EP-4742075-A1 - METHOD AND SYSTEM FOR A GENERATIVE ARTIFICIAL INTELLIGENCE BASED SENSOR DESIGN SYNTHESIS OF METAMATERIAL

Abstract

The embodiments of the present disclosure herein address unresolved problems of unavailability of a scalable framework to enable large spectrum of optical response-design combination with reduced computing time. Further, there are limitations in the choice of features to obtain the desired sensitivity. Embodiments herein provide a method and system for a generative artificial intelligence (GenAl) based sensor design synthesis of metamaterial. Herein, the sensor design synthesis framework is based on deep generative models. The GenAl based design synthesis model would provide that unknown information that would immensely be useful for optimizing the design towards the highest sensitivity, addressing the limitations in the choice of features using numerical simulator and easiest fabrication-feasibility following sensitivity response (SenR). The SenR is the sensitivity relationship between the physical properties of the ambient medium and the property of the sensor.

Inventors

• BANDYOPADHYAY, SOMA
• CHATTERJEE, SUBHASRI
• Datta, Anish
• CHAKRAVARTY, TAPAS
• PAL, ARPAN

Assignees

• Tata Consultancy Services Limited

Dates

Publication Date

20260513

Application Date

20250922

Claims (15)

1. A processor-implemented method (300) comprising: receiving (302), via an input/output (I/O) interface, one or more geometrical structures with a plurality of geometric design parameters, and one or more optical responses as a target sensor response to synthesize a metamaterial sensor design using a generative artificial intelligence (GenAI) model; generating (304), via one or more hardware processors, a set of optical response conditioned on the received one or more geometrical structures in a forward path learning technique using a conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned by the received one or more geometrical structures along with sensitivity relationship (SenR); and generating (306), via the one or more hardware processors, a geometrical structure of the metamaterial from the set of optical response in an inverse path learning technique using the conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned on optical response along with the sensitivity relationship (SenR) and with one or more domain adaptable features.
2. The processor-implemented method (300) as claimed in claim 1, wherein the conditional variational auto-encoder uses a conditional prior for data synthesis with the forward path learning and the inverse path learning.
3. The processor-implemented method (300) as claimed in claim 2, wherein the conditional prior includes a geometry of sensor, an optical response, and a sensitivity relationship (SenR).
4. The processor-implemented method (300) as claimed in claim 1, wherein the generated geometrical structure of metamaterial is operating through a predefined optical wavelength range; wherein the sensitivity relationship is a function of physical property and geometry and influences the sensitivity; wherein an optimization is followed using a loss function constraining towards the sensitivity relationship during the forward path learning and the inverse path learning.
5. The processor-implemented method (300) as claimed in claim 1, wherein response of the metamaterial sensor is changing with respect to different geometrical features and with respect to the physical property.
6. A system (100) comprising: a memory (110) storing instructions; one or more Input/Output (I/O) interfaces (104); and one or more hardware processors (108) coupled to the memory (110) via the one or more I/O interfaces (104), wherein the one or more hardware processors (108) are configured by the instructions to: receive one or more geometrical structures with a plurality of geometric design parameters, and one or more optical responses as a target sensor response to synthesize a metamaterial sensor design using a generative artificial intelligence (GenAI) model; generate a set of optical response conditioned on the received one or more geometrical structures in a forward path learning technique using a conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned by the received one or more geometrical structure along with sensitivity relationship (SenR); and generate a geometrical structure of the metamaterial from the set of optical response in an inverse path learning technique using the conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned on optical response along with the sensitivity relationship (SenR) and with one or more domain adaptable features.
7. The system (100) as claimed in claim 6, wherein the conditional variational auto-encoder uses a conditional prior for data synthesis with the forward path learning and the inverse path learning.
8. The system (100) as claimed in claim 7, wherein the conditional prior includes a geometry of sensor, an optical response, and a sensitivity relationship (SenR).
9. The system (100) as claimed in claim 6, wherein the generated geometrical structure of metamaterial is operating through a predefined optical wavelength range. wherein the sensitivity relationship is a function of physical property and geometry and influences the sensitivity. wherein an optimization is followed using a predefined loss function during the forward path learning and the inverse path learning.
10. The system (100) as claimed in claim 8, wherein response of the metamaterial sensor is changing with respect to different geometrical features and with respect to the physical property.
11. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving , via an input/output (I/O) interface, one or more geometrical structures with a plurality of geometric design parameters, and one or more optical responses as a target sensor response to synthesize a metamaterial sensor design using a generative artificial intelligence (GenAI) model; generating , a set of optical response conditioned on the received one or more geometrical structures in a forward path learning technique using a conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned by the received one or more geometrical structures along with sensitivity relationship (SenR); and generating , via the one or more hardware processors, a geometrical structure of the metamaterial from the set of optical response in an inverse path learning technique using the conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned on optical response along with the sensitivity relationship (SenR) and with one or more domain adaptable features.
12. The one or more non-transitory machine-readable information storage mediums of claim 11, wherein the conditional variational auto-encoder uses a conditional prior for data synthesis with the forward path learning and the inverse path learning.
13. The one or more non-transitory machine-readable information storage mediums of claim 12, wherein the conditional prior includes a geometry of sensor, an optical response, and a sensitivity relationship (SenR).
14. The one or more non-transitory machine-readable information storage mediums of claim 11, wherein the generated geometrical structure of metamaterial is operating through a predefined optical wavelength range; wherein the sensitivity relationship is a function of physical property and geometry and influences the sensitivity; and wherein an optimization is followed using a loss function constraining towards the sensitivity relationship during the forward path learning and the inverse path learning.
15. The one or more non-transitory machine-readable information storage mediums of claim 11, wherein response of the metamaterial sensor is changing with respect to different geometrical features and with respect to the physical property.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY The present application claims priority to Indian application no. 202421085898, filed on November 08, 2024. TECHNICAL FIELD The disclosure herein generally relates to the field of sensor design synthesis, and more particularly, a method and system for a Generative Artificial Intelligence (GenAI) based sensor design synthesis of metamaterial. BACKGROUND Plasmonic metamaterial sensor offers a promising approach to the detection of nano plastic particles due to their high sensitivity and compatibility with nanoscale analytes. When nano plastic particles interact with the sensor surface, they induce changes in the refractive index, leading to shifts in the plasmonic resonance wavelength or angle. These changes can be monitored in real time, providing quantitative information about the presence and concentration of nano plastic particles in the sample. Furthermore, the nanoscale dimensions of plastic particles are well-suited to the sensitivity and spatial resolution capabilities of plasmonic sensor, enabling the detection of individual nanoparticles. By optimizing the design and fabrication of plasmonic metamaterial sensor, it is likely to develop highly sensitive platforms for the detection of nano plastic pollution in environmental samples. This could contribute to efforts aimed at understanding and mitigating the environmental impact of nano plastic contamination in aquatic ecosystems. Traditional design systems and methods often require manual manipulations, which is time-consuming and resource intensive. The integration of Artificial Intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) play important role in the generation of novel metamaterial designs by capturing the intrinsic patterns of the sensor response and its associated geometrical structure along with the material property and other physical characteristics of ambient environment with a learned latent space and patterns to present novel metamaterial configurations with tuned and desired functionality. However, there is unavailability of a scalable framework to enable large spectrum of optical response-design combination with reduced computing time. Further, there are limitations in the choice of features to obtain the desired sensitivity. Numerical simulators are unable to generate structure/geometry with optical response as in input. There is unavailability of desired design for nano-plastic sensors and unavailability of mode of detection through a wide refractive index range. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for a Generative Artificial Intelligence (GenAI) based sensor design synthesis of metamaterial is provided. The processor-implemented method includes receiving, via an input/output (I/O) interface, one or more geometrical structures with a plurality of geometric design parameters, and one or more optical response as a target sensor response corresponding to each of the one or more geometrical structures to synthesize a metamaterial sensor design using the GenAI model. Further, the processor-implemented method includes generating, via one or more hardware processors, a set of optical response conditioned on the received one or more geometrical structures in a forward path learning technique using a conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned by the received one or more geometrical structure along with sensitivity relationship (SenR) and generating, via the one or more hardware processors, a geometrical structure of the metamaterial from the set of optical response in an inverse path learning technique using the conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned on optical response along with the sensitivity relationship (SenR) and with one or more domain adaptable features. In another embodiment, a system for a generative artificial intelligence (GenAI) based sensor design synthesis of metamaterial is provided. The system comprises a memory storing a plurality of instructions, one or more Input/Output (I/O) interfaces, and one or more hardware processors coupled to the memory via the one or more I/O interfaces. The one or more hardware processors are configured by the instructions