedures of solution and derivation are both recorded. After obtaining the cost value, the chain rule is applied, by which the differential relation between the cost value and movement of each segment is built. According to this differential relation, the next movement of each segment is decided under a quasi-Newton method. All results obtained by the proposed method are compared with results from commercial software. The comparison shows that the proposed OPC method has good OPC accuracy in few iterations.

Optical proximity correction of hot-spot patterns with subwavelength size in DMD maskless projection lithography.

When the critical dimension (CD) of resist patterns nears the resolution limit of the digital micromirror device (DMD) maskless projection lithography (DMD-MPL), significant distortion can emerge in the silicon wafer due to the optical proximity effect (OPE). The significant distortion (breakpoints, line-end scaling, corner rounding, etc.) between resist patterns and target patterns results in reduced lithographic quality. To address this issue, we have proposed a pixel-based optical proximity correction (PB-OPC) method used for the hot-spot patterns with subwavelength sizes specifically designed for DMD-MPL. Employing an end-to-end learning neural network, the PB-OPC algorithm is both straightforward and efficient. A well-trained U-net framework facilitates the mapping from unoptimized masks to optimized masks. Experimental exposure trials have demonstrated that this method not only corrects OPC in general patterns but also effectively rectifies hot-spot patterns. The pattern error (PE) value can be reduced by about 30% in the design layouts. We believe this approach holds the potential to enhance the resolution and fidelity of resist patterns in DMD maskless lithography.

Physical model-based ArF photoresist formulation development

Since the logic 28 nm technology and beyond, ArF immersion lithography has been widely used in manufacturing. To fully utilize the potential of the lithographic resolution, process simulation has been used since the lithography process setup step. The accuracy of the model prediction can be a key factor in the process stability, defectivity, and yield of the final product. To get better model prediction, it is very important to develop a process model with as many parameters as possible with physical meanings. Since the analysis of exposure data with a physical model can provide insights into the process and the photoresist material, it may greatly accelerate the photoresist formulation development or improvement process. From another aspect, as the function of the photoresist is to record the aerial image information on wafer, theoretically, the photoresist image should not deviate much from the aerial image or it will inevitably reduce the information content of the aerial image, i.e., through various kinds of averaging, mixing, etc. Since the emergence of Optical Proximity Correction (OPC), the time that takes to finalize a photolithography process has been significantly lengthened and made more complicated. To make the OPC model more reliable and less dependent on patches, a more physical OPC model is necessary. Therefore, it is clear that if we can develop a photoresist formulation to realize a more physical photoresist image, or a photoresist image closer to the aerial image, we can greatly improve the photolithography process performance and the time consumed for the process setup including OPC. In this paper, we focus on several physical model parameters for photoresist, especially the effective photoacid diffusion length and photo decomposable base parameters. We will show that these two parameters can provide a good description of photoresist imaging behavior, and the resist formulation can be improved to match the physical model prediction with more accuracy.

Microsphere photolithography with dynamic angular spectra control for metasurface fabrication.

Microsphere photolithography (MPL) is a promising technique for cost-effective fabrication of large-scale metasurfaces. This approach generates an array of photonic jets by the collimated illumination of self-assembled microspheres. The photonic jets can be precisely steered within the unit cell defined by each microsphere by changing the angle of incidence. This allows for the creation of complex metasurface element geometries. Computer controlled articulation of the substrate relative to a static UV source allows the direct-write of different metasurface elements. However, this is time-consuming and requires registration between each exposure for complex features. This paper investigates a single exposure method with the dynamic continuous angle of incidence control provided by a Digital Micromirror Device (DMD) in the front Fourier plane of the projection system. The grayscale values of the DMD pixels can be adjusted to provide optical proximity correction. Larger patterns can be achieved by scanning the substrate relative to the exposure beam. This approach is demonstrated with the creation of hierarchical patterns. This work greatly simplifies the MPL exposure process for complex resonators and provides potential for full light field control.

A self-training end-to-end mask optimization framework based on semantic segmentation network

As the process advances and the minimum linewidth gets closer to the physical limit, inverse lithography technique (ILT) is widely used for optical proximity correction (OPC). However, the computational overhead of the ILT method is high, and the printability of the mask is poor. In response to these limitations, we proposed ERFNet-ILT, a self-training method for an end-to-end learning framework for generating optimized masks directly from layout patterns, which introduces a feature fusion module at the end of the encoder and uses dilated convolution to expand the receptive field, thereby extracting layout pattern information such as edges, vertices and corners of the layout pattern from the feature map. The framework has shorter model building time and higher mask printability. Compared with the state-of-the-art methods, experimental quantitative results show that the proposed framework achieves 2.5 % squared L2 error and 7.9 % process variation band reduction within a comparable mask correction time.

A Novel Optical Proximity Correction Machine Learning Model Using a Single-Flow Convolutional Feedback Networks With Customized Attention

A Novel Optical Proximity Correction Machine Learning Model Using a Single-Flow Convolutional Feedback Networks With Customized Attention

Evolution of Maskless Digital Lithography: Game changer for Advanced Semiconductor Packaging

Lithography is a fundamental process in semiconductor chip manufacturing, patterning the chip design onto the photoresist coated wafer substrate before it is further processed for creating the device architectures. At one end, cutting EUV lithography is shaping the 3-5nm devices with masks and advanced OPC (optical proximity correction algorithms), and at the other end, newer maskless lithography technologies are shaping patterning at 2micron (predicted to touch 500nm) for other emerging applications in micro-electronics with new commercial propositions and actualization TAT. This paper gives a comparative overview of these patterning technologies and their potent applications in the semiconductor industry.

