Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed, wide bandwidth, and massive parallelism. Here, we offer a review on the optical neural computing in our research groups at the device and system levels. The photonics neuron and photonics synapse plasticity are presented. In addition, we introduce several optical neural computing architectures and algorithms including photonic spiking neural network, photonic convolutional neural network, photonic matrix computation, photonic reservoir computing, and photonic reinforcement learning. Finally, we summarize the major challenges faced by photonic neuromorphic computing, and propose promising solutions and perspectives.

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We propose a modified supervised learning algorithm for optical spiking neural networks, which introduces synaptic time-delay plasticity on the basis of traditional weight training. Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity. A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method. Moreover, the proposed algorithm is also applied to two benchmark data sets for classification. In a simple network structure with only a few optical neurons, the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning. The introduction of delay adjusting improves the learning efficiency and performance of the algorithm, which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.

This paper studies the problem of spatial path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties and input saturation taken into account. Initially, the reduced-order extended state observes (ESOs) are introduced to estimate and compensate all lumped uncertainties due to the model parameters perturbations, unmodeled dynamics, environmental disturbances, and nonlinear hydrodynamic damping terms. Furthermore, the spatial path-following control strategy is established by combining with backstepping, integral sliding mode control, and estimator to cope with the position, attitude, and linear velocity tracking of the AUV. Specifically, the dynamic surface control (DSC) technique is utilized to achieve satisfactory differential performance and avoid the ''explosion of complexity.'' Additionally, the auxiliary dynamic compensator is presented to analyze the effect of input saturation, and the states of the auxiliary design system are used to develop the controller. It is easily proved that the proposed control scheme can guarantee that all signals of the closed-loop system are globally stable through the Lyapunov theorem, with the tracking errors converging to an arbitrarily small neighborhood of zero. Finally, the numerical simulation results are carried out to illustrate the effectiveness of the proposed controller.INDEX TERMS AUV, path-following, multiple uncertainties, input saturation, time-varying sideslip angle.

We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers (SLs) that generate triple-channel chaotic signals with time delayed signature (TDS) concealment. The effects of the coupling strength and bias current on the concealment of the TDS are investigated. The generated chaotic signals are further applied to reinforcement learning, and a parallel scheme is proposed to solve the multiarmed bandit (MAB) problem. The influences of mutual correlation between signals from different channels, the sampling interval of signals, and the TDS concealment on the performance of decision making are analyzed. Comparisons between the proposed scheme and two existing schemes show that, with a simplified algorithm, the proposed scheme can perform as well as the previous schemes or even better. Moreover, we also consider the robustness of decision making performance against a dynamically changing environment and verify the scalability for MAB problems with different sizes. This proposed globally coupled SL network for a multi-channel chaotic source is simple in structure and easy to implement. The attempt to solve the MAB problem in parallel can provide potential values in the realm of the application of ultrafast photonics intelligence.

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