In brief

Using deep learning models, A*STAR researchers enhanced the design of power dividers in photonic chips, resulting in high-efficiency components that are compact and suitable for large-scale production.

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Bypassing silicon for light speed computing

2 Apr 2024

A new computer chip technology that uses light promises faster data processing for advancements in quantum communications and optical networking.

Phones and computers have gotten exponentially smarter and faster over the years. In the world of electronics, this growth has been made possible by Moore’s Law: the number of transistors on circuit boards that make up the heart of electronics doubles approximately every two years.

Nevertheless, the sector is reaching a juncture where integrating additional transistors onto silicon wafers, the traditional material for circuit boards, is proving to be a formidable challenge.

Consequently, materials scientists are investigating new materials and technologies to sustain the progression of computational power without being constrained by the physical limitations of silicon.

Principal Scientist Alagappan Gandhi, and Director for the Electronics and Photonics Department, Jason Png, from A*STAR’s Institute of High-Performance Computing (IHPC) have been exploring a novel paradigm that harnesses the power of light. “In contrast to electronic chips reliant on electrons, photonic chips exploit light for data transmission, yielding swifter communication and higher capacity,” noted the duo.

This photonic chip technology is similar to optical fibres used in long-distance data transfer, and according to experts, can completely revolutionise computer architecture.

While the potential of photonic circuits is vast, current chips have a much larger physical footprint compared to electronic circuits, presenting a hurdle for commercial viability. Addressing this, Alagappan and Png have reconceptualised the design of pivotal photonic components. Their study centred on the refinement of power dividers, integral devices that split light into discrete pathways for downstream processing.

Employing deep learning, the team enhanced inverse design algorithms—computational methods that ideate components conforming to specified functionalities and constraints.

Listing the merits of this approach, Alagappan said, “Embracing inverse design methodologies opens the door to creating innovative power dividers characterised by compactness, minimal loss, large bandwidth and importantly, suitability for large-scale manufacturing processes.”

Deep-learning models are advanced computer programmes that enhance the design process (which traditionally could be quite slow) by simulating how light moves through different structures. This allows for a more efficient and creative approach to design, where the computer can quickly test and refine different models.

This method proved to be particularly useful as the team successfully designed a range of power dividers that were shown to be highly efficient, compact and resilient to manufacturing variations.

Significantly, the team's deep-learning-based design process surpasses traditional methods in speed without sacrificing the precision or effectiveness of the final product.

Looking ahead, the researchers intend to apply their methods to other photonic components, potentially unlocking new prospects in quantum communications and optical networking.

The A*STAR-affiliated researchers contributing to this research are from the Institute of High-Performance Computing (IHPC).

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Alagappan, G. and Png, C.E. Deep learning accelerated discovery of photonic power dividers. Nanophotonics 12 (7), 1255-1269 (2023). | article

About the Researchers

Gandhi Alagappan is a Principal Scientist at A*STAR’s Institute of High-Performance Computing (IHPC). Beginning with a first-class honours bachelor's degree in Engineering from Nanyang Technological University, Singapore, he earned the prestigious ASEAN Undergraduate Scholarship. His academic pursuit continued with a PhD in photonics at the same institution, supported by the esteemed A*STAR Graduate Scholarship. Subsequently, Alagappan conducted postdoctoral research at the University of Toronto, Canada, under Sajeev John, a pioneer in photonics known for his work on photonic crystals and metasurface technology. With 40 first-author journal publications, Alagappan has made substantial contributions to photonics, focusing on integrated optical circuits, quantum photonics and inverse design. His expertise extends to AI-driven discovery of photonic devices, photonic computing acceleration and design automation using large language models. His leadership is evident in project management, and as an adjunct lecturer at Singapore Polytechnic, he actively shapes the next generation of scholars.
Jason Ching Eng Png obtained his PhD in Silicon Photonics from the University of Surrey, UK in 2004. In 2014, he earned executive MBA degrees from INSEAD and Tsinghua University, China. Additionally, he completed the Innovative Business Leadership Program at the Massachusetts Institute of Technology (MIT) Sloan School, USA in 2013. Between 1999 and 2000, he worked with Agilent Technologies. Since 2005, he has been affiliated with A*STAR’s Institute of High-Performance Computing (IHPC) in Singapore where he currently directs research in electronics and photonics. Png has extensive experience in commercialising silicon photonics. He successfully spun off Optic2Connect Pte Ltd, a silicon photonics modelling and design company, from A*STAR funded by Enterprise Singapore. In recognition of his significant contributions to photonics research, he has been honoured with the prestigious Royal Academy of Engineering Prize at the Palace of Westminster, UK, the IET Innovation Award in Software Design (highly commended), the Skolkovo Prize, and the Vebleo Scientist Award. He has also been elected as a Fellow of the IET.

This article was made for A*STAR Research by Wildtype Media Group