Ferroelectric Aluminum Scandium Nitride Transistors with Intrinsic Switching Characteristics and Artificial Synaptic Functions for Neuromorphic Computing.

Aluminum Scandium Nitride (Al1-xScxN) has received attention for its exceptional ferroelectric properties, whereas the fundamental mechanism determining its dynamic response and reliability remains elusive. In this work, an unreported nucleation-based polarization switching mechanism in Al0.7Sc0.3N (AlScN) is unveiled, driven by its intrinsic ferroelectricity rooted in the ionic displacement. Fast polarization switching, characterized by a remarkably low characteristic time of 0.00183 ps, is captured, and effectively simulated using a nucleation-limited switching (NLS) model, where the profound effect of defects on the nucleation and domain propagation is systematically studied. These findings are further integrated into Monte Carlo simulations to unravel the influence of the activation energy for ferroelectric switching on the distributions of switching thresholds. The long-term reliability of devices is also confirmed by time-dependent dielectric breakdown (TDDB) measurements, and the effect of thickness scaling is discussed. Ferroelectric field-effect transistors (FeFETs) are demonstrated through the integration of AlScN and 2D MoS2 channel, where biological synaptic functions can be emulated with optimized operation voltage. The artificial neural network built from AlScN-based FeFETs achieves 93.8% recognition accuracy of handwritten digits, demonstrating the potential of ferroelectric AlScN in future neuromorphic computing applications.

Physical reservoirs based on MoS2-HZO integrated ferroelectric field-effect transistors for reservoir computing systems.

Reservoir computing (RC), a variant of recurrent neural networks (RNNs), is well-known for its reduced energy consumption through exclusive focus on training the output weight and its superior performance in handling spatiotemporal information. Implementing these networks in hardware requires devices with superior fading memory behavior. Unlike filament-based two-terminal devices, those relying on ferroelectric switching demonstrate improved voltage reliability, while three-terminal transistors provide additional active control. HfO2-based ferroelectric materials such as Hf0.5Zr0.5O2 (HZO), have garnered attention for their scalability and seamless integration with CMOS technology. This study implements a RC hardware based on MoS2-HZO integrated device structure with enhanced spontaneous polarization field. By adjusting the oxygen vacancy concentration, the devices exhibit consistent responses to both identical and nonidentical voltages, making them suitable for diverse RC applications. The high accuracy of MNIST handwritten digits recognition highlights the rich reservoir states of the traditional RC architecture. Additionally, the impact of masks on RC implementation is assessed, showcasing the device's capability for spatiotemporal signal analysis. This development paves the way for implementing energy-efficient and high-performance computing solutions.

Coupled Ferroelectric-Photonic Memory in a Retinomorphic Hardware for In-Sensor Computing.

The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Attack Resilient True Random Number Generators Using Ferroelectric-Enhanced Stochasticity in 2D Transistor.

By harnessing the physically unclonable properties, true random number generators (TRNGs) offer significant promises to alleviate security concerns by generating random bitstreams that are cryptographically secured. However, fundamental challenges remain as conventional hardware often requires complex circuitry design, showing a predictable pattern that is susceptible to machine learning attacks. Here, a low-power self-corrected TRNG is presented by exploiting the stochastic ferroelectric switching and charge trapping in molybdenum disulfide (MoS2 ) ferroelectric field-effect transistors (Fe-FET) based on hafnium oxide complex. The proposed TRNG exhibits enhanced stochastic variability with near-ideal entropy of ≈1.0, Hamming distance of ≈50%, independent autocorrelation function, and reliable endurance cycle against temperature variations. Furthermore, its unpredictable feature is systematically examined by machine learning attacks, namely the predictive regression model and the long-short-term-memory (LSTM) approach, where nondeterministic predictions can be concluded. Moreover, the generated cryptographic keys from the circuitry successfully pass the National Institute of Standards and Technology (NIST) 800-20 statistical test suite. The potential of integrating ferroelectric and 2D materials is highlighted for advanced data encryption, offering a novel alternative to generate truly random numbers.

A MoS2 Hafnium Oxide Based Ferroelectric Encoder for Temporal-Efficient Spiking Neural Network.

Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applications, the artificial neural encoders are essential to convert the external stimuli to a spiking format that can be subsequently fed to the neural network. Here, a molybdenum disulfide (MoS2 ) hafnium oxide-based ferroelectric encoder is demonstrated for temporal-efficient information processing in SNN. The fast domain switching attribute associated with the polycrystalline nature of hafnium oxide-based ferroelectric material is exploited for spike encoding, rendering it suitable for realizing biomimetic encoders. Accordingly, a high-performance ferroelectric encoder is achieved, featuring a superior switching efficiency, negligible charge trapping effect, and robust ferroelectric response, which successfully enable a broad dynamic range. Furthermore, an SNN is simulated to verify the precision of the encoded information, in which an average inference accuracy of 95.14% can be achieved, using the Modified National Insitute of Standards and Technology (MNIST) dataset for digit classification. Moreover, this ferroelectric encoder manifests prominent resilience against noise injection with an overall prediction accuracy of 94.73% under various Gaussian noise levels, showing practical promises to reduce the computational load for the neural network.

Interface-Modulated Resistive Switching in Mo-Irradiated ReS2 for Neuromorphic Computing.

Coupling charge impurity scattering effects and charge-carrier modulation by doping can offer intriguing opportunities for atomic-level control of resistive switching (RS). Nonetheless, such effects have remained unexplored for memristive applications based on 2D materials. Here a facile approach is reported to transform an RS-inactive rhenium disulfide (ReS2 ) into an effective switching material through interfacial modulation induced by molybdenum-irradiation (Mo-i) doping. Using ReS2 as a model system, this study unveils a unique RS mechanism based on the formation/dissolution of metallic ß-ReO2 filament across the defective ReS2 interface during the set/reset process. Through simple interfacial modulation, ReS2 of various thicknesses are switchable by modulating the Mo-irradiation period. Besides, the Mo-irradiated ReS2 (Mo-ReS2 ) memristor further exhibits a bipolar non-volatile switching ratio of nearly two orders of magnitude, programmable multilevel resistance states, and long-term synaptic plasticity. Additionally, the fabricated device can achieve a high MNIST learning accuracy of 91% under a non-identical pulse train. The study's findings demonstrate the potential for modulating RS in RS-inactive 2D materials via the unique doping-induced charged impurity scattering property.

Wafer-Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy-Efficient Neural Network Hardware.

Memristor crossbar with programmable conductance could overcome the energy consumption and speed limitations of neural networks when executing core computing tasks in image processing. However, the implementation of crossbar array (CBA) based on ultrathin 2D materials is hindered by challenges associated with large-scale material synthesis and device integration. Here, a memristor CBA is demonstrated using wafer-scale (2-inch) polycrystalline hafnium diselenide (HfSe2 ) grown by molecular beam epitaxy, and a metal-assisted van der Waals transfer technique. The memristor exhibits small switching voltage (0.6 V), low switching energy (0.82 pJ), and simultaneously achieves emulation of synaptic weight plasticity. Furthermore, the CBA enables artificial neural network with a high recognition accuracy of 93.34%. Hardware multiply-and-accumulate (MAC) operation with a narrow error distribution of 0.29% is also demonstrated, and a high power efficiency of greater than 8-trillion operations per second per Watt is achieved. Based on the MAC results, hardware convolution image processing can be performed using programmable kernels (i.e., soft, horizontal, and vertical edge enhancement), which constitutes a vital function for neural network hardware.

An electro-photo-sensitive synaptic transistor for edge neuromorphic visual systems.

The practical application of optoelectronic artificial synapses in neuromorphic visual systems is still hindered by their limited functionality, reliability and the challenge of mass production. Here, an electro-photo-sensitive synapse based on a highly reliable amorphous InGaZnO thin-film transistor is demonstrated. Not only does the synapse respond to electrical voltage spikes due to charge trapping/detrapping, but also the weight is modified directly by persistent photocurrent effects under UV-light stimulation. Representative forms of synaptic plasticity, including inhibitory and excitatory postsynaptic currents, frequency-dependent characteristics, short-term to long-term plasticity transitions, and summation effects, are successfully demonstrated. In particular, optoelectronic synergetic modulation leads to reconfigurable excitatory and inhibitory synaptic behaviors, which provides a promising way to achieve the homeostatic regulation of synaptic weights. Moreover, the analogue channel conductance with 100 states is used as the weight update rule to perform MNIST handwritten digit recognition, using system-level LeNet-5 convolutional neural network simulations. The network shows a high recognition accuracy of 95.99% and good tolerance to noisy input patterns. This study highlights the commercial potential of mature optoelectronic InGaZnO transistor technology in edge neuromorphic systems.

Etifoxine, a TSPO Ligand, Worsens Hepatitis C-Related Insulin Resistance but Relieves Lipid Accumulation.

