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Your search keyword '"Esmaeeli, Omid"' showing total 8 results
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8 results on '"Esmaeeli, Omid"'

1. GHz-rate optical phase shift in light matter interaction-engineered, silicon-ferroelectric nematic liquid crystals

Author

Taghavi, Iman, Esmaeeli, Omid, Chowdhury, Sheri Jahan, Mitchell, Matthew, Witt, Donald, Pecinovsky, Cory, Sickler, Jason, Jaeger, Nicolas A. F., Shekhar, Sudip, and Chrostowski, Lukas

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

Organic electro-optic materials have demonstrated promising performance in developing electro-optic phase shifters. Their integration with other silicon photonic processes, nanofabrication complexities, and durability remain to be developed. While the required poling step in electro-optic polymers limits their potential and utilization on a large scale, devices made of paraelectric nematic liquid crystals suffer from slow bandwidth. In ferroelectric nematic liquid crystals, we report an additional GHz-fast phase shift that ultimately allows for significant second-order nonlinear optical coefficients and the related Pockels effect. It avoids poling issues and can pave the way for hybrid silicon-organic systems with CMOS-foundry compatibility. We report DC and AC modulation efficiencies of $\approx$0.25 V$\cdot$mm and $\approx$25.7 V$\cdot$mm, respectively, an on-chip insertion loss of $\approx$2.1 dB, and an electro-optic bandwidth of $f_{-6dB}$>4.18 GHz, employing improved light-matter interaction in a waveguide architecture that calls for only one lithography step., Comment: 25 pages, 5 figures

Published
2024

2. 120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training

Author

Lin, Zhongjin, Shastri, Bhavin J., Yu, Shangxuan, Song, Jingxiang, Zhu, Yuntao, Safarnejadian, Arman, Cai, Wangning, Lin, Yanmei, Ke, Wei, Hammood, Mustafa, Wang, Tianye, Xu, Mengyue, Zheng, Zibo, Al-Qadasi, Mohammed, Esmaeeli, Omid, Rahim, Mohamed, Pakulski, Grzegorz, Schmid, Jens, Barrios, Pedro, Jiang, Weihong, Morison, Hugh, Mitchell, Matthew, Guan, Xun, Jaeger, Nicolas A. F., Rusch, Leslie A. n, Shekhar, Sudip, Shi, Wei, Yu, Siyuan, Cai, Xinlun, and Chrostowski, Lukas

Subjects
Physics - Optics, Computer Science - Emerging Technologies, Physics - Applied Physics, 78A05
Abstract

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 * 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers., Comment: 21 pages, 6 figures

Published
2023

3. 120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training

Author

Lin, Zhongjin, Shastri, Bhavin J., Yu, Shangxuan, Song, Jingxiang, Zhu, Yuntao, Safarnejadian, Arman, Cai, Wangning, Lin, Yanmei, Ke, Wei, Hammood, Mustafa, Wang, Tianye, Xu, Mengyue, Zheng, Zibo, Al-Qadasi, Mohammed, Esmaeeli, Omid, Rahim, Mohamed, Pakulski, Grzegorz, Schmid, Jens, Barrios, Pedro, Jiang, Weihong, Morison, Hugh, Mitchell, Matthew, Guan, Xun, Jaeger, Nicolas A. F., Rusch, Leslie A., Shekhar, Sudip, Shi, Wei, Yu, Siyuan, Cai, Xinlun, and Chrostowski, Lukas

Published
2024
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5. 65 GOPS/neuron Photonic Tensor Core with Thin-film Lithium Niobate Photonics

