Sub-resolution Assist Features Research Articles

CTM-SRAF: Continuous Transmission Mask-Based Constraint-Aware Subresolution Assist Feature Generation

In lithography process, Sub-Resolution Assist Features (SRAFs), as an essential resolution enhancement technique (RET), is applied to improve the pattern fidelity and enlarge the process window. In this paper, we propose a robust constraint-aware SRAF generation method based on continuous transmission mask (CTM). The intensity distribution on the continuous transmission mask is extracted to guide the SRAF generation. The SRAF insertion also honors the design rules, which is formulated as integer programming with quadratic constraints and solved by a fast yet efficient algorithm. A fast probe-based SRAF evolution method is proposed to determine the shapes of SRAFs. The effectiveness and efficiency are demonstrated based on the experimental results.

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.

Evaluation of Lines and Spaces printing and general understanding of imaging with dark field low-n mask

BackgroundOne of the ways to push down resolution limits in extreme ultraviolet (EUV) lithography is to use alternative masks. New types of absorbers are being broadly investigated for current 0.33 NA as well as for high NA of 0.55.AimWe study the imaging performance of a low-n/low-k absorber (ABS) dark field (DF) mask for lines and spaces (LS) printing at 0.33 NA.ApproachExperimentally and via simulations, we investigate the use of low-n DF masks for a metal direct print application and compare low-n mask performance with Ta-based ABS masks.ResultsCompared to a Ta-based ABS mask used nowadays, the low-n DF mask brings contrast gain for dense LS. Also, for low-n masks, we observe large best focus (BF) shifts for isolated features with respect to dense features and change of bias. Hence, strong mask critical dimensions (CD) control is needed. We demonstrate how adding assist features can align BF through pitch. In addition, placing subresolution assist features (SRAFs) significantly enhances exposure latitude for semi/iso and iso-LS and improves the overlapping process window for selected cases.ConclusionsThese investigations show that low-n DF masks can enhance the imaging of LS through pitch if SRAFs are used and a good mask CD control is achieved. Our work further adds fundamental understanding to advantages observed for a metal direct print application of alternative ABS masks for the current and future EUV tools.

Nanoscale Phase Change Material Array by Sub-Resolution Assist Feature for Storage Class Memory Application

High density phase change memory array requires both minimized critical dimension (CD) and maximized process window for the phase change material layer. High in-wafer uniformity of the nanoscale patterning of chalcogenides material is challenging given the optical proximity effect (OPE) in the lithography process and the micro-loading effect in the etching process. In this study, we demonstrate an approach to fabricate high density phase change material arrays with half-pitch down to around 70 nm by the co-optimization of lithography and plasma etching process. The focused-energy matrix was performed to improve the pattern process window of phase change material on a 12-inch wafer. A variety of patternings from an isolated line to a dense pitch line were investigated using immersion lithography system. The collapse of the edge line is observed due to the OPE induced shrinkage in linewidth, which is deteriorative as the patterning density increases. The sub-resolution assist feature (SRAF) was placed to increase the width of the lines at both edges of each patterning by taking advantage of the optical interference between the main features and the assistant features. The survival of the line at the edges is confirmed with around a 70 nm half-pitch feature in various arrays. A uniform etching profile across the pitch line pattern of phase change material was demonstrated in which the micro-loading effect and the plasma etching damage were significantly suppressed by co-optimizing the etching parameters. The results pave the way to achieve high density device arrays with improved uniformity and reliability for mass storage applications.

Through-focus EUV multilayer defect compensation considering optical proximity correction.

