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2D-layered materials (e.g., graphene and transition metal dichalcogenides) have attracted huge attention due to their unique mechanical and electrical properties. Emerging research efforts, which seek to combine device characterization and high-resolution electron micrography analysis for 2D-layered device features, demand nano/microlithographic techniques capable of producing ordered 2D material patterns on ultrathin membranes with nanoscale thicknesses. However, such membranes are so fragile that most conventional lithographic techniques can be hardly performed on them to generate 2D material patterns. Our previous works have demonstrated that the rubbing-induced site-selective (RISS) deposition method can produce arbitrary 2D semiconductor (e.g., MoS2 and Bi2Se3) patterns on regular device substrates. This fabrication route prevents the vulnerable 2D-layered structures from the detrimental damage introduced by plasma etching and resist-based lithography processes. In this work, we explore the applicability of RISS for directly producing 2D material patterns on nanomembranes. Specifically, this work shows that a polymeric interfacing layer on the rubbing template features, which can effectively prevent stress concentration during the rubbing process, is crucial to successful implementation of RISS processes on nanomembranes. Furthermore, we carried out the mechanics simulation of the Von Mises stress and pressure distribution on the RISS-processed membrane to identify the optimal rubbing load, which can generate sufficient triboelectric charge for material deposition but no damage to the membrane. Using this approach, we have successfully demonstrated the deposition of Bi2Se3 patterns on 25 nm SiOx nanomembranes and high-resolution transmission electron micrography characterization of the crystallographic structures. 

Traditional robotic vehicle control algorithms, implemented on digital devices with firmware, result in high power consumption and system complexity. Advanced control systems based on different device physics are essential for the advancement of sophisticated robotic vehicles and miniature mobile robots. Here, we present a nanoelectronics-enabled analog control system mimicking conventional controllers’ dynamic responses for real-time robotic controls, substantially reducing training cost, power consumption, and footprint. This system uses a reservoir computing network with interconnected memristive channels made from layered semiconductors. The network’s nonlinear switching and short-term memory characteristics effectively map input sensory signals to high-dimensional data spaces, enabling the generation of motor control signals with a simply trained readout layer. This approach minimizes software and analog-to-digital conversions, enhancing energy and resource efficiency. We demonstrate this system with two control tasks: rover target tracking and drone lever balancing, achieving similar performance to traditional controllers with ~10-microwatt power consumption. This work paves the way for ultralow-power edge computing in miniature robotic systems.