Energy efficient integrated MEMS neural network for simultaneous sensing and computing

Authors

Hamed Nikfarjam, University of Texas at Dallas
Mohammad Megdadi, University of Nebraska-Lincoln
Mohammad Okour, University of Nebraska-Lincoln
Siavash Pourkamali, University of Nebraska-Lincoln
F. M. Alsaleem, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

4-29-2023

Citation

COMMUNICATIONS ENGINEERING | (2023) 2:19 | https://doi.org/10.1038/s44172-023-00071-6

Comments

Open access.

Abstract

Biological systems seamlessly combine multiple functions in lightweight and energy-efficient structures. Such capability in synthetic structures would be desirable in numerous engineering applications such as aerospace, robotics and wearable devices. Here we report an integrated silicon-based structure configured to sense, perform different classification algorithms, and produce an action signal within the same physical layer. The algorithms are coded in the mechanical responses of the sensing elements of multiple coupled micro-electromechanical systems (MEMS), simultaneously capturing acceleration measurements to produce an actuated signal. This all-in-one structure operates with zero circuitry and low power consumption. As a demonstration, we designed and fabricated a network of three MEMS neurons to successfully perform both simple signal classification and activity recognition problems (standing and sitting) with only 9.92 × 10⁻¹⁷ kWh and 17.79 × 10⁻¹⁹ kWh energy consumption per operation, respectively. Our approach will enable emergent technologies, such as wearable devices, to perform complex computations with power from a single battery charge.