An Improved 3D OPC Method for the Fabrication of High-Fidelity Micro Fresnel Lenses.

Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, the micron-to-millimeter-scale structures represented by the Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photochemical reaction curves is developed in this paper, and a subdomain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results show the superiority of the proposed 3D OPC method in the fabrication of Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1%, and the profile error of the fabricated Fresnel lens is reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image is increased by 18.92%, and the average contrast of the image is enhanced by 36%. We believe that the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.

DevelSet: Deep Neural Level Set for Instant Mask Optimization

As one of the key techniques for resolution enhancement technologies (RETs), optical proximity correction (OPC) suffers from prohibitive computational costs as feature sizes continue to shrink. Inverse lithography techniques (ILT) treat the mask optimization process as an inverse imaging problem, yielding high-quality curvilinear masks. However, ILT methods often fall short of printability and manufacturability due to their time-consuming procedures and excessive computational overhead. In this paper, we propose DevelSet, a potent metal layer OPC engine that replaces discrete pixel-based masks with implicit level set-based representations. With a GPU-accelerated lithography simulator, DevelSet achieves end-to-end mask optimization using a neural network to provide quasi-optimized level set initialization and further evolution with a CUDA-based mask optimizer for fast convergence. The backbone of DevelSet-Net is a transformer-based multi-branch neural network that offers a parameter selector to eliminate the need for manual parameter initialization. Experimental results demonstrate that the DevelSet framework outperforms state-of-the-art approaches in terms of printability while achieving fast runtime performance (around 1 second). We expect this enhanced level set technique, coupled with a CUDA/DNN accelerated joint optimization paradigm, to have a substantial impact on industrial mask optimization solutions.

Modeling and Simulation of a Massively Parallelized Multiphoton Polymerization 3D Microfabrication Process

While additive manufacturing based on multiphoton polymerization is currently considered to be a very promising technique for the fabrication of 3D micro‐ and nanostructures, long fabrication times are a major limitation of this approach. Parallelization of the fabrication process is an important technique to overcome this issue. The fabrication process is parallelized by imaging a 1920 × 1080 pixel spatial light modulator into an ultrasensitive triplet–triplet annihilation resist. However, proximity effects between close pixels generate uncontrolled polymerization and make the controlled fabrication of 3D structures difficult. This work models light propagation and chemical interactions in the system to predict fabricated structures with a view to precompensating plot data and improving 3D resolution by performing optical and chemical proximity correction. A simple model gives reasonable predictions of fabricated structures helping us fabricate fully 3D structures in parallel.

Inverse lithography physics-informed deep neural level set for mask optimization.

As the feature size of integrated circuits continues to decrease, optical proximity correction (OPC) has emerged as a crucial resolution enhancement technology for ensuring high printability in the lithography process. Recently, level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution, showcasing its powerful pattern fidelity, especially in advanced processing. However, the massive computational time consumption of ILT limits its applicability to mainly correcting partial layers and hotspot regions. Deep learning (DL) methods have shown great potential in accelerating ILT. However, the lack of domain knowledge of inverse lithography limits the ability of DL-based algorithms in process window (PW) enhancement, etc. In this paper, we propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization. This approach utilizes level set-based ILT as a layer within the DL framework and iteratively conducts mask prediction and correction to significantly enhance printability and PW in comparison with results from pure DL and ILT. With this approach, the computational efficiency is significantly improved compared with ILT. By gearing up DL with the knowledge of inverse lithography physics, ILDLS provides a new and efficient mask optimization solution.

Optical proximity correction by using unsupervised learning and the patch loss function: publisher's note.

This publisher's note contains corrections to Appl. Opt.61, 3924 (2022)APOPAI0003-693510.1364/AO.454357.

Applications of large field of view e-beam metrology to contour-based optical proximity correction modeling

Applications of large field of view e-beam metrology to contour-based optical proximity correction modeling

DTCO optimizes critical path nets to improve chip performance with timing-aware OPC in deep ultraviolet lithography.

Design technology co-optimization (DTCO) is a potential approach to tackle the escalating expenses and complexities associated with pitch scaling. This strategy offers a promising solution by minimizing the required design dimensions and mitigating the pitch scaling trend. It is worth noting that lithography has played a significant role in dimensional scaling over time. This paper proposes a DTCO flow to reduce the impact of the process variation (PV) band and edge placement error (EPE). First, we performed the digital back-end design of the high-performance processor and got the test layout; second, we executed timing analysis on the test layout to get the critical path net that affects the chip performance; third, we proposed the timing-aware optimized optical proximity correction (OPC) method to optimize the PV band and EPE by adjusting the weights of critical path net merit points, optimizing the generation of the sub-resolution assistant feature, giving tighter EPE specs for merit points on the critical path net, and placing denser merit points as well as denser breakpoints for the critical path net to obtain greater freedom in the OPC process. Finally, it is verified that our proposed DTCO process can significantly reduce the EPE and lead to a slight decrease in the PV band of the chip while maintaining the same process windows.