N231-012 TITLE: Compact, High-Performance Metamaterial Acoustic filter with Monolithic Integrated Signal Processor

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): General Warfighting Requirements (GWR);Microelectronics

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop passive acoustic metamaterials that can be combined/supplemented with the Navy�s existing acoustic sensor to perform analog signal processing, with the following simultaneous and substantial performance improvement objectives:

(a) 10X improvement in the signal to noise ratio;

(b) 1000X reduction in the amount of post-reception signals requiring Analog to Digital (A/D) & D/A conversions, digital processing, and sensor-to-aerial platform transmission, thereby significantly and commensurately improving the overall system speed and reducing the sensor power consumption.

 

DESCRIPTION: Naval underwater acoustic sensors operate in an underwater environment that is inundated with noises from multiple natural and man-made sources such as breaking waves, marine lives, and ship traffic. Reducing the noise level in an acoustic sensor�s received signals is critical to increasing the sensitivity in detecting acoustic signatures of modern underwater ever quieter naval targets. Since naval underwater acoustic sensors are deployed in water as expendable sensors, they also are constrained by limited on-board power supply, as well as the latency in system communication and information processing with the aerial platform. Acoustic metamaterials have recently demonstrated the full control of acoustic waves� amplitudes and phases [Refs 1�4], and therefore create the unparalleled potential to be used as an integrated analog signal processor within a sensor. This unique characteristic of the metamaterials is revolutionary, as the conventional acoustic sensors alone cannot possess any sensed signal or information [Refs 5�7]. A passive acoustic metamaterial layer mounted on the front end of an acoustic sensor can process the incoming acoustic signals, extract, and identify the acoustic signatures before acoustic-to-electrical transduction, A/D conversion, and sensor-to-aerial platform transmission. Such analog signal processing components will lead to significantly increased signal-to-noise ratio, reduced power consumption, and improved sensing speed compared to the existing legacy systems that directly capture and relay all the received digital signals to the aerial platform for back-end digital processing.

One pragmatic approach for implementing this multifunctional metamaterial filter/processor for improving the signal-to-noise ratio is to implement acoustic frequency and spatial filters into the metamaterial filter layer to remove the noises from various sources. Those filters can be created with arrays of subwavelength resonance structures. For instance, if the center frequency and direction for the acoustic signal reception are f_0 and a_0, respectively, only the signals within a narrow frequency band (e.g., f_0 � 0.1f_0) and direction range (e.g., a_0 � 10�) will be able to pass through the metamaterial layer and reach the underlying sensor. Noise outside the designated frequency and direction ranges will be rejected. In addition to metamaterial�s noise reduction capability via frequency/direction filtering, the metamaterial layer is multifunctional and also possesses the aforementioned unique, game-changing feature of extracting and identifying relevant underwater acoustic target signatures without the traditional back-end post-reception digital computational processing. Only those extracted features will be converted to electrical signals, digitalized, and transmitted to the aerial platform. As a result, there would be a 1000X reduction in the post-reception and post-detection information signals that require A/D & D/A conversions, digital processing, and sensor-to-aerial platform transmission, thereby significantly and commensurately improving the overall system speed. Last, but not the least, as the acoustic metamaterial layer is a completely passive structure that has no power consumption, the associated electronics of the acoustic sensor will consume less power and have lower complexity proportionally compared to that of existing legacy sensor system.

It is therefore the goal of this SBIR topic to develop passive acoustic metamaterials that can be combined/supplemented with the Navy�s existing acoustic sensor to perform analog signal processing, with the following simultaneous and substantial performance improvement objectives:

(a) 10X improvement in the signal to noise ratio;

(b) 1000X reduction in the amount of post-reception signals requiring A/D & D/A conversions, digital processing, and sensor-to-aerial platform transmission, thereby significantly and commensurately improving the overall system speed and reducing the sensor power consumption.

 

PHASE I: Determine feasibility of suitable acoustic metamaterials and the design procedure for a passive signal processing layer that extracts underwater target signatures from acoustic echo signals. Develop a detailed concept design that shows 10X improvement in the signal-to-noise ratio and 1000X reduction in the amount of post-reception and post-detection information requiring A/D & D/A conversions, digital processing, and sensor-to-aerial platform transmission. Modeling and simulation, or other rigorous and scientifically sound methods, should be used to demonstrate the metamaterial�s performance in accordance with the stated metrics of interest. Begin development of a prototype manufacturing plan for Phase II. The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Develop, demonstrate, and validate a well-defined deliverable prototype, which meets topic requirements. Test and evaluate the acoustic filtering and signature detection performances of the prototype in a laboratory setting and then in a relevant simulated operating environment compatible with intended naval applications. Deliver a prototype, including recommendations for large-scale manufacturing.

PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the technology for DoD use. Since the design and prototypes are generic, assist in applying the design for specific system applications such as active or passive underwater target detection and identification.

The industrial and medical sectors can also benefit from this crucial, game-changing technology development in the areas of acoustic detection and identification for industrial equipment and noninvasive health monitoring and sensing with unprecedented signal-to-noise improvements.

REFERENCES:

1.       Cummer, S. A., Christensen, J., & Alù, A. (2016). Controlling sound with acoustic metamaterials. Nature Reviews Materials, 1(3), 1-13. https://doi.org/10.1038/natrevmats.2016.1

2.       Ge, H., Yang, M., Ma, C., Lu, M. H., Chen, Y. F., Fang, N., & Sheng, P. (2018). Breaking the barriers: advances in acoustic functional materials. National Science Review, 5(2), 159-182. https://doi.org/10.1093/nsr/nwx154

3.       Xie, Y., Wang, W., Chen, H., Konneker, A., Popa, B. I., & Cummer, S. A. (2014). Wavefront modulation and subwavelength diffractive acoustics with an acoustic metasurface. Nature communications, 5(1), 1-5. https://doi.org/10.1038/ncomms6553

4.       Ma, C., Li, X., & Fang, N. X. (2020). Acoustic Angle-Selective Transmission Based on Binary Phase Gratings. Physical Review Applied, 14(6), 064058. https://doi.org/10.1103/PhysRevApplied.14.064058

5.       Zangeneh-Nejad, F., Sounas, D. L., Alù, A., & Fleury, R. (2021). Analogue computing with metamaterials. Nature Reviews Materials, 6(3), 207-225. https://doi.org/10.1038/s41578-020-00243-2

6.       Zuo, S., Wei, Q., Tian, Y., Cheng, Y., & Liu, X. (2018). Acoustic analog computing system based on labyrinthine metasurfaces. Scientific reports, 8(1), 1-8. https://doi.org/10.1038/s41598-018-27741-2

 

KEYWORDS: Metamaterials; Acoustic Filter; Monolithic; Signal Processor; Acoustic Sensor; Aerial Platform

---