Fifteen periods of Si/Si_0.7Ge_0.3 multilayers (MLs) with various SiGe thicknesses are grown on a 200 mm Si substrate using reduced pressure chemical vapor deposition (RPCVD). Several methods were utilized to characterize and analyze the ML structures. The high resolution transmission electron microscopy (HRTEM) results show that the ML structure with 20 nm Si_0.7Ge_0.3 features the best crystal quality and no defects are observed. Stacked Si_0.7Ge_0.3 ML structures etched by three different methods were carried out and compared, and the results show that they have different selectivities and morphologies. In this work, the fabrication process influences on Si/SiGe MLs are studied and there are no significant effects on the Si layers, which are the channels in lateral gate all around field effect transistor (L-GAAFET) devices. For vertically-stacked dynamic random access memory (VS-DRAM), it is necessary to consider the dislocation caused by strain accumulation and stress release after the number of stacked layers exceeds the critical thickness. These results pave the way for the manufacture of high-performance multivertical-stacked Si nanowires, nanosheet L-GAAFETs, and DRAM devices.

Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algorithms and supporting hardware. In recent years, the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models. This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance. Therefore, algorithm-hardware co-design has emerged as the primary methodology, which analyzes algorithm behaviors on hardware to identify common computational properties. These properties can motivate algorithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency. We then reviewed recent works on robotic and embodied AI algorithms and computing hardware to demonstrate this algorithm-hardware co-design methodology. In the end, we discuss future research opportunities by answering two questions: (1) how to adapt the computing platforms to the rapid evolution of embodied AI algorithms, and (2) how to transform the potential of emerging hardware innovations into end-to-end inference improvements.

Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it poses new challenges to system design in terms of computational speed and energy efficiency. The traditional von Neumann architecture cannot meet the requirements of heavily data-centric applications due to the separation of computation and storage. The emergence of computing in-memory (CIM) is significant in circumventing the von Neumann bottleneck. A commercialized memory architecture, static random-access memory (SRAM), is fast and robust, consumes less power, and is compatible with state-of-the-art technology. This study investigates the research progress of SRAM-based CIM technology in three levels: circuit, function, and application. It also outlines the problems, challenges, and prospects of SRAM-based CIM macros.

Significant progress has been achieved in the field of organic solar cells (OSCs). Most devices with power conversion efficiencies (PCEs) exceeding 20% rely predominantly on active materials that incorporate D18 or its derivatives as the donor. In contrast, the PCEs over 20% have been realized as well for OSCs with the non-D18-based donor materials by simultaneously optimizing material properties, active layer morphologies and interface engineering, thereby demonstrating the potential to outperform D18 counterparts. Therefore, this review summarizes an overview of recent advancements in OSCs with the PCEs over 20% utilizing the non-D18-based donor materials, and highlights three critical aspects including molecular design strategies, the active layer morphologies, and the interface optimization. Their synergistic roles are advantageous in enhancing the exciton dissociation, facilitating the charge transport, and suppressing the recombination losses, accordingly supporting the improved PCEs over 20%. Furthermore, the challenges and valuable insights are discussed, which can lead to improved efficiency, scalable fabrication, and enhanced environmental and thermal stability, potentially accelerating the commercialization of OSCs.

Currently, the global 5G network, cloud computing, and data center industries are experiencing rapid development. The continuous growth of data center traffic has driven the vigorous progress in high-speed optical transceivers for optical interconnection within data centers. The electro-absorption modulated laser (EML), which is widely used in optical fiber communications, data centers, and high-speed data transmission systems, represents a high-performance photoelectric conversion device. Compared to traditional directly modulated lasers (DMLs), EMLs demonstrate lower frequency chirp and higher modulation bandwidth, enabling support for higher data rates and longer transmission distances. This article introduces the composition, working principles, manufacturing processes, and applications of EMLs. It reviews the progress on advanced indium phosphide (InP)-based EML devices from research institutions worldwide, while summarizing and comparing data transmission rates and key technical approaches across various studies.

Since the invention of amorphous indium–gallium–zinc–oxide (IGZO) based thin-film transistors (TFTs) by Hideo Hosono in 2004, investigations on the topic of IGZO TFTs have been rapidly expanded thanks to their high electrical performance, large-area uniformity, and low processing temperature. This article reviews the recent progress and major trends in the field of IGZO-based TFTs. After a brief introduction of the history of IGZO and the main advantages of IGZO-based TFTs, an overview of IGZO materials and IGZO-based TFTs is given. In this part, IGZO material electron travelling orbitals and deposition methods are introduced, and the specific device structures and electrical performance are also presented. Afterwards, the recent advances of IGZO-based TFT applications are summarized, including flat panel display drivers, novel sensors, and emerging neuromorphic systems. In particular, the realization of flexible electronic systems is discussed. The last part of this review consists of the conclusions and gives an outlook over the field with a prediction for the future.

Digital to analog converters (DAC) play an important role as a bridge connecting the analog world and the digital world. With the rapid development of wireless communication, wideband digital radar, and other emerging technologies, better performing high-speed high-resolution DACs are required. In those applications, signal bandwidth and high-frequency linearity often limited by data converters are the bottleneck of the system. This article reviews the state-of-the-art technologies of high-speed and high-resolution DACs reported in recent years. Comparisons are made between different architectures, circuit implementations and calibration techniques along with the figure of merit (FoM) results.

For the non-stop demands for a better and smarter society, the number of electronic devices keeps increasing exponentially; and the computation power, communication data rate, smart sensing capability and intelligence are always not enough. Hardware supports software, while the integrated circuit (IC) is the core of hardware. In this long review paper, we summarize and discuss recent trending IC design directions and challenges, and try to give the readers big/cool pictures on each selected small/hot topics. We divide the trends into the following six categories, namely, 1) machine learning and artificial intelligence (AI) chips, 2) communication ICs, 3) data converters, 4) power converters, 5) imagers and range sensors, 6) emerging directions. Hope you find this paper useful for your future research and works.