Abstract

Projection lithography using extreme ultraviolet (EUV) light at 13.5-nm wavelength will be applied to the production of integrated circuits below 7-nm design rules. In pursuit of further miniaturization, however, stochastic pattern defect problems have arisen, and monitoring such defect generation probabilities in extremely low range (<10 − 10) is indispensable. We discuss a method for predicting stochastic defect probabilities from a histogram of feature sizes for patterns several orders of magnitude fewer than the number of features to inspect. Based on our previously introduced probabilistic model of stochastic pattern defect, the defect probability is expressed as the product sum of the probability for edge position and the probability that film defect covers the area between edges, and we describe the latter as a function of edge position. The defect probabilities in the order between 10 − 7 and 10 − 5 were predicted from 105 measurement data for real EUV-exposed wafers, suggesting the effectiveness of the model and its potential for defect inspection.

Highlights

  • Projection lithography using extreme ultraviolet (EUV) light at the 13.5-nm wavelength is expected to achieve production of integrated circuits (ICs) below 7-nm design rules.[1]

  • Because cutting-edge integrated circuit devices today have more than 1012 critical features per a device layer on a 300-mm wafer, such a defect probability will result in an unacceptable level of defect density

  • While suppressing the stochastic defect itself is indispensable for EUV lithography, monitoring and control of these defects is another crucial issue.[4,5,6,7]

Read more

Summary

Introduction

Projection lithography using extreme ultraviolet (EUV) light at the 13.5-nm wavelength is expected to achieve production of integrated circuits (ICs) below 7-nm design rules.[1]. The exponential relationships between defect probabilities and exposure dosage required for obtaining designed size observed among varieties of resist materials[4] are explained by the model.[9] Our basic approach is to predict defect probability by evaluating Pedge and P2 in Eq (2), not by directly inspecting full-pattern features.

Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call