In the semiconductor fabrication process, yield is negatively impacted by defects that appear systematically within specific patterns of the physical layout design. Those defective patterns are popularly known as hotspots, and they can arise due to various causes. There are several known approaches of hotspot detection. One approach for hotspot detection is Machine Learning (ML), where known hotspot and non-hotspot patterns are used for training the model to be used afterwards in prediction of new hotspots. The objective in ML approaches is to maximize the hit rate (i.e. finding all potential hotspots) and to minimize the false alarm rate (i.e. reduce the overhead of false positives). The model’s ability to correctly classify between hotspots and non-hotspots depends on the coverage of the training data set. The real-world challenge in training a ML system to classify hotspots/non-hotspots is the imbalanced nature of the problem, where the known hotspot patterns are always in the minority class. Another challenge specific to the problem of hotspot classification is the difficulty to correctly classify non-hotspots that are similar to hotspots. These “hard-to-classify” patterns are ones with high mask error enhancement factor (MEEF), as small variations in the pattern can make it change between hotspot and non-hotspot. These two challenges cause conventional methods of handling imbalanced training datasets to be inadequate to the problem of hotspot detection. This paper will present a flow for quantified training dataset selection approach and put extra focus on the patterns that are hard to classify due to close similarity with known hotspots. Improved model accuracy is illustrated when adopting the quantified sampling approach compared to conventional sampling approaches.
As the litho hotspot detection runtime is currently in a continuous increase with sub-10nm technology nodes due to the increase of the design and process complexity, new methods and approaches are needed to improve the runtime while guaranteeing high accuracy rate. Machine-Learning Fast LFD (ML-FLFD) is a new flow that uses a specialized machine learning technique to provide fast and accurate litho hotspot detection. This methodology is based on having input data to train the machine learning model during the model preparation phase. Current ML-FLFD techniques depend on collecting hotspots (HS) and Non hotspots (NHS) data from the drawn layer in order to train the model. In this paper, we present a new technique where we use the retarget data to train the machine learning model instead of using the drawn hotspot data. Using retargeting data is getting one step closer to the actual printed contours which gives a better insight about the hotspots of the manufactured wires during the machine learning model training step. The effect of using closer data to the printed contours will be reflected on both the accuracy and the extra rate which will reduce simulation area. In the different sections of this paper, we will compare the new approach of using retarget data as a ML input to the current technique of using drawn data. Pros and cons of the two approaches will be listed in details including the experimental results of hotspot accuracy and litho simulation area.
A simple analytical model is developed to estimate the power loss and time delay in photonic integrated circuits fabricated
using SOI standard wafers. This model is simple and can be utilized in physical verification of the circuit layout to verify
its feasibility for fabrication using certain foundry specifications. This model allows for providing new design rules for the
layout physical verification process in any electronic design automation (EDA) tool. The model is accurate and compared
with finite element based full wave electromagnetic EM solver. The model is closed form and circumvents the need to
utilize any EM solver for verification process. As such it dramatically reduces the time of verification process and allows
fast design rule check.
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