Optimisation criteria for the process electron beam lithography of negative AR-N7520 resists

Abstract

Experimental investigation of negative electron resist AR-N 7520 profiles using an electron beam lithography system ZBA23 (Raith) is performed at variation of the exposure doses and the exposure patterns. The form of the obtained after the exposure resist profiles is investigated and optimized. Artificial neural networks for the dependence of the overall geometry of the obtained resist profiles on process parameters are trained, tested and validated. Several overall geometry quality criteria for the shape of the developed resist profile cross-sections are defined. An approach, based on the defined overall quality characteristics and multicriterial parameter optimization, is proposed and implemented for fulfillment of the technological requirements for the produced resist profile dimensions.

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

“…1). To address this issue, photoresist optimization methods based on experimental design, [31][32][33][34][35][36] ternary combination, 37 and a regression model 38 have been proposed; however, these methods cannot accurately provide the corresponding relationship between the photoresist formulation and lithographic performance indicators. Machine learning (ML) is a popular research field, and is the core technology of artificial intelligence (AI) applications.…”

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

“…21 Nevertheless, these methods will take a lot of time, especially for newly developed photoresists. To solve this problem, researchers have proposed process optimization methods based on a regression model, 22 a ternary gradient combination, 23 the Taguchi method, 24 a genetic algorithm, 21 and a particle swarm algorithm, 5 and have investigated the influence of process conditions on lithographic imaging. [13][14][15][16][25][26][27][28] However, these optimization methods still need a large number of experiments, and the corresponding relationship between process conditions and lithographic imaging performance cannot be determined.…”