As the design node of memory device shrinks, OPC model accuracy is becoming ever more critical from development to manufacturing. To improve the model accuracy, more and more physical effects are analyzed and terms for those physical effects are added. But it is unachievable to capture the complete physical effects. In this study, deep neural network is employed and studied to improve model accuracy. Regularization is achieved using physical guidance model. To address overfitting issue, high volume of contour based edge placement (EP) gauges (>10K) are generated using fast eBeam tool (eP5) and metrology processing software (MXP) without increasing turnaround time. It is shown that the new approach improved model accuracy by >47% compared to traditional approach on >1.4K verification gauges.
Current patterning technology for manufacturing memory devices is being developed towards enabling high density
and high resolution capability. However, as applying high resolution technology results in decreased process margin,
OPC has to compensate for such effect. Since the process margin is decreased greatly for contact layers, technologies
such as RBAF (Rule-Based Assist Feature), MBAF (Model-Based Assist Feature), and ILT (Inverse Lithography
Technology) are considered to maximize the process margin [1, 2, 3]. Although ILT is the best solution in terms of
process margin, it has several disadvantages such as long OPC run-time, mask complexity, and unstable mask fidelity.
MBAF method is a good compromise for more advanced techniques mitigating those risks (but not eliminating it),
which is why it is often used for contact layers.
When setting up the rules for RBAF, not all patterns are considered. Thus, applying RBAF for contact layers may
result in decreased process margin for certain patterns since the same rule is applied globally. MBAF, on the other hand,
can maximize the process margin for various patterns as it generates AF (Assist Feature) to locations that maximize the
margin for the patterns considered. However, MBAF method is very sensitive to even a slight change of a target, which
influences the locations of the AF. This leads to generating different OPCed CD of the main features, even for those that
should not be affected by the changed target. Once the OPCed CD is changed, it is impossible to obtain the same mask
CD even when the mask is manufactured with the same method. If this case occurs during mass production, the entire
layer needs to be confirmed after each revision which leads to unnecessary time loss.
In this paper, we suggest a new OPC method to prevent this issue. With this flow, OPCed shapes of unchanged
patterns remain the same while only the changed targets are OPCed and replaced into the corresponding location, while
the boundaries between those regions are corrected using a model based boundary healing. This method can reduce the
overall OPCTAT as well as the time spent in verifying the entire layout after each revision. Details of these results will
be described in this paper. After further studies, this flow can also be applied to ILT.