A sharp bend, a junction or a sudden change in a circuit’s structure can weaken an electromagnetic signal. When communication systems operate at increasingly high frequencies, even these seemingly small disruptions become significant engineering obstacles. For researchers working towards sixth-generation (6G) wireless technology, finding ways to guide signals through compact circuits without losing their strength is a growing challenge.

A research team in India has demonstrated an approach that could help address this problem. Using a specially engineered silicon chip, scientists at the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Bengaluru, have shown that terahertz waves can cross artificial barriers with very little signal loss, even while carrying high-speed data.

The work draws on a phenomenon called photonic Klein tunnelling, which describes how waves can pass through certain barriers without undergoing the substantial reflection or attenuation commonly associated with such obstacles.

In one of the three devices developed by the researchers, data transmission reached 100 gigabits per second (Gbps). The findings, published in Advanced Materials, offer a potential design approach for the compact terahertz circuits that future communication technologies may require.

Borrowing a Principle From Quantum Physics

The idea behind the research has its origins in a phenomenon associated with quantum mechanics. In quantum physics, Klein tunnelling describes the unusual ability of certain particles to pass through a potential barrier even when classical physics would predict that they should be reflected. Researchers have explored analogous behaviour in photonic systems, where electromagnetic waves can exhibit similar transmission characteristics.

The JNCASR team applied this principle through topological photonics, a field concerned with controlling the movement of electromagnetic waves using specially designed structures. They created a silicon photonic crystal resembling the honeycomb lattice of graphene, with triangular holes arranged in a precise pattern across a silicon slab. This arrangement produces boundary channels along which terahertz waves can propagate. These channels are resistant to certain forms of backscattering, helping signals retain their direction of travel.

The researchers then introduced artificial obstacles into these pathways by changing the dimensions of the triangular holes over a short section of the crystal. The experiment tested whether the waves could retain their transmission characteristics even when their pathways were deliberately disrupted.

When a Barrier Barely Interrupts the Signal

The results showed that the engineered structures could transmit terahertz waves across the barriers with remarkably little loss, provided the strength of the barrier remained below a characteristic coupling threshold of the crystal. A single barrier produced only about 1–2% signal loss. Even with two barriers placed consecutively, the loss remained around 2–3%. For comparison, a similar barrier in a conventional, non-topological waveguide caused a considerably greater reduction in transmission.

This is significant for circuit design because communication signals must often navigate multiple pathways and changes in geometry within a limited physical space. Reducing the losses associated with these obstacles could give researchers greater flexibility when designing compact terahertz components.

The team demonstrated the principle in three silicon devices. One incorporated two barriers in sequence. Another used an omega-shaped pathway containing four sharp bends and a central barrier. The third was a power splitter, which divided an incoming signal into two branches, with one branch containing an engineered barrier.

The devices were fabricated using high-resistivity silicon, a material compatible with established semiconductor manufacturing processes. Measurements across frequencies of 310–360 GHz closely matched the researchers’ computer simulations. In the power splitter, the two output signals remained nearly identical, indicating that the barrier had little effect on signal transmission.

What does 100 Gbps Mean in Practice?

The researchers also examined whether the structures could carry high-speed data, rather than simply transmit terahertz waves. The double-barrier device achieved a data rate of 48 Gbps, while the omega-shaped configuration supported 60 Gbps. The power splitter recorded the highest rate, reaching 100 Gbps using QAM-32 modulation. This technique encodes information through variations in the amplitude and phase of a signal.

At 100 Gbps, a five-gigabyte high-definition film could theoretically be transferred in less than half a second. The different transmission rates are important because they show how the approach performed across three distinct circuit configurations. The 100 Gbps result was achieved by the power splitter, rather than all three devices. The demonstration suggests that engineered barriers need not substantially compromise data transmission, even in circuits incorporating multiple pathways and sharp bends.

From a Laboratory Demonstration to Future 6G Hardware

Terahertz frequencies, which lie between microwaves and infrared light in the electromagnetic spectrum, are attracting interest for their capacity to support high-bandwidth communication. Their potential lies in accommodating large volumes of data at speeds that conventional wireless technologies may struggle to sustain. However, generating and transmitting terahertz signals is only part of the challenge. Researchers must also develop components capable of directing, dividing and processing these signals within compact devices.

The JNCASR study addresses this less visible aspect of future communication infrastructure. Its findings could help in developing integrated terahertz circuits that route and split signals on a single chip without substantial transmission losses. The next step for the research team is to explore whether the engineered barriers can be adjusted electrically, thermally or mechanically. This could eventually allow a device to redirect signals between different pathways on demand, potentially enabling more adaptable terahertz circuits.

