LIC-CGAN: fast lithography latent images calculation method for large-area masks using deep learning.

Latent image calculation for large-area masks is an indispensable but time-consuming step in lithography simulation. This paper presents LIC-CGAN, a fast method for three-dimensional (3D) latent image calculation of large-area masks using deep learning. Initially, the library of mask clips and their corresponding latent images is established, which is then used to train conditional generative adversarial networks (CGANs).

Source and mask optimization for stability of reticle and wafer stages.

The relative motion of the reticle stage and wafer stage caused by vibration during the scanning exposure process of the lithography machine is an important factor that affects the imaging quality under limited lithography ability. In this paper, the influence of the vibration of the lithography stage system on the lithography imaging quality is studied. The lithography model including the vibration error of the stage is taken into account in the framework of source and mask optimization (SMO).

Convolutional Neural Network-Assisted Photoresist Formulation Discriminator Design of a Contact Layer for Electron Beam Lithography.

The photoresist formulation is closely related to the material properties, and its composition content determines the lithography imaging quality. To satisfy the process requirements, imaging verification of extensive formulations is required through lithography experiments. Identifying photoresist formulations with a high imaging performance has become a challenge.

Mask structure optimization for beyond EUV lithography.

Beyond extreme ultraviolet (BEUV) lithography with a 6 × nm wavelength is regarded as a future technique to continue the pattern shirking in integrated circuit (IC) manufacturing. This work proposes an optimization method for the mask structure to improve the imaging quality of BEUV lithography. Firstly, the structure of mask multilayers is optimized to maximize its reflection coefficient.

Cluster sampling and scalable Bayesian optimization with constraints for negative tone development resist model calibration.

As the semiconductor technology node continues to shrink, achieving smaller critical dimension in lithography becomes increasingly challenging. Negative tone development (NTD) process is widely employed in advanced node due to their large process window. However, the unique characteristics of NTD, such as shrinkage effect, make the NTD resist model calibration more complex.

Machine Learning Applied to Electron Beam Lithography to Accelerate Process Optimization of a Contact Hole Layer.

Determining the lithographic process conditions with high-resolution patterning plays a crucial role in accelerating chip manufacturing. However, lithography imaging is an extremely complex nonlinear system, and obtaining suitable process conditions requires extensive experimental attempts. This severely creates a bottleneck in optimizing and controlling the lithographic process conditions.

SCAPSM: attenuated phase-shift mask structure for EUV lithography.

The attenuated phase-shift mask (Att. PSM) is proven to be a promising resolution enhancement technology (RET) to improve the imaging performance in extreme ultraviolet (EUV) lithography. However, due to the reflective nature of the mask structure, the serious shadowing effect can affect the diffraction near field of the mask intensely and further impact the lithography imaging.

Method for optical proximity correction based on a 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.

Dynamic budget analysis of multiple parameters in a lithography system based on the superposition of light intensity fluctuations.

Lithography is one of the most critical processes in the manufacturing of micro- and nano-devices. As device critical dimensions continue to shrink, variations in system parameters during the lithography process often result in heavy deviations from the intended targets, making control of these parameters crucial to ensure that lithography results meet process requirements. Gaining a thorough comprehension of how various parameters interact and contribute to lithography errors is significant, and it is equally important to offer precise suggestions for managing these parameters in extreme ultraviolet lithography (EUVL) scanners.

Machine learning in electron beam lithography to boost photoresist formulation design for high-resolution patterning.

The reduction of the critical dimension (CD) usually improves the resolution of patterns and performance of chips. In chip manufacturing, electron beam lithography (EBL) is a promising technology for preparing sub-10 nm patterns, and its imaging resolution is primarily determined by the photoresist formulation. However, the smaller CDs are mainly achieved by optimizing process conditions, and little attention has been paid to the photoresist formulation optimization.

Mask correction method for surface plasmon lithography.

Surface plasmon lithography (SPL) has emerged as an innovative approach to nano-fabrication, offering an alternative to traditional patterning methods. To enhance its pattern fidelity in manufacturing, it is essential to incorporate mask correction to reduce critical dimension (CD) errors between the intended target features and the photoresist image. Traditionally, the aerial image of SPL has been modeled and simulated using methods such as finite difference time domain (FDTD) or rigorous coupled wave analysis (RCWA).

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.

Three-dimensional plasmonic lithography imaging modeling based on the RCWA algorithm for computational lithography.

This paper reminds the principle and characteristics of plasmonic lithography, and points out the importance of establishing a fast and high precision plasmonic lithography imaging model and developing computational lithography. According to the characteristics of plasmonic lithography, the rigorous coupled-wave analysis (RCWA) algorithm is a very suitable alternative algorithm. In this paper, a three-dimensional plasmonic lithography model based on RCWA algorithm is established for computational lithography requirements.

Aberration budget analysis of EUV lithography from the imaging performance of a contact layer in a 5 nm technology node.

