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

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. Herein, we develop a formulation discriminator of a metal oxide nanoparticle photoresist for a contact layer applied to electron beam lithography. This discriminator consists of convolutional neural network photoresist imaging and formulation classification models. A photoresist imaging model is adopted to predict the contact width of variable formulations, while a formulation classification model is used to classify formulations according to relative local critical dimension uniformity (RLCDU). The verification results indicate that the discriminator can accurately recognize photoresist formulations that simultaneously meet the conditions of contact width and RLCDU, and its feasibility has been demonstrated, providing a valuable reference for the preparation of photoresist materials.

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. Herein, we report a process optimization solution for a contact layer of metal oxide nanoparticle photoresists by combining electron beam lithography (EBL) experiments with machine learning. In this solution, a long short-term memory (LSTM) network and a support vector machine (SVM) model are used to establish the contact hole imaging and process condition classification models, respectively. By combining SVM with the LSTM network, the process conditions that simultaneously satisfy the requirements of the contact hole width and local critical dimension uniformity tolerance can be screened. The verification results demonstrate that the horizontal and vertical contact widths predicted by the LSTM network are highly consistent with the EBL experimental results, and the classification model shows good accuracy, providing a reference for process optimization of a contact layer.

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. Screening suitable photoresist formulations remains a significant challenge due to the considerable time and high cost. Herein, we report a formulation optimization technique of a metal oxide nanoparticle photoresist that combines EBL experiments with a machine learning long short-term memory (LSTM) network. Using the LSTM network, a CD photoresist evaluation model is established. Leveraging the CD model, a photoresist formulation optimizer is developed with a line width of 26 nm. The verification results demonstrate that the CDs predicted by the LSTM network are basically consistent with the EBL experimental results, and the photoresist formulations that meet the CD requirements can be screened. This work opens up a novel perspective to boost photoresist formulation design for high-resolution patterning with artificial intelligence and provides guidance for EBL experiments.

Critical dimension prediction of metal oxide nanoparticle photoresists for electron beam lithography using a recurrent neural network

The critical dimension (CD) of lithographic patterns is the most significant indicator for evaluating the imaging performance of photoresists, and its value is seriously affected by process conditions. However, the lithographic imaging system is highly nonlinear, and extensive exposure experiments are needed to obtain the desired CD. This consumes lots of time, manpower, and cost in screening for optimal process conditions. Here, we report a combined electron beam lithography (EBL) experiment and recurrent neural network (RNN) study on the CDs of metal oxide nanoparticle photoresists, and establish a CD RNN model. Leveraging the RNN model, a process condition filter is developed to screen suitable process conditions. The experimental results demonstrate that the prediction accuracy of the CD model exceeds 93%, and the photoresist patterns under the screened process conditions can satisfy the requirements of a preset CD. This work opens up a novel perspective for accurate EBL process modeling, and provides guidance for EBL experiments.