Multipath-controlled bidirectional metasurface for multitasking polarization regulation and absorption.

In the design of metasurfaces, integrating multiple tasks into a single small unit cell and achieving regulation through various paths pose a serious challenge. In this paper, a multipath-controlled bidirectional metasurface (MCBM) is designed to achieve polarization regulation, perfect absorption and total reflection as multitasking functions. The findings demonstrate that under different excitation conditions, when co-planar polarized terahertz (THz) waves are incident normally on the metasurface, the MCBM can convert co-planar polarization to cross-polarization, co-planar polarization to circular polarization wave in reflection mode, and co-planar polarization to cross-polarization in transmission, respectively. When co-planar polarized THz waves are incident from the back side of the metasurface, the tasks of MCBM change to broadband perfect absorption, total reflection, and transmission co-planar polarization to cross-polarization conversion. Remarkably, all operating frequency bands of these tasks are very approximate. Additionally, the multitasking functions can be switched by altering the excitation conditions, and their performance can be regulated through multipath controls, such as the temperature, voltage, and polarization status. Our design provides an effective strategy for multipath-controlled multitasking integrated devices in the THz band.

Thin-film transistor for temporal self-adaptive reservoir computing with closed-loop architecture.

Reservoir computing is a powerful neural network-based computing paradigm for spatiotemporal signal processing. Recently, physical reservoirs have been explored based on various electronic devices with outstanding efficiency. However, the inflexible temporal dynamics of these reservoirs have posed fundamental restrictions in processing spatiotemporal signals with various timescales. Here, we fabricated thin-film transistors with controllable temporal dynamics, which can be easily tuned with electrical operation signals and showed excellent cycle-to-cycle uniformity. Based on this, we constructed a temporal adaptive reservoir capable of extracting temporal information of multiple timescales, thereby achieving improved accuracy in the human-activity-recognition task. Moreover, by leveraging the former computing output to modify the hyperparameters, we constructed a closed-loop architecture that equips the reservoir computing system with temporal self-adaptability according to the current input. The adaptability is demonstrated by accurate real-time recognition of objects moving at diverse speed levels. This work provides an approach for reservoir computing systems to achieve real-time processing of spatiotemporal signals with compound temporal characteristics.

Memristor-based storage system with convolutional autoencoder-based image compression network.

The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and speed of the image compression/retrieval and improve the storage density. We adopt the 4-bit memristor arrays to experimentally demonstrate the functions of the system. We propose a step-by-step quantization aware training scheme and an equivalent transformation for transpose convolution to improve the system performance. The system exhibits a high (>33 dB) peak signal-to-noise ratio in the compression and decompression of the ImageNet and Kodak24 datasets. Benchmark comparison results show that the 4-bit memristor-based storage system could reduce the latency and energy consumption by over 20×/5.6× and 180×/91×, respectively, compared with the server-grade central processing unit-based/the graphics processing unit-based processing system, and improve the storage density by more than 3 times.

Flash-based content addressable memory with L2 distance for memory-augmented neural network.

Memory-augmented neural network (MANN) has received increasing attention as a promising approach to achieve lifelong on-device learning, of which implementation of the explicit memory is vital. Content addressable memory (CAM) has been designed to accelerate the explicit memory by harnessing the in-memory-computing capability. In this work, a CAM cell with quadratic code is proposed, and a 1Mb Flash-based multi-bit CAM chip capable of computing Euclidean (L2) distance is fabricated. Compared with ternary CAM, the latency and energy are significantly reduced by 5.3- and 46.6-fold, respectively, for the MANN on Omniglot dataset. Besides, the recognition accuracy has slight degradation (<1%) even after baking for 105 s at 200°C, demonstrating the robustness to environmental disturbance. Performance evaluation indicates a reduction of 471-fold in latency and 1267-fold in energy compared with GPU for search operation. The proposed robust and energy-efficient CAM provides a promising solution to implement lifelong on-device machine intelligence.

Adjustable short-term memory of SiOx:Ag-based memristor for reservoir computing.

