LITHOGRAPHY HOSTPOT DETECTION USING SVM

By Anmol Salvi

INTRODUCTION: Lithography can be defined as the process of transferring patterns of a particular geometric shape in a mask to a thin layer of radiation sensitive material called the resist , thus covering the surface of a semiconductor wafer . Lithography hotspots are places which are susceptible to having fatal pinching (open circuits) or bridging (short circuit) , which occurs due to poor printability of some patterns in a given design layout. In order to avoid such undesirable patterns in a layout , it is mandatory to find hotspots in the early design phase.  As manufacturing conditions evolve , lithography hotspot detection faces many challenges which include:-

a)Real hotspots are extremely hard to find during early design stages and also hard to fix in the post layout phase.

b)False alarms are detected which result in expensive post processing hotspot removal.

c)Full chip physical verification as well as optimization requires an extremely fast turn-around time.

The above problems can be solved by using an appropriate ML algorithm to save time and provide accurate predictions.

 

IMPLEMENTATION: SVM which stands for Support Vector Machines , can be used in order to develop a faster and more accurate hotspot identifier and in turn a better model for lithography hotspot detection . It is a supervised learning algorithm and a multiclass classifier. The first step involves training , which is carried out by providing the model with the required values from the dataset , where these values are first normalized to a scale ranging between [-1 , 1]. The model will assign weights to different parameters and return support vectors. More the data it is trained with ,  more is the error reduced by constantly updating the weights and gradients iteratively. Training targets are also chosen and accurate labels are provided to the targets. When the SVM model is fully trained and configured, we can apply it to evaluate a new design pattern without using accurate lithography simulations.

 

ADVANTAGES: a)Faster than already available methods.

                           b)More accurate and efficient.

                           c)The problem of detecting false alarms is reduced considerably.


CONCLUSION: Thus one can conclude from the above points just how essential it is to employ machine learning techniques such as SVM in order to develop a highly powerful , fast and efficient model.

 

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