Extreme ultraviolet (EUV) multilayer defects result in the degradation of through-focus imaging quality. The optical proximity effect is another crucial factor that degrades the imaging quality. Both the impacts of the defects and the optical proximity effects could be mitigated by modifying the original mask patterns. A heuristic-based defect compensation method considering optical proximity correction and through-focus optimization is proposed in this paper. The edge of the mask pattern and the insertion of sub-resolution assist features (SRAFs) are optimized by covariance matrix adaptation evolution strategy (CMA-ES) to compensate for the degradation of the imaging quality with a certain defocus range. New encoding strategies for the edge pixels of the mask pattern and the SRAFs are proposed and utilized in this paper to ensure the manufacturability of the mask and the efficiency of the optimization at the same time. The rigorous database approach based on the scattering matrix is adopted to simulate the mask diffraction spectrum efficiently. Simulations verify that the through-focus imaging quality of both the defective masks with bump defects and pit defects could be obviously improved by the proposed defect compensation method.

Exploration of alternative mask for 0.33NA extreme ultraviolet single patterning at pitch 28-nm metal design

Extending 0.33NA extreme ultraviolet (EUV) single patterning to pitch 28 nm will enable significantly shorter process flow for N2 node and cost-efficiency of metal layers patterning. At the same time, EUV single patterning becomes very challenging in terms of stochastic defectivity and process window. To enable EUV single patterning at pitch 28 nm with good process window and patterning fidelity (low defectivity and line edge roughness), three mask candidates are considered: a standard binary Ta-based absorber mask, a high extinction (high-k) absorber mask, and a low-n attenuated phase-shift mask (attPSM). The patterning performance of these three mask candidates is compared by means of source mask optimization. The patterning performance of the candidate masks is assessed using an imec N3 (foundry N2 equivalent) random logic M1 layout. The impact of mask tonality (bright field versus dark field) and insertion of sub-resolution assist features (SRAFs) on pattern fidelity and process window is evaluated. Considering all the aspects, simulations indicate that the low-n attPSM has the best patterning performance both for dark-field mask with SRAFs and bright-field mask without SRAFs.

Sub-resolution assist feature placement with generative adversarial network

Background: As the design layout of integrated circuits (ICs) is continually scaling down, sub-resolution assist features (SRAF) have been extensively used in resolution enhancement technique applications to enhance lithography printing fidelity and widen the manufacturing process window (PW). With conventional SRAF insertion techniques, rule-based SRAF (RB-SRAF) and model-based SRAF (MB-SRAF) methods have since been widely adopted. Aim: The typical RB-SRAF is an efficient method to generate SRAFs consistently for simple designs but cannot be optimized for multiple critical patterns or complex layout schemes. Although MB-SRAF is able to achieve better PW as well as reducing conflicts between placement rules and clean-up rules, many iterations for convergence and extremely high computational costs are required. The explosion of machine learning techniques could facilitate the complex processes of mask optimization, such as SRAF insertion. Approach: Generative adversarial network was studied on a Via layer of advanced 3D NAND flash memory, by training target images and Inverse Lithography Technology (ILT) images of target patterns. GAN models, pix2pix, and CycleGAN, were first trained and then utilized to synthesize realistic ILT images. Results: These ILT images were eventually translated to polygons of SRAF with simplification process and mask manufacturing rules check constraints. The simulation results demonstrate that CycleGAN approach can place SRAF with comparable performance to mask optimization (MO) result which was optimized by the Tachyon source-mask optimizer (SMO). Conclusions: Methodologies of SRAF placement based on GANs were demonstrated in this paper. While traditional MO approach showed the best lithography performance, CycleGAN in particular was close behind and had much better performance than conventional RB-SRAF approach, whilst achieving good stability for different layout environments. Most importantly, the efficiency of SRAF insertion can be enhanced significantly through GANs.

