The rapid development of artificial intelligence (AI), big data analytics, cloud computing, and Internet of Things applications expect the emerging memristor devices and their hardware systems to solve massive data calculation with low power consumption and small chip area. This paper provides an overview of memristor device characteristics, models, synapse circuits, and neural network applications, especially for artificial neural networks and spiking neural networks. It also provides research summaries, comparisons, limitations, challenges, and future work opportunities.

Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non-intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this paper, a target-driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep-learning-based design methods for MMs which directly use the spectrum response to generate the MM’s design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure-of-merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces.

Actively controlling the amplitude and phase of terahertz (THz) wave is of great significance for THz broadband wireless communication and high-resolution THz imaging. In this paper, we present a THz amplitude and phase modulator based on electrically triggered vanadium dioxide (VO ₂ ) reconfigurable metasurface. The unit cell of the device consists of three concentric split-rings-rings (SRRs) on the sapphire substrate, i.e., one VO ₂ -split-ring (VSR) and two metal SRRs. The VSR is embedded between two metal SRRs. The resonance mode, resonance intensity and surface current distribution in the unit cell can be dynamically manipulated by electrically triggering the insulator-to-metal-transition (IMT) of the VO ₂ . The sample is fabricated using a surface micromachining process and characterized by a THz time-domain-spectroscopy (TDS) system. The experimental results show that, 90 degrees phase shift is achieved in a broad bandwidth of 70 GHz. Meanwhile, the amplitude modulation depth reaches 71% at 0.79 THz. The transmission and the phase shift are calculated by an equivalent circuit model, and simultaneously simulated using a full-wave circuit simulation software based on the Method of Moments (MoM). The calculation and simulation results agree with the experimental results. The device has potential applications in THz imaging, broadband wireless communications and array phase controlling.

The scattering enhancement technique has shown prominent potential in various regimes such as satellite communication, Radar Cross Section (RCS) camouflage, and remote sensing. Currently, the scattering enhancement devices based on the metasurface have shown advantages in light weight and better performance. These metasurfaces always possess complex structure, it is hard to achieve through the tradition trial-and-error method which relies on the full-wave numerical simulation. In this paper, a new method combining the machine learning and the evolution optimization algorithm is proposed to design the metasurface retroreflector (MRF) for arbitrary direction incident wave. In this method, a predicting model and a generative inverse design model are constructed and trained, the predicting model is used to evaluate the fitness of each offspring in the genetic algorithm (GA), the generative model is used to initialize the first offspring of the GA by inverse generate the MRF based on the requirements of the designer. With the assistance of these two machine learning models, the evolution optimization algorithm is employed to find the optimal design of the MRF. This approach enables automatic solution of electromagnetic inverse design problems and opens the way to facilitate the optimization of other metadevices.

The portable real-time electrocardiogram (ECG) is a convenient and promising electronic device for cardiovascular diseases patients. However, unlike wet gel electrodes in traditional clinical applications, dry electrodes are competent for comfortable long-time wearing and can prevent skin ulceration. Its ultra-high source impedance and electrode offset (EOS) make traditional chopper amplifiers with low input impedance and limited EOS range difficult to apply to this area. To overcome these challenges, this paper proposes a novel chopper amplifier topology. This architecture includes a gain control loop, a ripple reduction loop, and a DC-servo loop (DSL). The proposed sampling input stage and digital-analog hybrid DSL are employed to boost input impedance and extend the EOS handing range. Designed with a 0.18 µm 1P6M 1.8 V CMOS salicide process, the proposed chopper capacitively coupled instrumentation amplifier achieves an ultra-high input impedance of 120 GΩ (<0.05 Hz) or 2.1 GΩ (0.6~250 Hz), an EOS handing range of ±325 mV and a low noise of 1.9 μVrms at 0.6~250 Hz. It occupies an area of 0.36 mm² and only consumes a quiescent current of 11 μA.