Projects - M.Tech
1) Lithography Hotspot Detection
In the process of IC design, lithography can be defined as the process of reprinting the pattern of mask on Silicon wafer. Lithography is an important step in this process as it enables feature size to decrease which further helps in decreasing device size. This continuous decrease in feature size may lead to printability issues and hence hotspots. Presence of hotspots can cause the circuit to fail completely, so it is very important to detect these hotspots with high accuracy. Previously various simulation, machine leaning and deep learning based techniques have been implemented to solve this issue.
In this work, a method to identify hotspots using Vision Transformers is proposed. Along with this, other deep learning techniques such as CNNs and ANNs have also been used for comparison purposes.
All three techniques are implemented on five datasets. ViT gives an overall average accuracy of 98.05% which is 1.39% higher than accuracy of CNNs and 2.04% higher than accuracy given by ANNs. Although the ViTs prove the best in terms of overall accuracy, but at dataset level its performance can be improved. Three out of five datasets have accuracy higher than 99% and for rest two it is slightly above 95%. In future, authors wish to improve accuracy for these two datasets by improving the model and reducing imbalance in the datasets.
2) Empirical Laws of Natural Language Processing for Neural Language Generated Text
In the domain of Natural Language Generation and Processing, a lot of work is being done for text generation. As the machines become able to understand the text and language, it leads to a significant reduction in human involvement. Many sequence models show great work in generating human like text, but the amount of research work done to check the extent up to which their results match the man-made texts are limited in number. In this paper, the text is generated using Long Short Term Memory networks (LSTMs) and Generative Pretrained Transformer-2 (GPT-2). The text by neural language models based on LSTMs and GPT-2 follows Zipf’s law and Heap’s law, two statistical representations followed by every natural language generated text. One of the main findings is about the influence of parameter Temperature on the text produced. The LSTM generated text improves as the value of Temperature increases. The comparison between GPT-2 and LSTM generated text also shows that text generated using GPT-2 is more similar to natural text than that generated by LSTMs.
3) Analysis and Classification of Bikeshare Data
In this project, we compared bike-share system usage between three major cities in the USA by analyzing various descriptive statistics such as popular times of travel, popular stations and trips, most common start and end station, total travel time, average travel time etc. using Python. We optimized different supervised learners (SVM, Gaussian NB, Random Forest and Decision Tree) for predicting whether a given person will buy subscription or not and obtained highest accuracy (.83) and F1 score (.78) using SVM.