GAN-SRAF: Subresolution Assist Feature Generation Using Generative Adversarial Networks

As the integrated circuits (ICs) technology continues to scale, resolution enhancement techniques (RETs) are mandatory to obtain high manufacturing quality and yield. Among various RETs, subresolution assist feature (SRAF) generation is a key technique to improve the target pattern quality and lithographic process window. While model-based SRAF insertion techniques have demonstrated high accuracy, they usually suffer from high computational cost. Therefore, more efficient techniques that can achieve high accuracy while reducing runtime are in strong demand. In this article, we leverage the recent advancement in machine learning for image generation to tackle the SRAF insertion problem. In particular, we propose a new SRAF insertion framework, GAN-SRAF, which uses generative adversarial networks (GANs) to generate SRAFs directly for any given layout. Our proposed approach incorporates a novel layout to image encoding using multichannel heatmaps to preserve the layout information and facilitate layout reconstruction. Our experimental results demonstrate ${\sim }14.6\times $ reduction in runtime when compared to the previous best machine learning approach for SRAF generation, and ${\sim }144\times $ reduction compared to the model-based approach, while achieving comparable quality of results.

Sub-Resolution Assist Feature Cleanup Based on Grayscale Map

As an approach of resolution enhancement technique (RET), sub-resolution assist feature (SRAF) is used to meet the pitch requirements for sparse patterns in off-axis illumination (OAI) and supplements the spatial frequencies of main patterns to improve the resolution. Rules-based SRAF (RB-SRAF) is a technique that foundry extracts SRAF placement rules from wafer data or simulation data and then apply these rules to full chip. RB-SRAF often produces a lot of conflicts. Usually, the geometric constrains are the only factors taken into account in cleaning up of these conflicts, which may lead to mistaken cleanup and be counter-productive. Therefore, it is necessary to introduce some physical factors in the course of cleanup. The transmissivity distribution of mask in hotspot region can be obtained by inverse lithography technology (ILT). And the statistical information of the grayscale map from ILT can be used for leading cleanup and keeping the SRAFs which contribute more to process window (PW). The feasibility of this method has been verified by a series of simulations.

SRAF Insertion via Supervised Dictionary Learning

In modern VLSI design flow, subresolution assist feature (SRAF) insertion is one of the resolution enhancement techniques (RETs) to improve chip manufacturing yield. With aggressive feature size continuously scaling down, layout feature learning becomes extremely critical. In this article, for the first time, we enhance conventional manual feature construction, by proposing a supervised online dictionary learning algorithm for simultaneous feature extraction and dimensionality reduction. By taking advantage of label information, the proposed dictionary learning framework can discriminatively and accurately represent the input data. We further consider SRAF design rules in a global view, and design two integer linear programming models in the post-processing stage of SRAF insertion framework. The experimental results demonstrate that, compared with a state-of-the-art SRAF insertion tool, our framework not only boosts the performance of the machine learning model but also improves the mask optimization quality in terms of edge placement error (EPE) and process variation (PV) band area.

Placement of Sub-Resolution Assist Features Based on a Genetic Algorithm

Resolution enhancement techniques compatible with an ArF (193 nm) immersion optical lithography system may constitute an effective means of minimizing the size of technology nodes of the dynamic random access memory. This paper investigated one such technique, namely mask optimization (MO), and applied sub-resolution assist features (SRAFs) in the MO to improve the aerial image quality of a target pattern that had undergone optical proximity correction (OPC). This paper first developed an optical model based on the Hopkins model to create an interference map, which was then used to create a cut-level map. The cut-level map was instrumental in predicting potential SRAF sites and randomly generating SRAFs that would serve as the initial population for a genetic algorithm (GA). Chromosomes were defined as a section map and encoded genes were used to define SRAF and target pattern. Using a GA to identify SRAF geometric measurements and placement was revealed to increase the process window and improve the image performance of the target pattern. This paper used 1D and 2D line/space (L/S) images as the baseline to test the convergence of the proposed method. 2D images were also used to test improvements in aerial image performance. The results indicated that the 1D L/S pattern converged at the 100th iteration. Furthermore, the depths of the focus of the 2D L/S array and 2D contact hole patterns were successfully increased by 113 and 21 nm, respectively. The proposed SRAF method, which integrated the GA and interference map, was able to ensure the diversity of potential SRAF solutions. Moreover, it was able to restrict the SRAF solutions to rectangular structures through the application of mask rules, thereby reducing the cost and improving the feasibility of photomasks.