By analyzing the impact of aberration in an extreme ultraviolet lithography projector on the imaging indicators of the test patterns for a contact layer in a 5 nm technology node, this paper establishes a mathematical aberration model based on the back propagating neutral network. On the basis of an aberration model, a method for estimating the aberration budget is proposed, which can help reduce the difficulty of achieving imaging performance thresholds in actual production. The performance of the results given by this method is verified by using a rigorous simulation.

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.

Fast diffraction model of an EUV mask based on asymmetric patch data fitting.

Calculating the diffraction near field (DNF) of a three-dimensional (3D) mask is a key problem in the extreme ultraviolet (EUV) lithography imaging modeling. This paper proposes a fast DNF model of an EUV mask based on the asymmetric patch data fitting method. Due to the asymmetric imaging characteristics of the EUV lithography system, a DNF library is built up including the training mask patches posed in different orientations and their rigorous DNF results.

Balance between the diffraction efficiency and process robustness for plasmonic lithographic alignment technology considering the Fabry-Perot resonator effect.

Different from traditional lithography, metal material with high absorptivity and high reflectivity is introduced into plasmonic lithography technology. In particular, a silver/photo resist/silver film stack can form a Fabry-Perot (F-P) resonator structure, which can greatly change the behavior of the light reflection and transmission. Since the silver layer has a strong absorption ability to the alignment probe light with a wavelength of 532 or 633 nm, the quality of the alignment signal is seriously affected.

Spatial modulation of scalable nanostructures by combining maskless plasmonic lithography and grayscale-patterned strategy.

Nanolithography techniques providing good scalability and feature size controllability are of great importance for the fabrication of integrated circuits (IC), MEMS/NEMS, optical devices, nanophotonics, Herein, a cost-effective, easy access, and high-fidelity patterning strategy that combines the high-resolution capability of maskless plasmonic lithography with the spatial morphology controllability of grayscale lithography is proposed to generate the customized pattern profile from microscale to nanoscale. Notably, the scaling effect of gap size in plasmonic lithography with a contact bowtie-shaped nanoaperture (BNA) is found to be essential to the rapid decay characteristics of an evanescent field, which leads to a wide energy bandwidth of the required optimal dose to record pattern in per unit volume, and hence, achieves the volumetrically scalable control of the photon energy deposition in the space more precisely. Based on the proper calibration and cooperation of pattern width and depth, a grayscale-patterned map is designed to compensate for the dose difference caused by the loss of the high spatial frequency component of the evanescent field.

Influence of the vibration of extreme ultraviolet lithographic tool stages on imaging.

Vibrations of the reticle and wafer stage are inevitable due to the high speed and acceleration required during the exposure movement of the lithography system. Previous studies have shown that these vibrations have an impact on both overlay and imaging quality. Furthermore, as the integrated circuit industry continues to develop and extreme ultraviolet (EUV) lithography is increasingly utilized, the size of the exposure image continues to decrease, making the stability of the reticle and wafer stage motion increasingly important.

Decomposition-learning-based thick-mask model for partially coherent lithography system.

The simulation of thick-mask diffraction near-field (DNF) is an indispensable process in aerial image calculation of immersion lithography. In practical lithography tools, the partially coherent illumination (PCI) is applied since it can improve the pattern fidelity. Therefore, it is necessary to precisely simulate the DNFs under PCI.

Process optimization of line patterns in extreme ultraviolet lithography using machine learning and a simulated annealing algorithm.

Resolution, line edge/width roughness, and sensitivity (RLS) are critical indicators for evaluating the imaging performance of resists. As the technology node gradually shrinks, stricter indicator control is required for high-resolution imaging. However, current research can improve only part of the RLS indicators of resists for line patterns, and it is difficult to improve the overall imaging performance of resists in extreme ultraviolet lithography.

Enhancement of pattern quality in maskless plasmonic lithography via spatial loss modulation.

Plasmonic lithography, which uses the evanescent electromagnetic (EM) fields to generate image beyond the diffraction limit, has been successfully demonstrated as an alternative lithographic technology for creating sub-10 nm patterns. However, the obtained photoresist pattern contour in general exhibits a very poor fidelity due to the near-field optical proximity effect (OPE), which is far below the minimum requirement for nanofabrication. Understanding the near-field OPE formation mechanism is important to minimize its impact on nanodevice fabrication and improve its lithographic performance.

Process optimization of contact hole patterns via a simulated annealing algorithm in extreme ultraviolet lithography.

The critical dimension (CD), roughness, and sensitivity are extremely significant indicators for evaluating the imaging performance of photoresists in extreme ultraviolet lithography. As the CD gradually shrinks, tighter indicator control is required for high fidelity imaging. However, current research primarily focuses on the optimization of one indicator of one-dimensional line patterns, and little attention has been paid to two-dimensional patterns.

Plasmonic lithography fast imaging model based on the decomposition machine learning method.

Plasmonic lithography can make the evanescent wave at the mask be resonantly amplified by exciting surface plasmon polaritons (SPPs) and participate in imaging, which breaks through the diffraction limit in conventional lithography. It provides a reliable technical way for the study of low-cost, large-area and efficient nanolithography technology. This paper introduces the characteristics of plasmonic lithography, the similarities and the differences with traditional DUV projection lithography.