Author

Lin, Zhongjin, Shastri, Bhavin J., Yu, Shangxuan, Song, Jingxiang, Zhu, Yuntao, Safarnejadian, Arman, Cai, Wangning, Lin, Yanmei, Ke, Wei, Hammood, Mustafa, Wang, Tianye, Xu, Mengyue, Zheng, Zibo, Al-Qadasi, Mohammed, Esmaeeli, Omid, Rahim, Mohamed, Pakulski, Grzegorz, Schmid, Jens, Barrios, Pedro, Jiang, Weihong, Morison, Hugh, Mitchell, Matthew, Qiang, Xiaogang, Guan, Xun, Jaeger, Nicolas A. F., Rusch, Leslie A. n, Shekhar, Sudip, Shi, Wei, Yu, Siyuan, Cai, Xinlun, Chrostowski, Lukas, Lin, Zhongjin, Shastri, Bhavin J., Yu, Shangxuan, Song, Jingxiang, Zhu, Yuntao, Safarnejadian, Arman, Cai, Wangning, Lin, Yanmei, Ke, Wei, Hammood, Mustafa, Wang, Tianye, Xu, Mengyue, Zheng, Zibo, Al-Qadasi, Mohammed, Esmaeeli, Omid, Rahim, Mohamed, Pakulski, Grzegorz, Schmid, Jens, Barrios, Pedro, Jiang, Weihong, Morison, Hugh, Mitchell, Matthew, Qiang, Xiaogang, Guan, Xun, Jaeger, Nicolas A. F., Rusch, Leslie A. n, Shekhar, Sudip, Shi, Wei, Yu, Siyuan, Cai, Xinlun, and Chrostowski, Lukas

Abstract

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by providing low latency, high bandwidth, and energy-efficient computations. Here, we introduce a photonic tensor core processor enabled by time-multiplexed inputs and charge-integrated outputs. This fully integrated processor, comprising only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver, can implement an entire layer of a neural network. It can execute 65 billion operations per second (GOPS) per neuron, including simultaneous weight updates-a hitherto unachieved speed. Our processor stands out from conventional photonic processors, which have static weights set during training, as it supports fast "hardware-in-the-loop" training, and can dynamically adjust the inputs (fan-in) and outputs (fan-out) within a layer, thereby enhancing its versatility. Our processor can perform large-scale dot-product operations with vector dimensions up to 131,072. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112*112-pixel images after "hardware-in-the-loop" training. To handle "hardware-in-the-loop" training for clustering AI tasks, we provide a solution for multiplications involving two negative numbers based on our processor., Comment: 19 pages, 6 figures

Published
2023

6. Neuromorphic photonic circuit modeling in Verilog-A

Author

Singh, Jagmeet, primary, Morison, Hugh, additional, Guo, Zhimu, additional, Marquez, Bicky A., additional, Esmaeeli, Omid, additional, Prucnal, Paul R., additional, Chrostowski, Lukas, additional, Shekhar, Sudip, additional, and Shastri, Bhavin J., additional

Published
2022
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8. A Transformer-Based Technique to Improve Tuning Range and Phase Noise of a 20–28GHz LCVCO and a 51–62GHz Self-Mixing LCVCO.

Author

Esmaeeli, Omid, Lightbody, Sam, Shirazi, Amir Hossein Masnadi, Djahanshahi, Hormoz, Zavari, Rod, Mirabbasi, Shahriar, and Shekhar, Sudip

Subjects
*PHASE noise, *VOLTAGE-controlled oscillators, *COMPLEMENTARY metal oxide semiconductors, *VARACTORS, *ELECTRIC capacity, *REACTIVE power
Abstract

Varactors in RF CMOS processes often have significantly lower Q- factor ($Q_{VAR}$) than inductors and transformers at mm-wave frequencies. Direct connection of varactors to the outputs of an LC oscillator lowers overall tank Q, $Q_{T} < Q_{VAR}$ , and at the same time, increases the ratio of parasitic capacitance to total tank capacitance which limits frequency tuning range (FTR). Instead, magnetically coupling a varactor to the oscillator core using an asymmetric transformer, where the core is connected to the primary and varactor to the secondary, limits the drop in $Q_{T}$. The ratio of parasitic capacitance to total tank capacitance is also reduced. The varactor can also be operated in accumulation-mode, with a larger $Q_{VAR}$. Thus, both FTR and phase noise (PN), in comparison to traditional tanks in LC VCOs, are improved simultaneously. In this paper, two VCO prototypes are implemented in 65-nm CMOS. A 60 GHz self-mixing VCO with a VCO core operating at 20 GHz shows an FTR of 18.5%, a PN of −92.5 dBc/Hz at 1 MHz offset, and an FoM $_{T}$ of −187.1 dBc/Hz. A 25 GHz VCO shows an FTR of 29.9%, a PN of −107.9 dBc/Hz at 1 MHz offset, and an FoM $_{T}$ of −194.2 dBc/Hz. [ABSTRACT FROM AUTHOR]

Published
2022
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