We propose a new concept of tuning a point-spread function (a “kernel” function) in the modeling of electron beam
lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the
output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this
work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the
fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel.
Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer
network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give
researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better
critical dimension (CD) and positional accuracy almost instantly.
Semiconductor scaling is slowing down because of difficulties of device manufacturing below logic 7nm
node generation. Various lithography candidates which include ArF immersion with resolution enhancement
technology (like Inversed Lithography technology), Extreme Ultra Violet lithography and Nano Imprint
lithography are being developed to address the situation. In such advanced lithography, shot counts of mask
patterns are estimated to increase explosively in critical layers, and then it is hoped that multi beam mask
writer (MBMW) is released to handle them within realistic write time. However, ArF immersion technology
with multiple patterning will continue to be a mainstream lithography solution for most of the layers. Then,
the shot counts in less critical layers are estimated to be stable because of the limitation of resolution in ArF
immersion technology. Therefore, single beam mask writer (SBMW) can play an important role for mask
production still, relative to MBMW. Also the demand of SBMW seems actually strong for the logic 7nm
node. To realize this, we have developed a new SBMW, EBM-9500 for mask fabrication in this generation. A
newly introduced electron beam source enables higher current density of 1200A/cm<sup>2</sup>. Heating effect
correction function has also been newly introduced to satisfy the requirements for both pattern accuracy and
throughput. In this paper, we will report the configuration and performance of EBM-9500.
Resist heating effect which is caused in electron beam lithography by rise in substrate temperature of a few tens or hundreds of degrees changes resist sensitivity and leads to degradation of local critical dimension uniformity (LCDU). Increasing writing pass count and reducing dose per pass is one way to avoid the resist heating effect, but it worsens writing throughput. As an alternative way, NuFlare Technology is developing a heating effect correction system which corrects CD deviation induced by resist heating effect and mitigates LCDU degradation even in high dose per pass conditions. Our developing correction model is based on a dose modulation method. Therefore, a kind of conversion equation to modify the dose corresponding to CD change by temperature rise is necessary. For this purpose, a CD variation model depending on local pattern density was introduced and its validity was confirmed by experiments and temperature simulations. And then the dose modulation rate which is a parameter to be used in the heating effect correction system was defined as ideally irrelevant to the local pattern density, and the actual values were also determined with the experimental results for several resist types. The accuracy of the heating effect correction was also discussed. Even when deviations depending on the pattern density slightly remains in the dose modulation rates (i.e., not ideal in actual), the estimated residual errors in the correction are sufficiently small and acceptable for practical 2 pass writing with the constant dose modulation rates. In these results, it is demonstrated that the CD variation model is effective for the heating effect correction system.