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Miniature Sensor Detects Spectral Signature via Optoelectronic Interface

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ESPOO, Finland, Jan. 30, 2025 — A team at Aalto University combined miniaturized hardware with intelligent algorithms to create a microscopic spectral sensor that can accurately identify a myriad of materials. The work of the researchers could make it possible for industries like health care, food safety, and transportation to implement miniaturized spectroscopy, using everyday devices, for various applications.

A high-performance, miniaturized spectral sensor that fits inside a smartphone or wearable device could be used, for example, to monitor changes in an individual’s health, detect counterfeit drugs, or identify spoiled food. Autonomous vehicles could use the sensor for accurate, cost-effective object identification.

Spectral sensing, which identifies the composition of materials by analyzing how they interact with light, traditionally has required bulky, expensive systems available only in laboratories and industrial applications. Unlike traditional spectral sensors that require large optical components like prisms or gratings, the miniaturized sensor achieves spectral differentiation through its electrical responses to light. This makes it ideal for integration into small devices.
Researchers at Aalto University hold a tiny chip, designed to accommodate hundreds of ultracompact spectral sensors. Courtesy of Aalto University/Faisal Ahmed and Andreas Liapis.
Researchers at Aalto University hold a tiny chip, designed to accommodate hundreds of ultracompact spectral sensors. Courtesy of Aalto University/Faisal Ahmed and Andreas Liapis.

The miniaturized sensing system uses an electrically tunable, compact optoelectronic interface to enable accurate spectral identification. The optoelectronic interface allows precise control of electrical flow through voltage adjustments. The interface leverages both bias-voltage and gate-voltage tunability.

The exceptional tunability of the optoelectronic interface enables the sensor to interact with light in many ways. The interface is combined with advanced algorithms that allow it to generate distinguishable photoresponses from various input spectra.

During the sensor’s training, the researchers exposed the device to a wide range of light colors. The device learned how to generate unique electrical fingerprints for each color. Using an intelligent algorithm, the device can decode these electrical fingerprints, enabling the sensor to accurately identify materials and analyze their properties based on how the material interacts with light.

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“Our device is trained to recognize complex light signatures that are imperceptible to the human eye, achieving a level of precision comparable to the bulky sensors typically found in laboratories,” professor Zhipei Sun said.

The sensor enables accurate spectral identification for both narrow-band and broadband complex spectra, within a tiny device footprint of 5 μm by 5 μm. In demonstrations, the sensor achieved a peak wavelength identification accuracy of approximately 0.19 nm in free space and exhibited an accuracy of approximately 2.45 nm in on-chip integrated spectral sensing.

The spectral sensing and identification process used by the device could facilitate numerous applications beyond material identification, such as composition analysis through photoluminescence peak sensing and computing through encoding input spectra. The researchers demonstrated the capability of the sensor to identify a range of materials, including organic dyes, metals, semiconductors, and dielectrics, directly from their luminescence.

“Our innovative spectral sensing approach simplifies challenges in material identification and composition analysis,” researcher Xiaoqi Cui said.

The new sensor provides high-performance, cost-effective, miniaturized optical spectroscopy for both free-space and on-chip applications. The sensor’s configuration is universal and applicable to various semiconductors, allowing for flexible device design, compatible fabrication, and mass production for large-scale applications. With its versatility, tunability, and ability to recognize thousands of colors, the researchers anticipate that the microscopic sensor could bring the power of advanced spectroscopy to smart and wearable and handheld devices — the types of devices that are used every day.

“This work is a major step forward in bringing spectroscopic identification to everyone’s fingertips,” researcher Fedor Nigmatulin said. “By integrating this ultracompact hardware with intelligent algorithms, we’ve taken a significant step toward miniature, portable spectrometers that could one day transform consumer electronics.”

The research was published in Science Advances (www.doi.org/10.1126/sciadv.ado6886).

Published: January 2025
Glossary
integrated photonics
Integrated photonics is a field of study and technology that involves the integration of optical components, such as lasers, modulators, detectors, and waveguides, on a single chip or substrate. The goal of integrated photonics is to miniaturize and consolidate optical elements in a manner similar to the integration of electronic components on a microchip in traditional integrated circuits. Key aspects of integrated photonics include: Miniaturization: Integrated photonics aims to...
optoelectronics
Optoelectronics is a branch of electronics that focuses on the study and application of devices and systems that use light and its interactions with different materials. The term "optoelectronics" is a combination of "optics" and "electronics," reflecting the interdisciplinary nature of this field. Optoelectronic devices convert electrical signals into optical signals or vice versa, making them crucial in various technologies. Some key components and applications of optoelectronics include: ...
machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience or training. Instead of being explicitly programmed to perform a task, a machine learning system learns from data and examples. The primary goal of machine learning is to develop models that can generalize patterns from data and make predictions or decisions without being...
spectrometry
The study and measurement of spectra and their components.
Research & TechnologyeducationEuropeAalto Universityintegrated photonicsLight SourcesMaterialsOpticsoptoelectronicsSensors & Detectorsspectroscopymachine learningsmart cameraswearable devicesautomotiveBiophotonicsConsumerindustrialmedicalpharmaceuticalsemiconductorsspectrometry

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