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Recently, the iGaN Laboratory led by Professor Haiding Sun at the School of Microelectronics, University of Science and Technology of China (USTC), together with the team of academician Sheng Liu from Wuhan University, has successfully developed the world's first miniaturized ultraviolet (UV) spectrometer chip and realized on-chip spectral imaging.

Based on a novel gallium nitride (GaN) cascaded photodiode architecture and integrated with deep neural network (DNN) algorithms, the device achieves high-precision spectral detection and high-resolution multispectral imaging.

With a response speed on the nanosecond scale, it sets a new world record for the fastest reported miniaturized spectrometer. The work, titled "A miniaturized cascaded-diode-array spectral imager," was online in Nature Photonics on September 26, 2025.

Spectral imaging technology captures both spectral and spatial information simultaneously, enabling the precise measurement and identification of complex environments and targets. It has wide applications in material analysis, environmental monitoring, satellite remote sensing and deep-space exploration.

However, conventional spectral imagers rely on diffraction gratings and mechanical scanning, making them large, complex and costly, and limiting their portability and integration. In particular, for deep-UV/UV applications crucial in biopharmaceuticals and organic molecule detection, there has long been no viable miniaturized solution.

To overcome this challenge, the iGaN Laboratory proposed a new GaN-based cascaded photodiode architecture (fig. 1a) and developed the world's first UV spectral imaging chip. The structure consists of two vertically cascaded asymmetric p–n diodes that can be fabricated as arrays on 2-inch wafers and integrated through bonding (fig. 1 b,c).

By applying external bias, carrier transport exhibits wavelength-dependent behavior, enabling voltage-tunable bidirectional spectral response. With the aid of DNN algorithms (fig. 1d), the device reconstructs unknown spectra with high accuracy (fig. 2a–c). Operating across 250–365 nm, the chip achieves ~0.62 nm resolution and <10 ns response time is the fastest among all reported miniaturized spectrometers.

Using this chip, the research team successfully performed spatially resolved, single-shot imaging of organic liquid droplets, including olive oil, peanut oil, animal fat, and milk. Each pixel recorded wavelength-dependent photocurrent signals, generating a full three-dimensional dataset.

After spectral reconstruction via neural networks, high-resolution spectral images were obtained, clearly showing the distinct UV absorption characteristics (fig. 2d) and spatial distributions (fig. 2e) of different organic substances. These demonstrations highlight the chip's potential in molecular recognition and food safety testing.

This work presents and validates a new paradigm for miniaturized spectrometer chips and, for the first time, uses wide-bandgap nitride semiconductors as a spectral chip platform. Looking forward, by adjusting material composition and doping, or by introducing other compound semiconductors (such as CdS, ZnO, GaAs, and InP), the operational range of this chip architecture could be extended from UV to visible and even infrared.

Because the fabrication process is fully compatible with large-scale semiconductor manufacturing, the chip dimensions can be further reduced to submicron or nanometer scales, enabling higher resolution at lower cost.

The team anticipates that production costs could fall to as little as one-hundredth that of conventional spectrometers. Much like silicon-based CCD/CMOS technology enabled the widespread adoption of digital cameras, this GaN-based miniaturized spectrometer chip is poised to drive a new wave of industrial advancement in spectral imaging.

The co-first authors on this work are Dr. Huabin Yu (postdoc), Dr. Muhammad Hunain Memon (postdoc), Mr. Zhixiang Gao (Ph.D. student), and Mr. Mingjia Yao (master's student). Professor Haiding Sun of USTC is the sole corresponding author. Academician Sheng Liu (Wuhan University), Professor Zongyin Yang (Zhejiang University), and Professor Tawfique Hasan (University of Cambridge) provided important support and guidance.

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Dr. Muhammad Hunain Memon is currently a postdoctoral researcher at the School of Microelectronics, University of Science and Technology of China (USTC).