The task of making a mechanical signal that can recognize a spoken command is challenging. To make it accessible, the project is structured in three work-packages (WPs). In WP1, we are designing simplified models, consisting of idealized masses and springs, that recognize to specific key words. Mass-spring models are relatively easy to design and simulate, and hence working with them allows us to explore many concepts for passive mechanical word recognition. In WP2, we aim at converting these idealized mass-spring models into blueprints of devices that can be fabricated. In WP3, we seek to fabricate the designs, using state-of-the-art cleanroom equipment. Although the three WPs address independent challenges, the team works together to ensure that the results are meaningful: In WP1 we consider realistic parameters (using inputs from WP2 and WP3), and in WP2 we explore device designs that are both useful and can be fabricated experimentally.
To achieve this goal, we use GPU-accelerated codes for the efficient simulation of mass-spring systems with a large number of parts. We conduct simulations of these complex networks to understand their behavior and optimize their capability of reacting only to a specific spoken word. After we have identified relevant mass-spring models, we convert them into device blueprint. To navigate the large space of possible material designs, we use finite element simulations, computer optimization and artificial intelligence. We also develop novel mathematical ‘tricks’ that allow us to drastically reduce the system complexity, while accurately describing the relevant phenomena. We fabricate the samples in a cleanroom, using photolithography and etching techniques borrowed from the microelectronics industry – that can be scaled to large quantities once successful designs are identified. We characterize the resulting samples using a microscope-based laser vibrometry setup.
