Reservoir computing was achieved by constructing a network of virtual nodes multiplexed in time and sharing a single silicon beam exhibiting a classical Duffing non-linearity as the source of nonlinearity. The delay-coupled electromechanical system performed well on time series classification tasks, with error rates below 0.1% for the 1st, 2nd, and 3rd order parity benchmarks and an accuracy of (78±2)% for the TI-46 spoken word recognition benchmark. As a first demonstration of reservoir computing using a non-linear mass-spring system in MEMS, this result paves the way to the creation of a new class of compact devices combining the functions of sensing and computing.

This study presents the design, fabrication, and test of a micro accelerometer with intrinsic processing capabilities, that integrates the functions of sensing and computing in the same MEMS. The device consists of an inertial mass electrostatically coupled to an oscillating beam through a gap of 8 µm. The motion of the inertial mass modulates an AC electrostatic field that drives the beam in its non-linear regime. This non-linearity is used to implement machine learning in the mechanical domain, using reservoir computing with delayed feedback to process the acceleration information provided by the inertial mass. The device is microfabricated on a silicon-on-insulator substrate using conventional MEMS processes. Dynamic characterization showed good accelerometer functionalities, with an inertial mass sensitivity on the order of 100 mV/g from 250 to 1300 Hz and a natural frequency of 1.7 kHz. In order to test the device computing capabilities, two different machine learning benchmarks were implemented, with the inputs fed to the device as accelerations. The neuromorphic MEMS accelerometer was able to accurately emulate non-linear autoregressive moving average models and compute the parity of random bit streams. These results were obtained in a test system with a non-trivial transfer function, showing a robustness that is well-suited to anticipated applications.

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