Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Semiconductors

Semiconductors

There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Ampere-Maxwell's Law: Problem-Solving

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...

Signal and System

Signal and System

A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...

Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Single-Mode Capability Enhancement of Curved Sapphire Fiber Utilizing High-Order Mode Suppression Characteristics Applied at High Temperature.

Micromachines·2026

Same author

An efficient mid-infrared computational spectrometer based on synergistic microcavity-coupled photonic crystal waveguides.

Nature communications·2026

Same author

Soft tactile chip with in-situ sensing for haptic rendering and reverse feedback enhanced gross to fine teleoperation.

Nature communications·2026

Same author

Nonvolatile photonic field-programmable coupler array.

Science advances·2026

Same author

Biomimetic hairy affective-touch sensory AI interface.

Nature communications·2026

Same author

Artificial Intelligence-Integrated Overcoupled Resonator for Multifunctional Pesticide Spectral Classification and Quantification.

ACS nano·2026

Same journal

Learning Moisture-Induced Damage From Vision: Diffusion Models for Real-Time Monitoring of Additive Manufacturing Processes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Intrinsic Dual-Phase Regulated GeSe<sub>2</sub> Nanoparticles Triggered by Ball-Milling Treatment for Photonic Multi-Valued Logic Circuits.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

A Plant Photoregulator-Inspired S-Type Heterojunction System for Diabetic Keratopathy via Tri-Modal Light-Driven Immunometabolic Reprogramming, Tissue Repair, and Antibacterial Activity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

eEF1G Orchestrates Translation to Ensure Meiotic Progression in Transcriptionally Quiescent Spermatocytes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Ultrasound-Recharged Sub-Nanometer Palladium Catalysts for on-Demand and Self-Terminating Bioorthogonal Prodrug Activation in Cancer Therapy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Graphene Aerogels With Spherical Pore Structure for Broad Frequency Regulation and Enhanced Low-Frequency Response.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 9, 2025

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.

Zian Xiao^1,2,3, Zhihao Ren^1,2, Yangyang Zhuge^1,2

¹Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|October 29, 2024

Summary

This summary is machine-generated.

This study introduces an in-sensor computing system using silicon photonics for multimodal spectroscopic sensing. It significantly reduces data transfer costs by processing data directly on the photonic chip, achieving high classification accuracy.

Keywords:

in‐sensor computing multimodal sensor photonic convolutional process silicon photonics

More Related Videos

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

Related Experiment Videos

Last Updated: Jun 9, 2025

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

Area of Science:

Photonics
Spectroscopy
Integrated Circuits

Background:

Multimodal spectroscopic sensory systems generate large, complex data volumes.
High communication bandwidth and power consumption are challenges for data transfer.
In-sensor computing offers a solution to reduce data processing costs.

Purpose of the Study:

To develop a photonic multimodal in-sensor computing system.
To integrate photonic sensors with a silicon photonic convolutional processor.
To demonstrate in situ processing of spectroscopic sensory data.

Main Methods:

Utilized a microring resonator crossbar array as a photonic processor.
Implemented convolutional operations with 5-bit accuracy for image edge detection.
Integrated the processor with a photonic spectroscopic sensor for multimodal data analysis.

Main Results:

Achieved 5-bit accuracy in convolutional operations via image edge detection.
Demonstrated in situ processing of multimodal spectroscopic data.
Attained 97.58% classification accuracy across 45 classes for protein species detection.

Conclusions:

The developed system enables in-sensor computing for photonic multimodal spectroscopic sensors.
Integration of photonic processors and sensors enhances edge data processing capabilities.
This approach significantly reduces communication costs associated with sensory data transfer.