Mr. Pardeep Kumar Profile

Pardeep Kumar

at IIT Gandhinagar

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Publications (4)

SPIE Journal Paper | 14 May 2019

Random forest-based robust classification for lithographic hotspot detection

Rohit Dawar, Samit Barai, Pardeep Kumar, Babji Srinivasan, Nihar Mohapatra

JM3, Vol. 18, Issue 02, 023501, (May 2019) https://doi.org/10.1117/12.10.1117/1.JMM.18.2.023501

KEYWORDS: Lithography, Optical proximity correction, Image processing, Feature extraction, Machine learning, Photomasks, Data modeling, Image classification, Calibration, Binary data

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With continuous downscaling of feature sizes, potentially problematic patterns (hotspots) have become a major issue in generation of optimized mask design for better printability. The lithography process sensitive patterns in a design lead to degradation of both electrical performance and manufacturing yield of the integrated circuit. Due to sequential flow of very large-scale integration (VLSI) design and manufacturing, missing any hotspot has an adverse impact on product turnaround time and cost. The lithographic samples are generally defined using a combination of continuous variables (to represent aerial image and pattern density) and categorical variables (to represent allowed layout design rules). The conventional hotspot classification techniques suffer from suboptimum performance due to their inability to efficiently represent and use the above-mentioned feature metrics. In general, the number of hotspots in the lithographic data is much less compared to the total number of patterns in a full-chip design. It makes the input data imbalanced and adds additional difficulties in the decision making processes. We present a robust technique to detect the process sensitive patterns using random forest-based machine learning technique. The emphasis is put on the layout features extraction techniques to improve the performance of the proposed approach. The simulation results show that the patterns susceptible to variations under different dose and focus conditions undergo a drastic change in their aerial image characteristics even when the geometry is varied by a very small margin. We observed from our analysis that the minimum number of false negatives can be achieved with reasonable increase in the number false positives. Moreover, compared to conventional hotspot classification techniques, we are able to achieve a very low percentage of false negatives with a binary classifier trained on an imbalanced dataset. Another key observation from our analysis is that the random forest method can obtain the most representative heuristics required to define categories from the lithographic datasets with continuous and categorical variables. In addition, our proposed approach can easily be integrated with commercially available electronic design automation tools and in-house design simulators to make the process flow viable in terms of a business perspective.

SPIE Journal Paper | 13 October 2018

Sample plan selection techniques for lithography process model building

Pardeep Kumar, Babji Srinivasan, Nihar R. Mohapatra

JM3, Vol. 17, Issue 04, 043501, (October 2018) https://doi.org/10.1117/12.10.1117/1.JMM.17.4.043501

KEYWORDS: Statistical modeling, Process modeling, Data modeling, Lithography, Photomasks, Image processing, Calibration, Performance modeling, Error analysis, Principal component analysis

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SPIE Journal Paper | 28 April 2018

Multiple stages of regression to improve accuracy in calibrated lithography process models

Pardeep Kumar, Alan Rosenbluth, Ramana Murthy Pusuluri, Ramya Viswanathan, Babji Srinivasan, Nihar Mohapatra

JM3, Vol. 17, Issue 02, 023503, (April 2018) https://doi.org/10.1117/12.10.1117/1.JMM.17.2.023503

KEYWORDS: Calibration, Data modeling, Process modeling, Autoregressive models, Lithography, Photomasks, Statistical modeling, Monte Carlo methods, Photoresist processing, Optical proximity correction

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A two-stage approach is introduced to improve the accuracy of compact patterning models used in large-scale computational lithography. Each of the two stages uses a separate empirically calibrated regression model whose accuracy at predicting printed feature dimensions has been proven in the usual standalone (single stage) mode. For the first model stage, we choose an established regularized regression model of the kind that accounts for resist non-idealities by suitably modifying the pre-thresholded exposing dose pattern, with the model basis functions taking the form of modified convolutions of adjustable kernels with the optical image. A different class of regression model is used in the second stage, namely a model that accounts for resist non-idealities by making a pattern-dependent local adjustment in the develop threshold, with the model basis functions being characteristic traits of the image trace along feature cutlines. However, rather than applying this second model in the usual mode where it adjusts the develop threshold applied to the exposing optical image, we use it to adjust a threshold that is applied to the improved effective dose distribution provided by the first-stage model. The effectiveness of the proposed method is verified by modeling pattern transfer of critical layers in 14- and 22-nm complementary metal–oxide–semiconductor (CMOS) technology. In our experience, little accuracy improvement is gained by expanding the complexity of standard single-stage models beyond the level of empirically proven model forms. However, even in a basic implementation, inclusion of a second stage of modeling will in itself reduce RMS error by ∼45 % in our 14-nm example. Moreover, accuracy improvement is further boosted to ∼55 % by adopting a minimax strategy in which the model is conservatively regularized according to the worst-case outcome in cross-validation tests but is calibrated according to the best-case outcome.

SPIE Journal Paper | 18 May 2015

Fast and accurate lithography simulation using cluster analysis in resist model building

Pardeep Kumar, Babji Srinivasan, Nihar Mohapatra

JM3, Vol. 14, Issue 02, 023506, (May 2015) https://doi.org/10.1117/12.10.1117/1.JMM.14.2.023506

KEYWORDS: Lithography, Statistical modeling, Optical proximity correction, Process modeling, Calibration, Data modeling, Photomasks, Performance modeling, Computer simulations, CMOS technology

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