Sequential logic in soft, multistable, and electroactive materials

Abstract

Recent developments in soft autonomous matter strive to invest means for material intelligence whereby environmental stimuli is processed through unconventional computing methods. Combinational logic operations are recently explored through mechanologic techniques and reconfigurable integrated circuits in compliant materials. In this research we further advance information processing by introducing sequential logic in soft, and conductive mechanical materials with electroactive components. We develop a multilayer material platform to integrate electromechanical combinational logic layers with memory storage layers. Such non-volatile memory is introduced through structural multistability to obtain stable conductive network configurations. By employing liquid crystal elastomer components with joule heating elements in the material system, the memory bit layers are capable of self-control through the integrated circuit. We establish a mathematical method to program the mechanical computing platform and the conductive network based on Boolean characteristic functions. This design technique allows for the development of fundamental sequential logic operations such as flip flops, and counters. We also advance on such computing capabilities to demonstrate the basics of artificial intelligence through feedback processes. This research provides material systems composed of polymers with electroactive, conductive and multistable properties that can memorize and think about applied mechanical stress. Such findings in the fundamental multiphysics of information processing inspire a new class of soft autonomous matter with advanced computing capabilities.