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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. 

Despite the unique advantages of the memristive switching devices based on two-dimensional (2D) transition metal dichalcogenides, scalable growth technologies of such 2D materials and wafer-level fabrication remain challenging. In this work, we present the gold-assisted large-area physical vapor deposition (PVD) growth of Bi2Se3 features for the scalable fabrication of 2D-material-based crossbar arrays of memristor devices. This work indicates that gold layers, prepatterned by photolithography processes, can catalyze PVD growth of few-layer Bi2Se3 with 100-folds larger crystal grain size in comparison with that grown on bare Si/SiO2 substrates. We also present a fluid-guided growth strategy to improve growth selectivity of Bi2Se3 on Au layers. Through the experimental and computational analyses, we identify two key processing parameters, i.e., the distance between Bi2Se3 powder and the target substrate and the distance between the leading edges of the substrate and the substrate holder with a hollow interior, which plays a critical role in realizing large-scale growth. By optimizing these growth parameters, we have successfully demonstrated cm-scale highly-selective Bi2Se3 growth on crossbar-arrayed structures with an in-lab yield of 86%. The whole process is etch- and plasma-free, substantially minimizing the damage to the crystal structure and also preventing the formation of rough 2D-material surfaces. Furthermore, we also preliminarily demonstrated memristive devices, which exhibit reproducible resistance switching characteristics (over 50 cycles) and a retention time of up to 106 s. This work provides a useful guideline for the scalable fabrication of vertically arranged crossbar arrays of 2D-material-based memristive devices, which is critical to the implementation of such devices for practical neuromorphic applications. 

Memristors based on 2D semiconductors such as MoS2 and its derivative materials exhibit analog switching behaviors capable of emulating some synaptic functions, including short-term plasticity, long-term potentiation, and spike-time-dependent-plasticity. Additional investigation is needed to realize reliable control of such synaptic behaviors for practical device implementation. To meet this scientific need, we fabricated MoS2-based memristors and studied their paired-pulse facilitation (PPF) and long-term memory characteristics under different pulse programming settings. This research has provided a guideline for identifying the programming settings for different neuromorphic processes. For example, a specific setting resulting in PPF > 30% and long-term conductance change < 20% has been identified to be suited for processing real-time temporal information. Furthermore, this research also indicates that the MoS2 memristor keeps having an almost constant relative change in conductance but greatly enhanced drive current level under laser illumination. This behavior can enable an easy integration of such memristive devices with state-of-the-art controller circuits for practice neuromorphic control applications.