Subresolution assist features impact and implementation in extreme ultraviolet lithography for next-generation beyond 7-nm node

The next-generation beyond 7-nm node potentially requires the implementation of subresolution assist features (SRAF) with extreme ultraviolet (EUV) lithography. This paper aims at providing a clear SRAF strategy for the next-generation beyond 7-nm node designs through a series of experiments. Various factors are considered, including stochastic effects, three-dimensional (3-D) mask effects, through-slit effects, aberrations, and pixelated source mask optimization (SMO) sources. We consider process variability bands with a variety of process conditions, including focus/dose/mask bias changes and also the normalized image log-slope/image log-slope as our objective functions, to determine what the best SRAF solution is for a set of test patterns. Inverse lithography technology is implemented to optimize both the main feature (MF) mask and SRAF placement, in particular, asymmetric SRAF placement to balance the 3-D mask effects. SRAF can potentially mitigate image shift through-focus, i.e., nontelecentricity, caused by EUV 3-D shadowing effect. This shadowing effect is pattern-dependent and contributes to the overlay variation. As we approach the next-generation beyond 7-nm node, this image shift can be more significant relative to the overlay budget, hence, we further investigate the impact of SRAF placement to the image shift. Moreover, the center of focus shift due to the large 3-D mask absorber thickness can be potentially mitigated by SRAF implementation. The common process window is significantly impacted by both the center of focus shift and the individual depth of focus and is evaluated using both metal and contact layer test cases. We study the source impact to SRAF insertion by experimenting with both a symmetric source (standard source) and an asymmetric source (SMO source). Finally, we understand the importance of using full flare map and full through-slit model (including aberration variation through-slit) in the MF correction. Furthermore, we evaluate the need of using full models in SRAF insertion. This is a necessary step to determine the strategy of SRAF implementation for the next-generation beyond 7-nm node.

Subresolution Assist Feature Generation With Supervised Data Learning

Subresolution assist feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model-based and rule-based approaches are widely adopted in the semiconductor industry. The model-based SRAF generation can achieve a high accuracy but it is known to be time-consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. The rule-based SRAF generation is highly technology dependent and it is becoming extremely difficult to render high-quality results in advanced technology nodes. This paper proposes supervised data learning techniques for fast yet consistent SRAF generation with high-quality results. We first propose the constrained concentric circle with area sampling scheme for feature extraction. Illumination source symmetry-based feature compaction technique is further invented to reduce the training data set size and achieve consistent SRAF predictions. Using accurate model-based SRAFs as training data, classification models based on logistic regression (LGR) and support vector machine are calibrated for SRAF predictions. Moreover, the probability maximum prediction is proposed to generate manufacturing-friendly SRAFs with a greedy simplification scheme. We compare support vector machine and LGR models by embedding into an entire mask optimization flow, where the support vector machine model obtains better lithographic performance. Experimental results demonstrate that, compared with the commercial Calibre tool, supervised data learning techniques for SRAF generation obtain significant speed up (>3X for a ${100~{ {{\mathrm{um}}} }^{2}}$ layout clip) and comparable lithographic performance in terms of edge placement error and process variation band.

