Direct access to furan and cyclopropane derivatives via palladium-catalyzed C-H activation/alkene insertion/annulation.

A practical and effective palladium-catalyzed C-H activation/alkene insertion/annulation has been reported for the synthesis of furans and cyclopropanes from cyclic 1,3-diketones or 1,3-indandione and diverse alkenes, resulting in moderate to good yields. This protocol demonstrates excellent selectivity and is well-compatible with a wide range of alkene substrates, exhibiting exceptional regioselectivities, high efficiency, and good functional group tolerance.

Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing.

Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)-based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (<0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors.

Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Pd(II)-Catalyzed tandem selective dehydrogenative [4+2] annulation of 2-methyl-1,3-cycloalkanediones with olefins.

A practical and effective palladium-catalyzed selective dehydrogenative [4+2] annulation of 2-methyl-1,3-cycloalkanediones with olefins was reported. The active 2-methylene-1,3-cycloalkanedione was in situ generated via Pd-catalyzed enolate oxidation processes, and it subsequently reacted with a wide variety of olefins to afford various polysubstituted dihydropyran derivatives in good to excellent yields.

CuBr-mediated synthesis of 1,4-naphthoquinones via ring expansion of 2-aryl-1,3-indandiones.

A CuBr-mediated direct insertion of alkenes into unstrained ring 2-aryl-1,3-indandiones is reported, which provides a one-carbon ring expansion strategy for the synthesis of 1,4-naphthoquinones. Entirely differing from the existing reports, the alkenes herein behave as C1 units to participate in annulation reactions. This transformation provides a facile route to access a class of highly functionalized 1,4-naphthoquinones with good yields, good functional-group tolerance and high atom-economy. Further research on the application of this reaction is currently underway in our laboratory.

Memristive technologies for data storage, computation, encryption, and radio-frequency communication.

Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non-von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.

Concealable physically unclonable function chip with a memristor array.

A physically unclonable function (PUF) is a creditable and lightweight solution to the mistrust in billions of Internet of Things devices. Because of this remarkable importance, PUF need to be immune to multifarious attack means. Making the PUF concealable is considered an effective countermeasure but it is not feasible for existing PUF designs. The bottleneck is finding a reproducible randomness source that supports repeatable concealment and accurate recovery of the PUF data. In this work, we experimentally demonstrate a concealable PUF at the chip level with an integrated memristor array and peripherals. The correlated filamentary switching characteristic of the hafnium oxide (HfOx)-based memristor is used to achieve PUF concealment/recovery with SET/RESET operations efficiently. PUF recovery with a zero-bit error rate and remarkable attack resistance are achieved simultaneously with negligible circuit overhead. This concealable PUF provides a promising opportunity to build memristive hardware systems with effective security in the near future.

A Threshold Switching Selector Based on Highly Ordered Ag Nanodots for X-Point Memory Applications.

Leakage interference between memory cells is the primary obstacle for enlarging X-point memory arrays. Metal-filament threshold switches, possessing excellent selectivity and low leakage current, are developed in series with memory cells to reduce sneak path current and lower power consumption. However, these selectors typically have limited on-state currents (≤10 µA), which are insufficient for memory RESET operations. Here, a strategy is proposed to achieve sufficiently large RESET current (≈2.3 mA) by introducing highly ordered Ag nanodots to the threshold switch. Compared to the Ag thin film case, Ag nanodots as active electrode could avoid excessive Ag atoms migration into solid electrolyte during operations, which causes stable conductive filament growth. Furthermore, Ag nanodots with rapid thermal processing contribute to forming multiple weak Ag filaments at a lower voltage and then spontaneous rupture as the applied voltage reduced, according to quantized conductance and simulation analysis. Impressively, the Ag nanodots based threshold switch, which is bidirectional and truly electroforming-free, demonstrates extremely high selectivity >109, ultralow leakage current <1 pA, very steep slope of 0.65 mV dec-1, and good thermal stability up to 200 °C, and further represents significant suppression of leakage currents and excellent performances for SET/RESET operations in the one-selector-one-resistor configuration.

A fully integrated nose-on-a-chip for rapid diagnosis of ventilator-associated pneumonia.

Ventilator-associated pneumonia (VAP) still lacks a rapid diagnostic strategy. This study proposes installing a nose-on-a-chip at the proximal end of an expiratory circuit of a ventilator to monitor and to detect metabolite of pneumonia in the early stage. The nose-on-a-chip was designed and fabricated in a 90-nm 1P9M CMOS technology in order to downsize the gas detection system. The chip has eight on-chip sensors, an adaptive interface, a successive approximation analog-to-digital converter (SAR ADC), a learning kernel of continuous restricted Boltzmann machine (CRBM), and a RISC-core with low-voltage SRAM. The functionality of VAP identification was verified using clinical data. In total, 76 samples infected with pneumonia (19 Klebsiella, 25 Pseudomonas aeruginosa, 16 Staphylococcus aureus, and 16 Candida) and 41 uninfected samples were collected as the experimental group and the control group, respectively. The results revealed a very high VAP identification rate at 94.06% for identifying healthy and infected patients. A 100% accuracy to identify the microorganisms of Klebsiella, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida from VAP infected patients was achieved. This chip only consumes 1.27 mW at a 0.5 V supply voltage. This work provides a promising solution for the long-term unresolved rapid VAP diagnostic problem.