The work remains an experimental demonstration of signal transmission at the chip level; it does not establish a commercially deployable 6G system. Further research will be needed to integrate these components into larger communication architectures.

Led by Abhishek Kumar, Assistant Professor at JNCASR and head of its Meta Optics Laboratory (MetoLab), the study involved joint first authors Samina Mulla and Sangram Kumar Sadhu. Researchers from JNCASR, IISER Kolkata, IIT Bombay, the Institute of Microelectronics at A*STAR, Singapore, and the University of Lille and CNRS, France, also contributed. The research offers a new way of thinking about how high-frequency signals can be guided through increasingly intricate circuits. As work on 6G progresses, such advances in the underlying hardware will help determine how much of the promised speed and bandwidth can eventually be translated into practical communication systems.

India has released three indigenous artificial intelligence models under the IndiaAI Mission, with applications ranging from multilingual reasoning and coding to offline AI assistance in sensitive sectors such as defence, government and education. The models — EKA-1, developed by Soket Labs Technology and Research Private Limited, and Dhīr 2B and Dhīr 5B, developed by ZenteiQ AiTech Innovations, were unveiled in New Delhi under the IndiaAI Innovation Centre (Foundation Models) pillar.

The three models have different technical capabilities and deployment priorities. EKA-1 is designed as a large multilingual foundation model supporting all 22 scheduled Indian languages and English. The Dhīr models, meanwhile, focus on lightweight, locally deployable AI assistance, allowing organisations to process information without depending on continuous cloud connectivity.

EKA-1 Trained on 7.5 Trillion Tokens

Developed by Soket Labs, EKA-1 is a 26.3-billion-parameter foundation model trained on approximately 7.5 trillion tokens. It supports all 22 scheduled Indian languages alongside English and is designed for mathematical tasks, coding, reasoning and agentic applications.

The model uses an open, sparse hybrid architecture. Its intended applications include banking, financial services and cybersecurity, where understanding India’s digital ecosystem and sector-specific requirements is important. According to Abhishek Upperwal, CEO of Soket Labs, the model was developed with support from the IndiaAI Mission to address requirements within India’s banking, financial services and cybersecurity sectors.

Dhīr Models Target Offline and Secure AI Applications

ZenteiQ has introduced two open-source models, Dhīr 2B and Dhīr 5B, supporting English and 10 Indian languages. Their primary focus is secure deployment within organisations. The models can operate on-premise, enabling institutions to use AI systems within their own computing infrastructure rather than relying entirely on external cloud services.

They support document-grounded automation, knowledge retrieval and citizen-facing or enterprise services. The press release identifies defence, government, industrial and educational institutions as potential application areas, particularly in environments with limited internet connectivity or sensitive information.

Their lightweight architecture also allows deployment on mobile devices, according to ZenteiQ. The company says the models can run across different computing platforms, including graphics processing units (GPUs) and tensor processing units (TPUs). Prof. Sashikumar, CEO of ZenteiQ AiTech Innovations, said local deployment would allow users to retain data on their devices, improving privacy and security while reducing dependence on cloud connectivity.

Public Computing Infrastructure Supports Model Development

Both development teams were selected under the IndiaAI Innovation Centre pillar, which aims to build indigenous foundation models and large language models. The IndiaAI Mission provided access to subsidised national AI computing infrastructure and support for associated research expenses. These resources helped the selected teams develop their models.

Speaking at the launch, S. Krishnan, Secretary of the Ministry of Electronics and Information Technology, said indigenous AI models could strengthen India’s technological and strategic autonomy while creating opportunities to improve productivity across government, industry, research and education. He also pointed to AIKosh, the IndiaAI datasets platform, as a channel through which researchers, students, innovators and the wider public could access these resources and experiment with AI applications.

India’s Wider AI Development Programme

Approved by the Union Cabinet on March 7, 2024, the IndiaAI Mission operates through seven pillars: computing capacity, the Innovation Centre, the AIKosh datasets platform, application development, future skills, startup financing, and safe and trusted AI. The development of EKA-1 and the Dhīr models falls under the Innovation Centre pillar, which supports the creation of foundation models tailored to India’s requirements.

The models represent two different approaches to expanding domestic AI capabilities: large-scale multilingual processing and lightweight AI systems designed for secure local deployment. Their practical impact will depend on how effectively they perform across real-world applications and how widely organisations adopt them.

Understanding how the human brain processes information requires scientists to observe the activity of individual neurons and the connections between them. However, recording electrical signals from large numbers of these cells simultaneously has remained a technical challenge.