Temporal information processing is critical for a wide spectrum of applications, such as finance, biomedicine, and engineering. Reservoir computing (RC) can efficiently process temporal information with low training costs. Various memristors have been explored to demonstrate RC systems leveraging the short-term memory and nonlinear dynamic behaviours. However, the short-term memory is fixed after the device fabrication, limiting the applications to diverse temporal analysis tasks. In this work, we propose the approaches to modulating the short-term memory of Pt/SiOx:Ag/Pt memristor for the performance improvement of the RC systems. By controlling the read voltage, pulse amplitude and pulse width applied to the devices, the obtainable range of the characteristic time reaches three orders of magnitude from microseconds to around milliseconds. Based on the fabricated memristor, the classification of 4-bit pulse streams is demonstrated. Memristor-based RC systems with adjustable short-term memory are constructed for time-series prediction and pattern recognition tasks with different requirements for the characteristic times. The simulation results show that low normalized root mean square error of 0.003 (0.27) in Hénon map (Mackey-Glass time series) and excellent classification accuracy of 99.6% (91.7%) in spoken-digit recognition (MNIST image recognition) are achieved, which outperforms most memristor-based RC systems recently reported. Furthermore, the RC networks with diverse short-term memories are constructed to address more complicated tasks with low prediction errors. This work proves the high controllability of memristor-based RC systems to handle multiple temporal processing tasks.

Multifunctional-hierarchical flexibility metasurfaces for multispectral compatible camouflage of microwave, infrared and visible.

The prevalent use of multispectral detection technology makes single-band camouflage devices ineffective, and the investigation of technology for camouflage that combines multispectral bands becomes urgent. The multifunctional-hierarchical flexibility metasurfaces (MHFM) for multispectral compatible camouflage of microwave, infrared, and visible, is proposed, fabricated, and measured. MHFM is primarily composed of an infrared shielding layer (IRSL), a radar absorbing layer (RAL), and a visible color layer (VCL). Among them, IRSL can block thermal infrared detection, and RAL can efficiently absorb microwave band electromagnetic (EM) waves. The VLC can display black (below 28°C), purple (28°C∼31°C), green (31°C∼33°C), and yellow (above 33°C) at different temperatures to achieve visible camouflage. Simulation results show that MHFM can achieve absorption higher than 90% in the 2.9∼13.9 GHz microwave band. Theoretically, the emissivity of MHFM in the infrared spectral range 3∼14 µm is less than 0.34. In addition, the MHFM consists of high-temperature-resistant materials that can be used normally at temperatures up to 175°C, providing excellent high-temperature stability. The measurement results show that the camouflage performance of the MHFM is in excellent agreement with the proposed theory. This study proposes a new method for multispectral camouflage that has broad engineering applications.

Optoelectronic graded neurons for bioinspired in-sensor motion perception.

Motion processing has proven to be a computational challenge and demands considerable computational resources. Contrast this with the fact that flying insects can agilely perceive real-world motion with their tiny vision system. Here we show that phototransistor arrays can directly perceive different types of motion at sensory terminals, emulating the non-spiking graded neurons of insect vision systems. The charge dynamics of the shallow trapping centres in MoS2 phototransistors mimic the characteristics of graded neurons, showing an information transmission rate of 1,200 bit s-1 and effectively encoding temporal light information. We used a 20 × 20 photosensor array to detect trajectories in the visual field, allowing the efficient perception of the direction and vision saliency of moving objects and achieving 99.2% recognition accuracy with a four-layer neural network. By modulating the charge dynamics of the shallow trapping centres of MoS2, the sensor array can recognize motion with a temporal resolution ranging from 101 to 106 ms.

Semi-syntheses and interrogation of indole-substituted Aspidosperma terpenoid alkaloids.

We demonstrated here a series of Aspidosperma terpenoid alkaloids can be quickly prepared using semisynthesis from naturally sourced tabersonine, featuring multiple oxygen-based substituents on the indole ring such as hydroxy and methoxy groups. This panel of complex compounds enabled the exploration of indole modifications to optimize the indole alkaloids' anticancer activity, generating lead compounds (e.g., with C15-hydroxy, C16-methoxy, and/or C17-methoxy derivatizations) that potently inhibit cancer cell line growth in the single-digit micromolar range. These results can help guide the development of Aspidosperma terpenoid alkaloid therapeutics. Furthermore, this synthetic approach features late-stage facile derivatization on complex natural product molecules, providing a versatile path to indole derivatization of this family of alkaloids with diverse chemical functionalities for future medicinal chemistry and chemical biology discoveries.

Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses.

Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remarkable energy efficiency. Memristor is considered as one of the most promising candidates for the electronic synapse of the neuromorphic computing system due to its scalability, power efficiency and capability to simulate biological behaviors. Several memristor-based hardware demonstrations have been explored to achieve the capacity of unsupervised learning with the spike-rate-dependent plasticity (SRDP) learning rule. However, the learning capacity is limited and few of the memristor-based hardware demonstrations have explored the online unsupervised learning at the network level with an SRDP algorithm. Here, we construct a memristor-based hardware system and demonstrate the online unsupervised learning of SRDP networks. The neuromorphic system consists of multiple memristor arrays as the synapse and the discrete CMOS circuit unit as the neuron. Unsupervised learning and online weight update of 10 MNIST handwritten digits are realized by the constructed SRDP networks, and the recognition accuracy is above 90% with 20% device variation. This work paves the way towards the realization of large-scale and efficient networks for more complex tasks.

Synthesis of (-)-Melodinine K: A Case Study of Efficiency in Natural Product Synthesis.

Efficiency is a key organizing principle in modern natural product synthesis. Practical criteria include time, cost, and effort expended to synthesize the target, which tracks with step-count and scale. The execution of a natural product synthesis, that is, the sum and identity of each reaction employed therein, falls along a continuum of chemical (abiotic) synthesis on one extreme, followed by the hybrid chemoenzymatic approach, and ultimately biological (biosynthesis) on the other, acknowledging the first synthesis belongs to Nature. Starting materials also span a continuum of structural complexity approaching the target with constituent elements on one extreme, followed by petroleum-derived and "chiral pool" building blocks, and complex natural products (i.e., semisynthesis) on the other. Herein, we detail our approach toward realizing the first synthesis of (-)-melodinine K, a complex bis-indole alkaloid. The total syntheses of monomers (-)-tabersonine and (-)-16-methoxytabersonine employing our domino Michael/Mannich annulation is described. Isolation of (-)-tabersonine from Voacanga africana and strategic biotransformation with tabersonine 16-hydroxylase for site-specific C-H oxidation enabled a scalable route. The Polonovski-Potier reaction was employed in biomimetic fragment coupling. Subsequent manipulations delivered the target. We conclude with a discussion of efficiency in natural products synthesis and how chemical and biological technologies define the synthetic frontier.

Optoelectronic resistive random access memory for neuromorphic vision sensors.

Neuromorphic visual systems have considerable potential to emulate basic functions of the human visual system even beyond the visible light region. However, the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges in terms of device integration and power consumption. Here we show simple two-terminal optoelectronic resistive random access memory (ORRAM) synaptic devices for an efficient neuromorphic visual system that exhibit non-volatile optical resistive switching and light-tunable synaptic behaviours. The ORRAM arrays enable image sensing and memory functions as well as neuromorphic visual pre-processing with an improved processing efficiency and image recognition rate in the subsequent processing tasks. The proof-of-concept device provides the potential to simplify the circuitry of a neuromorphic visual system and contribute to the development of applications in edge computing and the internet of things.

Low-Power Resistive Switching Characteristic in HfO2/TiOx Bi-Layer Resistive Random-Access Memory.

