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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Undavalli, Aswin Chowdary Cauwenberghs, Gert Natarajan, Arun Chakrabartty, Shantanu Nagulu, Aravind Washington University in St. Louis Department of Electrical and Systems Engineering MO United States University of California at San Diego Department of Bioengineering CA United States School of Electrical Engineering and Computer Science Oregon State University CorvallisOR United States

ISBN: (纸本)9798350302103

Compute In Memory (CIM) has gained significant attention in recent years due to its potential to overcome the memory bottleneck in Von-Neumann computing architectures. While most CIM architectures use non-volatile memory elements in a NOR-based configuration, NAND-based configuration, and in particular, 3D-NAND flash memories are attractive because of their potential in achieving ultra-high memory density and ultra-low cost per bit storage. Unfortunately, the standard multiply-and-accumulate (MAC) CIM-paradigm can not be directly applied to NAND-flash memories. In this paper, we report a NAND-Flash-based CIM architecture by combining the conventional 3D-NAND flash with a Margin-Propagation (MP) based approximate computing technique. We show its application for implementing matrix-vector multipliers (MVMs) that do not require analog-to-digital converters (ADCs) for read-out. Using simulation results we show that this approach has the potential to provide a 100 x improvement in compute density, read speed, and computation efficiency compared to the current state-of-the-art. © 2023 IEEE.

关键词： Compute-In Memory Margin Propagation Matrix Vector Multipliers Multiply and Accumulate NAND Flash

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Crosstalk in polymer microelectrode arrays

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Nano Research 2021年第9期14卷 3240-3247页

作者： Yi Qiang Wen Gu Zehua Liu Shanchuan Liang Jae Hyeon Ryu Kyung Jin Seo Wentai Liu Hui Fang Department of Electrical and Computer Engineering Northeastern UniversityBostonMA 02115USA Department of Bioengineering University of CaliforniaLos AngelesLos AngelesCA 90095USA Department of Bioengineering Northeastern UniversityBostonMA 02115USA

Thin-film polymer microelectrcxje arrays(MEAs)facilitate the high-resolution neural recording with its superior mechanical ***,the densely packed electrodes and interconnects along with the ultra-thin polymeric encapsulation/substrate layers give rise to non-negligible crosstalk,which could result in severe interference in the neural signal *** to the lack of standardized characterization or modeling of crosstalk in neural electrode arrays,to date,crosstalk in polymer MEAs remains poorly *** this work,the crosstalk between two adjacent polymer microelectrodes is measured experimentally and modeled using equivalent ***,this study demonstrated a two-well measuring platform and systematically characterized the crosstalk in polymer microelectrodes with true isolation of the victim channel and precise control of its grounding condition.A simple,unified equation from detailed circuit modeling was proposed to calculate the crosstalk in different *** element analysis(FEA)analysis was conducted further to explore the crosstalk in more aggressively scaled polymer electrode *** addition to standardizing neural electrode array crosstalk characterization,this study not only reveals the dependence of the crosstalk in polymer MEAs on a variety of key device parameters but also provides general guidelines for the design of thin polymer MEAs for high-quality neural signal recording.

关键词： crosstalk effect flexible microelectrodes equivalent circuit modelling finite element analysis polymer

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Erratum to “Edge Selections in Bilinear Dynamic Networks”

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IEEE Transactions on Automatic Control 2024年第1期70卷 705-706页

作者： Arthur Castello B. de Oliveira Milad Siami Eduardo D. Sontag Department of Electrical and Computer Engineering Northeastern University Boston MA USA Department of Bioengineering Northeastern University Boston MA USA

Lemma 2 of “Edge Selections in Bilinear Dynamical Networks” (Oliveira et al., 2024) allows one to efficiently compute a lower bound for the optimal $\mathcal {H}_{2}$ -norm of a bilinear dynamical network following optimal edge selection, by showing convexity of a relaxed version of the problem. However, the proof presented is wrong in general, leaving the statement unproven. In this note, we discuss a case in which the presented result is guaranteed to hold and update our experimental results in light of this fact. Despite this problem with an auxiliary result in the article, notice that the main result in Theorem 1 remains correct, proving supermodularity of the $\mathcal {H}_{2}$ -norm under edge addition.