A technology called Neuropixels is helping researchers overcome this limitation. Developed initially for neuroscience experiments in animals, these extremely thin silicon probes can record electrical activity from hundreds of neurons simultaneously. Their use is now expanding into human research, where they could help scientists investigate brain functions at a level of detail that was previously difficult to achieve.

How Do Neuropixels Probes Work?

Neurons communicate through electrical signals. When a neuron becomes active, it produces a brief electrical impulse known as an action potential. Recording these impulses allows researchers to study when individual neurons fire and how their activity relates to other cells.

Traditional electrode systems had limitations in the number of neurons they could monitor simultaneously. Researchers commonly used electrodes that recorded from a few dozen channels, making it difficult to study activity across several brain regions at once.

Neuropixels uses a thin silicon probe containing hundreds of closely spaced recording sites. These sites detect electrical signals from nearby neurons, while integrated electronics amplify and convert the signals into digital data that researchers can analyse. The first Neuropixels 1.0 probe, developed through a collaboration between researchers at the Howard Hughes Medical Institute’s Janelia Research Campus and Belgium’s Interuniversity Microelectronics Centre (Imec), was reported in 2017.

It contained 960 recording sites along a hair-thin silicon shaft, with 384 channels capable of reading signals simultaneously. In early experiments, researchers inserted two probes into a mouse brain and recorded activity from approximately 700 neurons across five brain regions. Neuropixels 2.0 followed in 2021. Its four-shank version contains 5,120 recording sites, of which 1,536 can be read out simultaneously. The smaller and lighter design also supports longer-term recordings in small animals.

What are the Possibilities of the Technology?

Neuropixels has helped researchers study how different regions of the brain coordinate during sensory processing, movement and decision-making. Earlier experiments often examined relatively small groups of neurons. Recording larger populations has revealed that some brain functions are distributed across several regions rather than confined to a single location.

The technology has also helped scientists investigate neural manifolds, mathematical patterns formed by the collective activity of populations of neurons. Studying these patterns can provide insights into how the brain represents information and organises its activity. One major effort involved the International Brain Laboratory, a collaboration of 12 research laboratories in the United States and Europe.

In a 2025 study, researchers recorded activity from more than 620,000 neurons across 279 brain regions in 139 mice. The resulting dataset provided a large-scale map of neural activity associated with functions including vision, movement and decision-making. Neuropixels is now being used in more than 1,700 laboratories worldwide and has been applied to over two dozen species, including zebra finches, bearded dragons and octopuses.

To Human Brain Research

One of the most significant developments is the technology’s entry into human neuroscience. Researchers at the National Hospital for Neurology and Neurosurgery in London have been working with neurosurgeons to use Neuropixels probes during brain operations.

In one procedure described by Nature, a patient undergoing surgery for normal pressure hydrocephalus, a condition involving excess cerebrospinal fluid inside the skull, agreed to participate in a research experiment.

During the operation, researchers briefly inserted a probe into her brain to record neuronal activity. They played sequences of familiar and unfamiliar word combinations to examine how the brain responded to changes in auditory stimuli.

The recordings allowed researchers to observe electrical responses from individual neurons as the patient received the sounds. Human brain research has traditionally relied on techniques such as electrocorticography, which measures electrical activity through electrodes placed on the brain’s surface. Other devices can record activity at the single-neuron level, but generally monitor fewer cells.

Neuropixels offers a more detailed approach by allowing researchers to examine the activity of large numbers of individual neurons simultaneously. The technique is particularly valuable because some existing imaging methods, including calcium imaging, cannot capture the rapid electrical activity of neurons with the same temporal resolution. Calcium imaging also relies on genetically encoded sensors in many experimental settings, limiting its application in humans.

Understanding Brain Disorders

The ability to monitor individual neurons across larger brain networks could help researchers investigate how changes in neural activity contribute to neurological and psychiatric conditions. Diseases such as Alzheimer’s involve changes in brain cells and the circuits connecting them. Understanding how these changes affect communication between neurons could provide important information about disease progression and the development of potential treatments.

Neuropixels is already being used in research examining neural circuit activity in animal models of neurodegenerative conditions. Its expansion into human research could eventually help scientists compare findings from animal experiments with neuronal activity recorded directly from patients.

However, the technology remains a research tool. Its use during surgery is still developing, and further work is needed to establish safe and reliable procedures for different research applications. The recordings themselves do not diagnose brain disorders or provide an immediate treatment.

For now, the major contribution of Neuropixels is the scale and precision of the information it can collect. By allowing scientists to follow the electrical activity of large populations of individual neurons, it is helping them investigate how the brain processes information through interconnected networks of cells.