Toward memristive in-memory computing: principles and applications

MEMS Oscillators-Network-Based Ising Machine with Grouping Method

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

Oxovanadium electronics for in-memory, neuromorphic, and quantum computing applications

Vanadium is a critical raw material. In the nearby future, it may, however, become one of the key elements of computer devices based on two-dimensional arrays of spin qubits for quantum information processing or charge- and resistance-based data memory cells for non-volatile in-memory and neuromorphic computing. The research and development (R&D) of vanadium-containing electronic materials and methods for their responsible fabrication underpins the transition to innovative hybrid semiconductors for energy- and resource-efficient memory and information processing technologies. The combination of standard and emerging solid-state semiconductors with stimuli-responsive oxo complexes of vanadium(IV,V) is envisioned to result in electronics with a new room-temperature device nanophysics, and the ability to modulate and control it at the sub-nanometer level. The development of exponential (Boolean) logics based on the oxovanadium-comprising circuitry and crossbar arrays of individual memristive cells for in-memory computing, the implementation of basic synaptic functions via dynamic electrical pulses for neuromorphic computing, and the readout and control of spin networks and interfaces for quantum computing are strategically important future areas of molecular chemistry and applied physics of vanadium.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

Low-dimensional nanostructures for monolithic 3D-integrated flexible and stretchable electronics

Flexible/stretchable electronics, which are characterized by their ultrathin design, lightweight structure, and excellent mechanical robustness and conformability, have garnered significant attention due to their unprecedented potential in healthcare, advanced robotics, and human-machine interface technologies. An increasing number of low-dimensional nanostructures with exceptional mechanical, electronic, and/or optical properties are being developed for flexible/stretchable electronics to fulfill the functional and application requirements of information sensing, processing, and interactive loops. Compared to the traditional single-layer format, which has a restricted design space, a monolithic three-dimensional (M3D) integrated device architecture offers greater flexibility and stretchability for electronic devices, achieving a high-level of integration to accommodate the state-of-the-art design targets, such as skin-comfort, miniaturization, and multi-functionality. Low-dimensional nanostructures possess small size, unique characteristics, flexible/elastic adaptability, and effective vertical stacking capability, boosting the advancement of M3D-integrated flexible/stretchable systems. In this review, we provide a summary of the typical low-dimensional nanostructures found in semiconductor, interconnect, and substrate materials, and discuss the design rules of flexible/stretchable devices for intelligent sensing and data processing. Furthermore, artificial sensory systems in 3D integration have been reviewed, highlighting the advancements in flexible/stretchable electronics that are deployed with high-density, energy-efficiency, and multi-functionalities. Finally, we discuss the technical challenges and advanced methodologies involved in the design and optimization of low-dimensional nanostructures, to achieve monolithic 3D-integrated flexible/stretchable multi-sensory systems.

Self-Rectifying Memristors for Three-Dimensional In-Memory Computing

Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102  Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.