关键词： Symmetric matrices Simulation Optimization Noise

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Wavelength-multiplexed multiplane quantitative phase imaging using a diffractive optical processor 11

Wavelength-multiplexed multiplane quantitative phase imaging...

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Quantitative Phase Imaging XI 2025

作者： Shen, Che-Yung Li, Jingxi Gan, Tianyi Li, Yuhang Bai, Langxing Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States

ISBN: (纸本)9781510684065

We present deep learning-designed all-optical processors that can perform multiplane quantitative phase imaging (QPI). By leveraging diffractive processing and wavelength multiplexing, our approach allows the direct capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor, eliminating the need for digital phase recovery algorithms. A proof-of-concept experiment validated the approach, showcasing successful imaging of distinct phase objects at different axial positions in the terahertz spectrum. By providing a faster, more efficient method for QPI, this technology can be applied to medical diagnosis, material science, and environmental monitoring, among other applications. © 2025 SPIE.

关键词： Phase imaging Biological imaging Image processing Biomedical optics Imaging devices Multiplexing Stereoscopy

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All-Optical Phase Conjugation Using a Diffractive Visual Processor

All-Optical Phase Conjugation Using a Diffractive Visual Pro...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Shen, Che-Yung Li, Jingxi Gan, Tianyi Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

We present a diffractive visual processor that can approximate optical phase conjugation operation by linear optical processing without any digital computing or external power sources, which can be used for turbidity ... 详细信息

关键词： Optical phase conjugation

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Unidirectional image magnification and demagnification using pyramid diffractive optical networks

Unidirectional image magnification and demagnification using...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Bai, Bijie Yang, Xilin Gan, Tianyi Li, Jingxi Mengu, Deniz Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

ISBN: (纸本)9781957171395

We introduce a pyramid-structured deep diffractive optical network (P-D2NN) design that performs unidirectional image magnification and demagnification using reduced degrees of freedom. Experimental validation at terahertz spectrum demonstrates the efficacy of this design strategy. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Optical fibers

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All-Optical Phase Conjugation Using a Diffractive Visual Processor

All-Optical Phase Conjugation Using a Diffractive Visual Pro...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Adaptive optics

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Unidirectional image magnification and demagnification using pyramid diffractive optical networks

Unidirectional image magnification and demagnification using...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Diffractive optics

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All-Optical Complex Field Imager

All-Optical Complex Field Imager

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2024 Frontiers in Optics, FiO 2024

作者： Li, Yuhang Li, Jingxi Gan, Tianyi Shen, Che-Yung Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

We present a diffractive complex field imager that uses an intensity-based optoelectronic sensor array to directly capture both the amplitude and phase images of input fields in a snapshot without digital processing. ... 详细信息

关键词： Optoelectronic devices

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Fourier Imager Network(FIN):A deep neural network for hologram reconstruction with superior external generalization

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Light(Science & Applications) 2022年第9期11卷 2225-2234页

作者： HANLONG CHEN LUZHE HUANG TAIRAN LIU AYDOGAN OZCAN Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USAElectrical and Computer Engineering DepartmentUniversity of CaliforniaLos AngelesCA 90095USA David Geffen School of Medicine University of California Los AngelesLos AngelesCA 90095USA

Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic ***,the generalization of their image reconstruction performance to new types of samples never seen by the network remains a *** we introduce a deep learning framework,termed Fourier Imager Network(FIN),that can perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples,exhibiting unprecedented success in external *** architecture is based on spatial Fourier transform modules that process the spatial frequencies of its inputs using learnable filters and a global receptive *** with existing convolutional deep neural networks used for hologram reconstruction,FIN exhibits superior generalization to new types of samples,while also being much faster in its image inference speed,completing the hologram reconstruction task in~0.04 s per 1 mm^(2) of the sample *** experimentally validated the performance of FIN by training it using human lung tissue samples and blindly testing it on human prostate,salivary gland tissue and Pap smear samples,proving its superior external generalization and image reconstruction *** holographic microscopy and quantitative phase imaging,FIN and the underlying neural network architecture might open up various new opportunities to design broadly generalizable deep learning models in computational imaging and machine vision fields.

关键词： field. generalization holographic

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