Challenges of anamorphic high-NA lithography and mask making

Abstract Chip makers are actively working on the adoption of 0.33 numerical aperture (NA) EUV scanners for the 7-nm and 5-nm nodes (B. Turko, S. L. Carson, A. Lio, T. Liang, M. Phillips, et al., in ‘Proc. SPIE9776, Extreme Ultraviolet (EUV) Lithography VII’, vol. 977602 (2016) doi: 10.1117/12.2225014; A. Lio, in ‘Proc. SPIE9776, Extreme Ultraviolet (EUV) Lithography VII’, vol. 97760V (2016) doi: 10.1117/12.2225017). In the meantime, leading foundries and integrated device manufacturers are starting to investigate patterning options beyond the 5-nm node (O. Wood, S. Raghunathan, P. Mangat, V. Philipsen, V. Luong, et al., in ‘Proc. SPIE. 9422, Extreme Ultraviolet (EUV) Lithography VI’, vol. 94220I (2015) doi: 10.1117/12.2085022). To minimize the cost and process complexity of multiple patterning beyond the 5-nm node, EUV high-NA single-exposure patterning is a preferred method over EUV double patterning (O. Wood, S. Raghunathan, P. Mangat, V. Philipsen, V. Luong, et al., in ‘Proc. SPIE. 9422, Extreme Ultraviolet (EUV) Lithography VI’, vol. 94220I (2015) doi: 10.1117/12.2085022; J. van Schoot, K. van Ingen Schenau, G. Bottiglieri, K. Troost, J. Zimmerman, et al., ‘Proc. SPIE. 9776, Extreme Ultraviolet (EUV) Lithography VII’, vol. 97761I (2016) doi: 10.1117/12.2220150). The EUV high-NA scanner equipped with a projection lens of 0.55 NA is designed to support resolutions below 10 nm. The high-NA system is beneficial for enhancing resolution, minimizing mask proximity correction bias, improving normalized image log slope (NILS), and controlling CD uniformity (CDU). However, increasing NA from 0.33 to 0.55 reduces the depth of focus (DOF) significantly. Therefore, the source mask optimization (SMO) with sub-resolution assist features (SRAFs) are needed to increase DOF to meet the demanding full chip process control requirements (S. Hsu, R. Howell, J. Jia, H.-Y. Liu, K. Gronlund, et al., EUV ‘Proc. SPIE9048, Extreme Ultraviolet (EUV) Lithography VI’, (2015) doi: 10.1117/12.2086074). To ensure no assist feature printing, the assist feature sizes need to be scaled with λ/NA. The extremely small SRAF width (below 25 nm on the reticle) is difficult to fabricate across the full reticle. In this paper, we introduce an innovative ‘attenuated SRAF’ to improve SRAF manufacturability and still maintain the process window benefit. A new mask fabrication process is proposed to use existing mask-making capability to manufacture the attenuated SRAFs. The high-NA EUV system utilizes anamorphic reduction; 4× in the horizontal (slit) direction and 8× in the vertical (scanning) direction (J. van Schoot, K. van Ingen Schenau, G. Bottiglieri, K. Troost, J. Zimmerman, et al., ‘Proc. SPIE. 9776, Extreme Ultraviolet (EUV) Lithography VII’, vol. 97761I (2016) doi: 10.1117/12.2220150; B. Kneer, S. Migura, W. Kaiser, J. T. Neumann, J. van Schoot, in ‘Proc. SPIE9422, Extreme Ultraviolet (EUV) Lithography VI’, vol. 94221G (2015) doi: 10.1117/12.2175488). For an anamorphic system, the magnification has an angular dependency, and thus, familiar mask specifications such as mask error factor (MEF) need to be redefined. Similarly, mask-manufacturing rule check (MRC) needs to consider feature orientation.

Sub-Resolution Assist Feature in Attenuated Phase-Shift Mask for Extreme Ultraviolet Lithography

Sub-Resolution Assist Feature in Attenuated Phase-Shift Mask for Extreme Ultraviolet Lithography

Gradient-based inverse extreme ultraviolet lithography.

Extreme ultraviolet (EUV) lithography is the most promising successor of current deep ultraviolet (DUV) lithography. The very short wavelength, reflective optics, and nontelecentric structure of EUV lithography systems bring in different imaging phenomena into the lithographic image synthesis problem. This paper develops a gradient-based inverse algorithm for EUV lithography systems to effectively improve the image fidelity by comprehensively compensating the optical proximity effect, flare, photoresist, and mask shadowing effects. A block-based method is applied to iteratively optimize the main features and subresolution assist features (SRAFs) of mask patterns, while simultaneously preserving the mask manufacturability. The mask shadowing effect may be compensated by a retargeting method based on a calibrated shadowing model. Illustrative simulations at 22 and 16nm technology nodes are presented to validate the effectiveness of the proposed methods.

