Journal of Electronic Science and Technology最新文献

Quantum-dot cellular automata (QCA) is an emerging computational paradigm which can overcome scaling limitations of the existing complementary metal oxide semiconductor (CMOS) technology. The existence of defects cannot be ignored, considering the fabrication of QCA devices at the molecular level where it could alter the functionality. Therefore, defects in QCA devices need to be analyzed. So far, the simulation-based displacement defect analysis has been presented in the literature, which results in an increased demand in the corresponding mathematical model. In this paper, the displacement defect analysis of the QCA main primitive, majority voter (MV), is presented and carried out both in simulation and mathematics, where the kink energy based mathematical model is applied. The results demonstrate that this model can also be valid for the displacement defect in QCA MV.

For the anti-interference/denoise purpose, it usually requires minimizing the sidelobe level (SLL) of a wide-beam pattern with a desired low nulling level (NL) in the nulling region. To realize such an objective, the shaped-beam pattern synthesis (SBPS) is the most commonly used approach. However, since the SBPS problem focuses on synthesizing a predetermined beam shape, the minimum SLL via this approach cannot ensure to obtain the maximum power gain. Conversely, it cannot obtain the lowest SLL with a certain power gain requirement. Based on such consideration, this paper tries to further minimize SLL of a wide-beam pattern with a desired low NL nulling region, by solving the power gain pattern synthesis (PGPS) problem. The PGPS problem selects the array excitation by directly optimizing the power gain. Hence, it has the potential to reduce SLL, when achieving the equal mainlobe power gain constraint via SBPS. An iterative algorithm which converts the primal optimization problem into convex sub-problems is proposed, resulting in an effective problem-solving scheme. Numerical simulations demonstrate the proposed algorithm can obtain about 10-dB lower SLL than the existing algorithms.

This paper proposes low-cost yet high-accuracy direction of arrival (DOA) estimation for the automotive frequency-modulated continuous-wave (FMCW) radar. The existing subspace-based DOA estimation algorithms suffer from either high-computational costs or low accuracy. We aim to solve such contradictory relation between complexity and accuracy by using randomized matrix approximation. Specifically, we apply an easily-interpretable randomized low-rank approximation to the covariance matrix (CM) and approximately compute its subspaces. That is, we first approximate CM $R$ $\in {ℂ}^{M\times M}$ through three sketch matrices, in the form of $R\approx QB{Q}^H$, here the matrix $Q\in {ℂ}^{M\times z}$ contains the orthonormal basis for the range of the sketch matrix $C\in {ℂ}^{M\times z}$ which is extracted from $R$ using randomized uniform column sampling and $B\in {ℂ}^{z\times z}$ is a weight-matrix reducing the approximation error. Relying on such approximation, we are able to accelerate the subspace computation by the orders of the magnitude without compromising estimation accuracy. Furthermore, we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation. As validated by the simulation results, the DOA estimation accuracy of the proposed algorithm, efficient multiple signal classification (E-MUSIC), is high, closely tracks standard MUSIC, and outperforms the well-known algorithms with tremendously reduced time complexity. Thus, the devised method can realize high-resolution real-time target detection in the emerging multiple input and multiple output (MIMO) automotive radar systems.

A compressive near-field millimeter wave (MMW) imaging algorithm is proposed. From the compressed sensing (CS) theory, the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data. The Gini index (GI) has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates, and Babies. By combining the total variation (TV) operator, the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed. In addition, the corresponding algorithm based on a primal-dual framework is also proposed. Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l₁-TV mixed regularization algorithm.

Colloidal quantum dots (CQDs) are of great interest for photovoltaic (PV) technologies as they possess the benefits of solution-processability, size-tunability, and roll-to-roll manufacturability, as well as unique capabilities to harvest near-infrared (NIR) radiation. During the last decade, lab-scale CQD solar cells have achieved rapid improvement in the power conversion efficiency (PCE) from ∼1% to 18%, which will potentially exceed 20% in the next few years and approach the performance of other PV technologies, such as perovskite solar cells and organic solar cells. In the meanwhile, CQD solar cells exhibit long lifetimes either under shelf storage or continuous operation, making them highly attractive to industry. However, in order to meet the industrial requirements, mass production techniques are necessary to scale up the fabrication of those lab devices into large-area PV modules, such as roll-to-toll coating. This paper reviews the recent developments of large-area CQD solar cells with a focus on various fabrication methods and their principles. It covers the progress of typical large-area coating techniques, including spray coating, blade coating, dip coating, and slot-die coating. It also discusses next steps and new strategies to accomplish the ultimate goal of the low-cost large-area fabrication of CQD solar cells and emphasizes how artificial intelligence or machine learning could facilitate the developments of CQD solar cell research.

The spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore's Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons, and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.

The direction-of-arrival (DOA) estimation problem can be solved by the methods based on sparse Bayesian learning (SBL). To assure the accuracy, SBL needs massive amounts of snapshots which may lead to a huge computational workload. In order to reduce the snapshot number and computational complexity, a randomize-then-optimize (RTO) algorithm based DOA estimation method is proposed. The “learning” process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hastings process in the RTO algorithm. To apply the RTO algorithm for a Laplace prior, a prior transformation technique is induced. To demonstrate the effectiveness of the proposed method, several simulations are proceeded, which verifies that the proposed method has better accuracy with 1 snapshot and shorter processing time than conventional compressive sensing (CS) based DOA methods.

Wide-bandgap gallium oxide (Ga₂O₃) is one of the most promising semiconductor materials for solar-blind (200 ​nm–280 ​nm) photodetection. In its amorphous form, a-Ga₂O₃ maintains its intrinsic optoelectronic properties while can be prepared at a low growth temperature, thus it is compatible with Si integrated circuits (ICs) technology. Herein, the a-Ga₂O₃ film is directly deposited on pre-fabricated Au interdigital electrodes by plasma enhanced atomic layer deposition (PE-ALD) at a growth temperature of 250 ​°C. The stoichiometric a-Ga₂O₃ thin film with a low defect density is achieved owing to the mild PE-ALD condition. As a result, the fabricated Au/a-Ga₂O₃/Au photodetector shows a fast time response, high responsivity, and excellent wavelength selectivity for solar-blind photodetection. Furthermore, an ultra-thin MgO layer is deposited by PE-ALD to passivate the Au/a-Ga₂O₃/Au interface, resulting in the responsivity of 788 A/W (under 254 ​nm at 10 ​V), a 250-nm-to-400-nm rejection ratio of 9.2 ​× ​10³, and the rise time and the decay time of 32 ​ms and 6 ​ms, respectively. These results demonstrate that the a-Ga₂O₃ film grown by PE-ALD is a promising candidate for high-performance solar-blind photodetection and potentially can be integrated with Si ICs for commercial production.

When an inaudible sound covert channel (ISCC) attack is launched inside a computer system, sensitive data are converted to inaudible sound waves and then transmitted. The receiver at the other end picks up the sound signal, from which the original sensitive data can be recovered. As a forceful countermeasure against the ISCC attack, strong noise can be used to jam the channel and literally shut down any possible sound data transmission. In this paper, enhanced ISCC whose transmission frequency can be dynamically changed is proposed. Essentially, if the transmitter detects that the covert channel is being jammed, the transmitter and receiver both will switch to another available frequency and re-establish their communications, following the proposed communications protocol. Experimental results show that the proposed enhanced ISCC can remain connected even in the presence of a strong jamming noise source. Correspondingly, a detection method based on frequency scanning is proposed to help to combat such an anti-jamming sound channel. With the proposed countermeasure, the bit error rate (BER) of the data communications over enhanced ISCC soars to more than 48%, essentially shutting down the data transmission, and thus neutralizing the security threat.

This article presents a modeling and simulation method for transient thermal analyses of integrated circuits (ICs) using the original and voltage-in-current (VinC) latency insertion method (LIM). LIM-based algorithms are a set of fast transient simulation methods that solve electrical circuits in a leapfrog updating manner without relying on large matrix operations used in conventional Simulation Program with Integrated Circuit Emphasis (SPICE)-based methods which can significantly slow down the solution process. The conversion from the thermal to electrical model is performed first by using the analogy between heat and electrical conduction. Since electrical inductance has no thermal equivalence, a modified VinC LIM formulation is presented which removes the requirement of the insertion of fictitious inductors. Numerical examples are presented which show that the modified VinC LIM formulation outperforms the basic LIM formulation, both in terms of stability and accuracy in the transient thermal simulation of ICs.