United States of America As Represented by the Secretary of the Air Force

David C Williamson

Daniel J Burns

Mark T Pronobis

A novel microelectromechanical system artificial neural network (MEMS ANN) device performs the function of a conventional artificial neural network node element. Micro-machined polysilicon or high aspect ratio composite beam micro-resonators replace as computational elements the silicon transistors and software simulations of prior-art ANNs. The basic MEMSANN device forms a non-linear (e.g., sigmoid) function of a sum of products. Products of the magnitudes of sine waves, applied to the input drive comb and shuttle magnitudes, are formed in the frequency domain and summed by coupling a plurality of resonators with a mechanical coupling frame, or by integrating them into one resonator. A sigmoid function is applied to the sum of products by shaping the overlap capacitance of the output comb fingers of the resonator. Methods of building and using various single MEMS ANN devices and multi-layered arrays of MEMS ANN circuits are also described. These novel MEMS ANNs exhibit an attractive combination of performance characteristics, compared to conventional hardware ANNs that use silicon transistors or simulations of ANNs running in software on digital computers, including lower cost, simpler design, wider temperature range, greater radiation tolerance, and lower operating and standby power. These advantages favorably impact system weight and size because of reduced shielding, cooling, and power requirements.

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Holland Patent, NY, United States of America

David C Williamson