Subresolution assist features in extreme ultraviolet lithography

Lithographic critical dimension (CD) printing variability can be easily captured with a CD uniformity measurement; however, minimizing the variability is a challenging task that requires manipulation of many variables. Contact hole variability has a direct impact on device performance, while via variability affects metal area scaling and design. Subresolution assist features (SRAFs) have been used in the past to improve lithographic printing variability. SRAFs enhance the image log slope of nearby features but are not intended to print themselves. The role of SRAFs in extreme ultraviolet is explored here.

Sub-Resolution Assist Features Challenge and Solution in 28nm Active Area Lithography

Sub-resolution assist features (SRAFs) provide an absolutely essential technique for critical dimension (CD) control and process window enhancement in immersion lithography. The selection of SRAF’s size and placement are vital. 28nm node requires stringent CD control for active area (AA) layer. Since both AA line CD and space CD control are important, not only scattering bar (SB) but also reverse scattering bar (RSB) is used to improve AA litho process window. SB is used to improve line pattern process window, meanwhile RSB is implemented to improve space pattern process window as space CD shrinks as small as possible to meet design rule requirement. The main challenges of SRAFs are the proper SRAF size, right SRAFs placement, and avoiding SRAFs printing issue. SRAF size needs to balance process window and SRAF printing issue. Further more, mask making feasibility is also considered. 28nm AA lithography SRAFs rule simulation study with FDTD is used to optimize the AA litho process. 28nm AA test vehicle with SB and RSB size split and location split is designed to determine the integrated SRAFs solution. After implement thorough considered SRAFs rule, 28nm AA litho through pitch line and through pitch space process window can meet manufacturing specification.

Block-based mask optimization for optical lithography

Pixel-based optical proximity correction (PBOPC) methods have been developed as a leading-edge resolution enhancement technique (RET) for integrated circuit fabrication. PBOPC independently modulates each pixel on the reticle, which tremendously increases the mask's complexity and, at the same time, deteriorates its manufacturability. Most current PBOPC algorithms recur to regularization methods or a mask manufacturing rule check (MRC) to improve the mask manufacturability. Typically, these approaches either fail to satisfy manufacturing constraints on the practical product line, or lead to suboptimal mask patterns that may degrade the lithographic performance. This paper develops a block-based optical proximity correction (BBOPC) algorithm to pursue the optimal masks with manufacturability compliance, where the mask is shaped by a set of overlapped basis blocks rather than pixels. BBOPC optimization is formulated based on a vector imaging model, which is adequate for both dry lithography with lower numerical aperture (NA), and immersion lithography with hyper-NA. The BBOPC algorithm successively optimizes the main features (MF) and subresolution assist features (SRAF) based on a modified conjugate gradient method. It is effective at smoothing any unmanufacturable jogs along edges. A weight matrix is introduced in the cost function to preserve the edge fidelity of the printed images. Simulations show that the BBOPC algorithm can improve lithographic imaging performance while maintaining mask manufacturing constraints.

SRAF Optimization for sub-40nm Technology Node Contact Patterning

As 193nm immersion lithography is extended to sub-40nm technology node, how to define a single-pass contact hole patterning process with enough common lithography process window is a very challenging task. Sub-resolution assist features (SRAF) optimization is one crucial step besides illumination optimization and mask bias optimization. This paper presents a comprehensive SRAF optimization flow that achieves a set of SRAF solutions that balances SRAF benefits and mask manufacturing feasibility. As the criterion of patterning performance, the through pitch process window is investigated for different SRAF placement options based on simulated PV-band of a given process variation (focus & dose). Full chip optimization with representative layouts is also studied. Silicon level performance verification is done with final SRAF solution for through pitch pattern and typical layouts. Finally, we conclude the paper by demonstrating the overall benefit of SRAF on CD uniformity, from process capability as well as mask complexity perspectives.