Scientists Develop Effective Microlasers as Small as a Speck of Dust

Researchers at HSE University–St Petersburg have discovered a way to create effective microlasers with diameters as small as 5 to 8 micrometres. They operate at room temperature, require no cooling, and can be integrated into microchips. The scientists relied on the whispering gallery effect to trap light and used buffer layers to reduce energy leakage and stress. This approach holds promise for integrating lasers into microchips, sensors, and quantum technologies. The study has been published in Technical Physics Letters.
The devices around us are becoming increasingly compact without sacrificing functionality. Smartphones now handle tasks that once required a computer, and small cameras can capture images with quality approaching that of professional equipment. Miniaturisation has also extended to lasers—sources of directed light that are embedded in optical chips, sensors, medical devices, and communication systems.
However, shrinking a laser while preserving its optical properties, efficiency, and reliability remains a significant challenge. Developing a laser measuring 5–8 micrometres—approximately the diameter of a red blood cell—requires complex calculations, and its fabrication demands high precision. The main challenge lies in the design of the laser itself. Unlike conventional light sources, lasers amplify radiation within a resonator—a structure where light is repeatedly reflected and amplified. The more compact the laser, the harder it is to trap the light inside so that it undergoes continuous reflection and amplification without losing energy, which is essential for stable operation.
Another challenge is the presence of defects in the material. Lasers rely on crystals that can amplify light, but microscopic defects often form during their growth, reducing the efficiency of light generation. To minimise these irregularities, scientists carefully select synthesis conditions and simulate the properties of crystals under various scenarios in advance. However, solving one problem often gives rise to others, turning laser development into a continual search for balance.
HSE scientists have developed microlasers with diameters as small as 5 to 8 micrometres that operate at room temperature. The researchers used a crystal structure composed of indium, gallium, nitrogen, and aluminium compounds grown on a silicon substrate. To trap light in a tiny space, the scientists relied on the whispering gallery effect.
Eduard Moiseev
'This phenomenon is well-known in acoustics: in some churches and cathedrals, you can whisper words against one wall, and the sound will be clearly heard on the opposite wall—even though, under normal conditions, the sound would not travel that far. A similar effect enables light to be repeatedly reflected inside the disk-shaped microlaser, minimising energy loss,' explains Eduard Moiseev, Senior Research Fellow at the International Laboratory of Quantum Optoelectronics, HSE University–St Petersburg.
However, even under these conditions, light waves can partially escape into the substrate and be lost. To prevent this, the researchers added a stepped buffer layer, which compensates for mechanical stresses between the silicon and nitride layers and reduces radiation leakage, enabling the laser to operate stably even at such small sizes.

Natalia Kryzhanovskaya
'Our microlasers operate stably at room temperature without the need for cooling systems, making them convenient for real-world applications. In the future, such devices will enable the creation of more compact and energy-efficient optoelectronic technologies,' explains Natalia Kryzhanovskaya, Head of the International Laboratory of Quantum Optoelectronics at HSE University–St Petersburg.
The paper has been prepared as part of a project implemented within the framework of the International Academic Cooperation competition at HSE University.
See also:
Neurolinguists Assist in Awake Surgery on 11-Year-Old Patient with Epilepsy
Researchers at the HSE Centre for Language and Brain took part in a rare awake neurosurgical procedure performed on an 11-year-old patient with drug-resistant epilepsy. Working alongside surgeons at the Voyno-Yasenetsky Centre of Specialised Medical Care for Children in Solntsevo, they monitored the resection of a portion of the left temporal lobe, where the epileptic focus had been identified.
Scientists Explain How Emotions Shape Attitudes Toward Digital Governance
Today, interactions between citizens and government increasingly take place through digital governance platforms, including digital public services, AI-powered systems, and algorithmic decision-making tools. Until now, however, these technologies have largely been viewed as technical instruments, with their effectiveness assessed primarily in terms of efficiency and user-friendliness. The authors of a new study propose a broader perspective, arguing that digital governance should also be understood as an emotional experience that directly shapes citizens' trust in public institutions.
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
‘Entering Robotics Now Means Growing with the Area’
Unmanned vehicles, courier robots, and smart speakers are rapidly becoming a part of our lives. In 2026, the HSE Faculty of Computer Science opens its new Bachelor’s Programme ‘Design of Intelligent Robotic Systems’ (DIRS). It will train specialists at the intersection of IT, artificial intelligence, and robotics. Academic Supervisor of DIRS Vadim Morgachev explains how studies are organised and why graduates of the programme ‘will definitely be accepted into the future.’
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
MIEM Tech Day at Pokrovka: Exploring HSE’s Engineering DNA Together
On May 26, 2026, the central atrium of the building at 11 Pokrovsky Bulvar will host the annual large-scale festival of engineering developments created by project teams from the HSE Tikhonov Moscow Institute of Electronics and Mathematics (HSE MIEM). The programme includes presentations of the best student technological projects, stands from partner companies and joint workshops, a lecture series featuring practising engineers, a round table on the development of engineering education, and presentations of MIEM master’s degree programmes.


