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Nanophotonics, Plasmonics & Inverse-Designed Photonic Devices
Nanophotonics, Plasmonics & Inverse-Designed Photonic Devices
Development of plasmonic nanomaterials, metamaterials, metasurfaces, PCM and 2D-material platforms for biosensing, plasmonic circuitry, and light management in next-generation solar and biomedical technologies.
Research Motivation:
Enhance Light–Matter Interaction: Advance plasmonic, metamaterial, and 2D material-based photonic devices for high-performance sensing, imaging, and optical communication.
Develop Scalable & Integrated Photonics: Design cost-effective, broadband, and miniaturized photonic components for next-generation on-chip optical systems.
Enable Future Photonic Technologies: Improve light–matter coupling and device reliability to support quantum photonics, optical computing, and harsh-environment applications.
Previous related work:
https://doi.org/10.25916/sut.26271751.v1
https://doi.org/10.1109/JPHOT.2018.2825435
https://doi.org10.1109/ACCESS.2024.3382713
https://doi.org/10.1007/s12596-022-00837-9
Solar Cell/ STPV/Absorber
Solar Cell/ STPV/Absorber
Development of 3rd generation solar cell (Thin Film/Plasmonic/QD/Perovskite/IB /STPV (Single/Tandem)
Research Motivation:
Material Stability: Focus on the long-term stability and environmental effects of emerging materials (like perovskites and quantum dots) in solar cells to improve their practical application.
Efficiency Limitations: Address the efficiency bottlenecks in tandem solar cells, particularly in combinations of different materials, to maximize energy conversion rates.
Cost Reduction: Research cost-effective synthesis and deposition techniques for thin-film solar cells to reduce the overall production expenses while maintaining performance.
Previous related work:
https://doi.org/10.1016/j.egyr.2024.03.007
https://doi.org/10.1007/s12596-020-00656-w
AI-Integrated ASIC, FPGA and Edge Computing Systems
To develop energy-efficient, secure, trustworthy, and intelligent semiconductor hardware by integrating AI with ASIC, FPGA, heterogeneous computing, and emerging semiconductor technologies for future computing, communication, healthcare, robotics, and sustainable infrastructure
Key Research Focus
ASIC, FPGA, and SoC Design (RISC-V, VLSI, low-power and heterogeneous computing)
AI Hardware Accelerators (Edge AI, TinyML, CNN/Transformer, neuromorphic computing)
AI-assisted chip design
AI for Electronic Design Automation (EDA) (RTL generation, placement & routing, verification, optimization)
Low-power VLSI and green computing
Analogue and Mixed Signal Design
Embedded Systems, Edge AI, and Intelligent IoT
Hardware Security and Trustworthy AI (PUF, hardware security, post-quantum systems)
Beyond CMOS: Silicon Photonics, Programmable PICs & Energy-Efficient Optical/Quantum Systems
Design of CMOS-compatible photonic devices and reconfigurable PIC architectures for FinFET-scale integration, optical processors/interconnects, sensing, high-speed memory, quantum information, and data-centric computing.
Research Motivation:
Enable next-generation, energy-efficient computing and communication by developing CMOS-compatible silicon photonics and programmable PIC architectures that deliver high-bandwidth optical interconnects, reconfigurable processing, and FinFET-scale system integration.
Overcome the performance and scaling limits of conventional electronics through optical and quantum-aware hardware platforms that support data-centric computing, high-speed memory, and sensing with significantly lower latency and power consumption.
Translate photonics-driven computation into practical, manufacturable technology by designing programmable, application-ready PIC systems for AI workloads, secure communication, biomedical sensing, and emerging quantum-information applications.
Nanobiophotonics/Biosensor and Imaging
Nanobiophotonics/Biosensor and Imaging
NP-Cell Interaction, Uptake, Aggregation kinetics,Biosensinga nd Imaging, Signalling, Drug Delivery, Tracking, Disease Detection and Diagnosis, etc.
Research Motivation:
Nano–Bio Interactions: Understand nanoparticle–cell interactions and aggregation kinetics to improve targeted drug delivery and therapeutic efficacy.
Ultra-Sensitive Biosensing: Develop plasmonic nanostructures and biosensors for early, high-sensitivity detection of low-abundance biomarkers.
Advanced Bioimaging: Integrate nanophotonics and metamaterials with optical imaging to achieve higher resolution, contrast, and diagnostic accuracy.
Previous related work:
https://doi.org/10.25916/sut.26271751.v1
https://doi.org/10.1088/1402-4896/ad735b
https://doi.org/10.1088/1402-4896/ad3513
https://doi.org/10.1007/s11468-024-02674-x
Renewables/Digital Twin/NetZero
Renewables/Digital Twin/NetZero
Renewable based CO2 mitigation, Digital Twin, Net Zero, EV, Battery management, Algae Based CO2 mitigation
Research Motivation:
Data-Driven Optimization: Investigate the use of digital twins for real-time optimization and predictive maintenance in renewable energy systems, such as wind and solar farms.
