Dr. Puneet Gupta
at UCLA Samueli School of Engineering
SPIE Involvement:
Author | Instructor
Publications (49)

Proceedings Article | 8 March 2024 Presentation + Paper
Hangbo Yang, Nicola Pesericoa, Shurui Li, Benyamin Fallahi Motlagh, Jaskirat Singh Virdi, Puneet Gupta, Volker Sorger, Chee Wei Wong
Proceedings Volume 12890, 1289003 (2024) https://doi.org/10.1117/12.2692958
KEYWORDS: Convolution, Neural networks, Integrated photonics, Modulation, Waveguides, Backscatter, Fourier transforms, Design, Wavelength division multiplexing

Proceedings Article | 4 October 2023 Presentation + Paper
Proceedings Volume 12673, 1267307 (2023) https://doi.org/10.1117/12.2678666
KEYWORDS: Convolution, Silicon photonics, Neural networks, Receivers, Photodetectors, Microrings, Geometrical optics, Fourier transforms, Analog to digital converters, Wavelength division multiplexing

Proceedings Article | 17 March 2023 Presentation + Paper
Proceedings Volume 12424, 124240L (2023) https://doi.org/10.1117/12.2650228
KEYWORDS: Photonics, Neural networks, Silicon photonics, Lithium, Photodetectors, Machine learning, Design and modelling, Convolutional neural networks, Microrings, Artificial neural networks

Proceedings Article | 4 October 2022 Presentation
Puneet Gupta, Shurui Li
Proceedings Volume PC12204, PC1220404 (2022) https://doi.org/10.1117/12.2634039
KEYWORDS: Neural networks, Computing systems, Digital micromirror devices, Convolution, Fourier transforms, Photonics systems, Optical correlators, Optical computing, Micromirrors, Lenses

Proceedings Article | 3 October 2022 Presentation + Paper
Proceedings Volume 12204, 1220409 (2022) https://doi.org/10.1117/12.2633917
KEYWORDS: Diffraction, Convolution, Image processing, Data processing, Digital micromirror devices, Convolutional neural networks, Machine learning, Quantization, Electronics, Binary data

Showing 5 of 49 publications
Course Instructor
SC1101: Understanding Design-Patterning Interactions
This course explains how layout and circuit design interact with lithography choices. We especially focus on multi-patterning technologies such as LELE double patterning and SADP. We will explore role of design in lithography technology development as well as in lithographic process control. We will further discuss design enablement of multi-patterning technologies, especially in context of cell-based digital designs.
SC1187: Understanding Design-Patterning Interactions for EUV and DSA
EUV lithography and DSA haven been accepted by the industry as most promising candidates for dimensional scaling enablement at N7 technology node and beyond. This tutorial explains how introduction of such lithography technologies going to impact layout and circuit design. Choices of lithography would impact physical design and have a significant impact at system level. This tutorial will focus on transition from 193i multi-patterning technologies to EUV lithography and DSA. Factors that would determine on the enablement of these technologies would be highlighted and possible solutions would be shared.
SC708: Impact Of Variability On VLSI Circuits
Sub-90nm CMOS technologies are giving rise to significant variation in physical parameters of VLSI designs which has adverse impact on their electrical behavior. Most manufacturing-oriented professionals are familiar with the variations in physical parameters. This course will provide attendees with knowledge of how these physical variations impact the circuit operations, i.e., their electrical behavior. The impact on timing as well as power will be discussed. We will describe relative impact of these variations on various circuit families as well as circuit design techniques to mitigate the impact of manufacturing variations. Due to the large mangnitude of these variations, it is clear that designing for worst case behavior leaves significant performance on the table. We will discuss how systematic variation can be exploited in the current static timing methodology if it is known. A statistical timing and design methodology will also be discussed that can help regain some of this performance. With an eye towards the future, we will also explore manufacturing aware design closure. The course will be illustrated with practical examples throughout.
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