Resistive random-access memory devices with atomic layer deposition HfO2 and radio frequency sputtering TiOx as resistive switching layers were fabricated successfully. Low-power characteristic with 1.52 µW set power (1 µA@1.52 V) and 1.12 µW reset power (1 µA@1.12 V) was obtained in the HfO2/TiOx resistive random-access memory (RRAM) devices by controlling the oxygen content of the TiOx layer. Besides, the influence of oxygen content during the TiOx sputtering process on the resistive switching properties would be discussed in detail. The investigations indicated that "soft breakdown" occurred easily during the electrical forming/set process in the HfO2/TiOx RRAM devices with high oxygen content of the TiOx layer, resulting in high resistive switching power. Low-power characteristic was obtained in HfO2/TiOx RRAM devices with appropriately high oxygen vacancy density of TiOx layer, suggesting that the appropriate oxygen vacancy density in the TiOx layer could avoid "soft breakdown" through the whole dielectric layers during forming/set process, thus limiting the current flowing through the RRAM device and decreasing operating power consumption.

Negative Differential Resistance Effect in Ru-Based RRAM Device Fabricated by Atomic Layer Deposition.

In this work, Ru-based RRAM devices with atomic layer deposited AlOy/HfOx functional layer were fabricated and studied. A negative differential resistance (NDR) behavior was observed during the voltage set process, and its physical origin was explored. Based on the physics understanding of the resistive switching, the measured NDR behavior is believed to be associated with the partially unipolar reset effect, which is due to the recombination between oxygen vacancies and the thermally released oxygen ions from the RuO2 interface layer. The measured electrical characteristics and X-ray photoelectron spectroscopy (XPS) results verified the physical interpretation.

The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition.

A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition.

Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today's electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

Direct Observations of Nanofilament Evolution in Switching Processes in HfO2 -Based Resistive Random Access Memory by In Situ TEM Studies.

Resistive switching processes in HfO2 are studied by electron holography and in situ energy-filtered imaging. The results show that oxygen vacancies are gradually generated in the oxide layer under ramped electrical bias, and finally form several conductive channels connecting the two electrodes. It also shows that the switching process occurs at the top interface of the hafnia layer.

Demonstration of Logic Operations in High-Performance RRAM Crossbar Array Fabricated by Atomic Layer Deposition Technique.

In this paper, resistive random access memory (RRAM)-based crossbar arrays with the cell structure of Pt/[AlO y /HfO x ] m /TiN were fabricated by using atomic layer deposition (ALD) technique. The RRAM devices in the arrays show excellent performances such as good uniformity and high reliability. Based on the fabricated RRAM array, a complete set of basic logic operations including NOR and XNOR were successfully demonstrated.

Reconfigurable Nonvolatile Logic Operations in Resistance Switching Crossbar Array for Large-Scale Circuits.

Resistance switching (RS) devices have potential to offer computing and memory function. A new computer unit is built of RS array, where processing and storing of information occur on same devices. Resistance states stored in devices located in arbitrary positions of RS array can be performed various nonvolatile logic operations. Logic functions can be reconfigured by altering trigger signals.

Disturbance characteristics of half-selected cells in a cross-point resistive switching memory array.

Disturbance characteristics of cross-point resistive random access memory (RRAM) arrays are comprehensively studied in this paper. An analytical model is developed to quantify the number of pulses (#Pulse) the cell can bear before disturbance occurs under various sub-switching voltage stresses based on physical understanding. An evaluation methodology is proposed to assess the disturb behavior of half-selected (HS) cells in cross-point RRAM arrays by combining the analytical model and SPICE simulation. The characteristics of cross-point RRAM arrays such as energy consumption, reliable operating cycles and total error bits are evaluated by the methodology. A possible solution to mitigate disturbance is proposed.

Discovery of an Orally Active and Liver-Targeted Prodrug of 5-Fluoro-2'-Deoxyuridine for the Treatment of Hepatocellular Carcinoma.

We report a series of novel O-(substituted benzyl) phosphoramidate prodrugs of 5-fluoro-2'-deoxyuridine for the treatment of hepatocellular carcinoma. Through structure optimization, the o-methylbenzyl analog (1t) was identified as an orally bioavailable and liver-targeted lead compound. This lead prodrug is well-tolerated at a dose up to 3 g/kg in Kuming mice via oral administration. An efficacy study demonstrated that it possesses good inhibitory effect (61.67% and 72.50%, respectively) on tumor growth in a mouse xenograft model. A metabolism study in Sprague-Dawley rats suggested that 1t can release the desired 5'-monophosphate in the liver with high liver-targeting index.