The evolution of computers is driven by advances not only in computer science, but also in materials science. As the post-CMOS era approaches, research is increasingly focusing on flexible and unconventional computing systems, including the study of systems that incorporate new computational paradigms into the materials, enabling the computer and the material to be the same entity.
In this dissertation, we design a coupled oscillator system based on a new hybrid material that can autonomously transduce chemical, mechanical, and electrical energy. Each material unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemo-mechanical oscillations of the BZ gels deflect the piezoelectric layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these coupled units become synchronized across the network, with the mode of synchronization depending on the polarity of the piezoelectric. Taking advantage of this synchronization behavior, we demonstrate that the network of coupled BZ-PZ oscillators can perform specific computational tasks such as pattern matching in a self-organized manner, without external electrical power sources. The results of the computational modeling show that the convergence time for stable synchronization gives a distance measure between the “stored” and “input” patterns, which are encoded by the connection and phases of BZ-PZ oscillators. In addition, we demonstrate two methods to enrich the information representation in our system. One is to employ multiple BZ-PZ oscillator networks in parallel and to process information encoded in different channels. The other is to introduce capacitors into a BZ-PZ network that modify the dynamical behavior of the systems and increase the information storage. We analyze and simulate the proposed coupled oscillator systems by using linear stability analysis and phase models and explore their potential computational capabilities. Through these studies, we establish experimentally realizable design rules for creating “materials that compute”
