Applications of Deep reinforcement learning in MEMS and nanotechnology

Deep reinforcement learning (DRL) is an artificial intelligence technique that allows agents to learn optimal behaviors through trial-and-error interactions with their environment. This paper reviews applications of DRL in the fields of micro-electro-mechanical systems (MEMS) and nanotechnology. DRL has been used to enhance the design, manufacturing, and control of micro- and nanoscale systems. Notable applications include optimizing MEMS device designs, controlling nanomaterial synthesis, enabling precise nanorobotic manipulation, automating nanofabrication, directing nanoparticle self-assembly, and optimizing MEMS/nanotechnology fabrication processes. DRL allows for greater precision, increased autonomy, and enhanced performance. However, challenges remain regarding computational complexity, data availability, and responsible AI adoption. Continued DRL research and development focused on micro- and nanoscale systems hold promise for transformative innovations in electronics, medicine, energy, and other domains.