Behavioral Modeling: Study social behavior and technological adoption factors influencing renewable energy usage in communities, emphasizing strategies for achieving net-zero targets.
Integration of CO2 Mitigation Strategies: Examine the effectiveness of combined renewable-based CO2 mitigation strategies, such as algae cultivation and biomaterials, within digital twin frameworks.
Previous related work:
https://doi.org/10.1109/GlobConHT56829.2023.10087679
https://doi.org/10.1109/ICMEE.2010.5558476.
Robotics and Internet of Things
Robotics and Internet of Things
Smart City, Smart House, Smart Farming, Smart Health, Smart Grid, etc.
Research Motivation:
Energy Optimization: Research smart algorithms for energy management in smart grids and smart farming to optimize resource usage and reduce waste.
Interoperability Challenges: Investigate solutions to improve interoperability among various IoT devices and platforms to enhance functionality and user experience in smart environments.
Data Privacy and Security: Examine strategies for ensuring data privacy and security within IoT applications in smart cities, especially concerning sensitive user information and autonomous systems.
Previous related work:
https://doi.org/10.1007/s43926-024-00073-6
https://doi.org/10.1007/s13762-024-05954-5
https://doi.org/10.1016/j.heliyon.2024.e26348.
Therapeutic Potential of Nanomaterial
Nanoparticle–Bacteria Interactions & Therapeutic Nanomaterials for Infectious Disease Detection, Diagnosis and Treatment
Design of plasmonic, metallic, polymeric, and hybrid nanomaterials with diagnostic, antimicrobial, and drug-delivery functionalities, enabling next-generation biosensing, rapid detection, targeted therapy, and resistance-aware treatment pathways for infectious diseases.
Address the global burden of antimicrobial resistance (AMR) by developing nanomaterial-based theranostic platforms that enable early bacterial detection, rapid diagnosis, and precision-guided treatment, reducing misuse of broad-spectrum antibiotics.
Bridge the gap between nanoscale interaction physics and clinical translation by investigating nanoparticle–bacteria interaction dynamics (uptake, membrane response, biofilm interaction) to inform the design of safer, more effective therapeutic nanomaterials.
Enable low-cost, portable and point-of-care infectious disease technologies through plasmonic and nano-biosensing platforms that support real-time pathogen monitoring, outbreak surveillance, and personalized treatment strategies.
Applied Machine/Deep Learning/Inverse Design
Applied Machine/Deep Learning/Inverse Design
Photonic inverse design, Disease detection, Cell segmentation, precision agriculture, and Energy Management
Key Research Focus
Key Research Focus
Applied AI & Deep Learning: Predictive modeling, computer vision, disease detection, and precision agriculture.
Inverse Design & Generative AI: AI-driven design of photonic, electronic, and nanophotonic devices.
Physics-Informed AI: Digital twins, surrogate modeling, and AI-assisted multiphysics simulation.
Autonomous Optimization: Reinforcement learning and closed-loop optimization for intelligent materials and devices.
Scientific AI: Material discovery, real-time characterization, and explainable AI for next-generation engineering systems.
Previous related work:
Next-Generation Photonic Computing and Spintronic Systems
Next-Generation Photonic Computing and Spintronic Systems
Research Motivation
Research Motivation
Beyond CMOS Computing: Develop ultra-fast, low-power photonic and spintronic technologies to overcome the limitations of conventional electronic computing.
AI-Native Hardware: Enable energy-efficient optical computing platforms for AI, high-performance computing (HPC), and data centers.
Integrated Intelligent Systems: Advance scalable photonic integrated circuits (PICs) and spintronic architectures for future communication and computing systems.
Key Research Focus
Key Research Focus
Photonic Logic & Processing: Optical logic gates, photonic processors, neuromorphic and optical AI accelerators.
Advanced Photonic Devices: High-speed photonic modulators, switches, waveguides, nanoantennas, optical interconnects, and integrated photonic devices.
Silicon & Quantum Photonics: Silicon photonics, heterogeneous photonic integration, quantum photonic components, and photonic integrated circuits (PICs).
Spintronic Computing: Spintronic devices, spin-wave logic, MRAM, spin-orbitronics, and non-volatile computing architectures.
Co-Designed Photonic–Spintronic Systems: Hybrid electronic–photonic–spintronic platforms for beyond-CMOS, edge AI, 6G/7G communications, and exascale computing.
Previous related work:
https://doi.org/10.1088/1402-4896/ad735b
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