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Abstract High-bandwidth applications, from multi-gigabit communication and high-performance computing to radar signal processing, demand ever-increasing processing speeds. However, they face limitations in signal sampling and computation due to hardware and power constraints. In the microwave regime, where operating frequencies exceed the fastest clock rates, direct sampling becomes difficult, prompting interest in neuromorphic analog computing systems. We present the first demonstration of direct broadband frequency domain computing using an integrated circuit that replaces traditional analog and digital interfaces. This features a Microwave Neural Network (MNN) that operates on signals spanning tens of gigahertz, yet reprogrammed with slow, 150 MBit/sec control bitstreams. By leveraging significant nonlinearity in coupled microwave oscillators, features learned from a wide bandwidth are encoded in a comb-like spectrum spanning only a few gigahertz, enabling easy inference. We find that the MNN can search for bit sequences in arbitrary, ultra-broadband10 GBit/sec digital data, demonstrating suitability for high-speed wireline communication.Notably, it can emulate high-level digital functions without custom on-chip circuits, potentially replacing power-hungry sequential logic architectures. Its ability to track frequency changes over long capture times also allows for determining flight trajectories from radar returns. Furthermore, it serves as an accelerator for radio-frequency machine learning, capable of accurately classifying various encoding schemes used in wireless communication. The MNN achieves true, reconfigurable broadband computation, which has not yet been demonstrated by classical analog modalities, quantum reservoir computers using superconducting circuits, or photonic tensor cores, and avoidsthe inefficiencies of electro-optic transduction. Its sub-wavelength footprint in a Complementary Metal-Oxide-Semiconductor process and sub-200 milliwatt power consumption enable seamless integration as a general-purpose analog neural processor in microwave and digital signal processing chips.
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This content will become publicly available on March 28, 2026
Nanoelectronics-enabled reservoir computing hardware for real-time robotic controls
Traditional robotic vehicle control algorithms, implemented on digital devices with firmware, result in high power consumption and system complexity. Advanced control systems based on different device physics are essential for the advancement of sophisticated robotic vehicles and miniature mobile robots. Here, we present a nanoelectronics-enabled analog control system mimicking conventional controllers’ dynamic responses for real-time robotic controls, substantially reducing training cost, power consumption, and footprint. This system uses a reservoir computing network with interconnected memristive channels made from layered semiconductors. The network’s nonlinear switching and short-term memory characteristics effectively map input sensory signals to high-dimensional data spaces, enabling the generation of motor control signals with a simply trained readout layer. This approach minimizes software and analog-to-digital conversions, enhancing energy and resource efficiency. We demonstrate this system with two control tasks: rover target tracking and drone lever balancing, achieving similar performance to traditional controllers with ~10-microwatt power consumption. This work paves the way for ultralow-power edge computing in miniature robotic systems.
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- Award ID(s):
- 2331169
- PAR ID:
- 10636251
- Publisher / Repository:
- American Association for the Advancement of Science
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 11
- Issue:
- 13
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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