Aligned arrays of semiconducting carbon nanotubes (s-CNTs) with high homogenous density and orientation are urgently needed for high-performance carbon-based electronics. Herein, a length-controlled approach using combined technologies was developed to regulate the s-CNT length and reduce the length distribution. The impact of different lengths and length distributions was studied during aligned self-assembly on a liquid–liquid confined interface was investigated. The results show that short s-CNTs with a narrow distribution have the best alignment uniformity over the large scale. The optimized and aligned s-CNT array can reach a density as high as 100 CNTs·μm⁻¹ on a 4-inch wafer. The field-effect transistor (FET) performance of these optimized s-CNT arrays was 64% higher than arrays without length-control. This study clarified that rational control of s-CNTs with desired length and length distribution on the aligned self-assembly process within the liquid–liquid confined interface. The results illustrate a solid foundation for the application of emerging carbon-based electronics.

The rapid growth of the Internet of Things (IoTs) has resulted in an explosive increase in data, and thus has raised new challenges for data processing units. Edge computing, which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud, can reduce the amount of data for transmission and is a promising solution to address the challenges. One of the potential candidates for edge computing is a memristor, an emerging nonvolatile memory device that has the capability of in-memory computing. In this article, from the perspective of edge computing, we review recent progress on memristor-based signal processing methods, especially on the aspects of signal preprocessing and feature extraction. Then, we describe memristor-based signal classification and regression, and end-to-end signal processing. In all these applications, memristors serve as critical accelerators to greatly improve the overall system performance, such as power efficiency and processing speed. Finally, we discuss existing challenges and future outlooks for memristor-based signal processing systems.