Etifoxine, an 18 kDa translocator protein (TSPO) agonist for the treatment of anxiety disorders in clinic, may be able to cause acute liver injury or cytolytic hepatitis. TSPO has been demonstrated to participate in inflammatory responses in infective diseases as well as to modulate glucose and lipid homeostasis. Hepatitis C virus (HCV) infection disrupts glucose and lipid homoeostasis, leading to insulin resistance (IR). Whether TSPO affects the HCV-induced IR remains unclear. Here, we found that the administration of etifoxine increased the TSPO protein expression and recovered the HCV-mediated lower mitochondrial membrane potential (MMP) without affecting HCV infection. Moreover, etifoxine reversed the HCV-induced lipid accumulation by modulating the expressions of sterol regulatory element-binding protein-1 and apolipoprotein J. On the other hand, in infected cells pretreated with etifoxine, the insulin-mediated insulin receptor substrate-1/Akt signals, forkhead box protein O1 translocation, and glucose uptake were blocked. Taken together, our results pointed out that etifoxine relieved the HCV-retarded MMP and reduced the lipid accumulation but deteriorated the HCV-induced IR by interfering with insulin signal molecules.

Calcitriol Inhibits HCV Infection via Blockade of Activation of PPAR and Interference with Endoplasmic Reticulum-Associated Degradation.

Vitamin D has been identified as an innate anti-hepatitis C virus (HCV) agent but the possible mechanisms for this issue remain unclear. Here, we clarified the mechanisms of calcitriol-mediated inhibition of HCV infection. Calcitriol partially inhibited HCV infection, nitric oxide (NO) release and lipid accumulation in Huh7.5 human hepatoma cells via the activation of vitamin D receptor (VDR). When cells were pretreated with the activators of peroxisome proliferator-activated receptor (PPAR)-α (Wy14643) and -γ (Ly171883), the calcitriol-mediated HCV suppression was reversed. Otherwise, three individual stimulators of PPAR-α/ß/γ blocked the activation of VDR. PPAR-ß (linoleic acid) reversed the inhibition of NO release, whereas PPAR-γ (Ly171883) reversed the inhibitions of NO release and lipid accumulation in the presence of calcitriol. The calcitriol-mediated viral suppression, inhibition of NO release and activation of VDR were partially blocked by an inhibitor of endoplasmic reticulum-associated degradation (ERAD), kifunensine. Furthermore, calcitriol blocked the HCV-induced expressions of apolipoprotein J and 78 kDa glucose-regulated protein, which was restored by pretreatment of kifunensine. These results indicated that the calcitriol-mediated HCV suppression was associated with the activation of VDR, interference with ERAD process, as well as blockades of PPAR, lipid accumulation and nitrative stress.

Fluoxetine regulates cell growth inhibition of interferon-α.

Fluoxetine, a well-known anti-depression agent, may act as a chemosensitizer to assist and promote cancer therapy. However, how fluoxetine regulates cellular signaling to enhance cellular responses against tumor cell growth remains unclear. In the present study, addition of fluoxetine promoted growth inhibition of interferon-alpha (IFN-α) in human bladder carcinoma cells but not in normal uroepithelial cells through lessening the IFN-α-induced apoptosis but switching to cause G1 arrest, and maintaining the IFN-α-mediated reduction in G2/M phase. Activations and signal transducer and transactivator (STAT)-1 and peroxisome proliferator-activated receptor alpha (PPAR-α) were involved in this process. Chemical inhibitions of STAT-1 or PPAR-α partially rescued bladder carcinoma cells from IFN-α-mediated growth inhibition via blockades of G1 arrest, cyclin D1 reduction, p53 downregulation and p27 upregulation in the presence of fluoxetine. However, the functions of both proteins were not involved in the control of fluoxetine over apoptosis and maintained the declined G2/M phase of IFN-α. These results indicated that activation of PPAR-α and STAT-1 participated, at least in part, in growth inhibition of IFN-α in the presence of fluoxetine.

Rosiglitazone regulates anti-inflammation and growth inhibition via PTEN.

Peroxisome proliferator-activated receptor gamma (PPARγ) agonist has anti-inflammatory and anticancer properties. However, the mechanisms by which PPARγ agonist rosiglitazone interferes with inflammation and cancer via phosphatase and tensin homolog-(PTEN)-dependent pathway remain unclear. We found that lower doses (<25 µ M) of rosiglitazone significantly inhibited lipopolysaccharide-(LPS)-induced nitric oxide (NO) release (via inducible nitric oxide synthase, iNOS), prostaglandin E2 (PGE2) production (via cyclooxygenase-2, COX-2), and activation of Akt in RAW 264.7 murine macrophages. However, rosiglitazone did not inhibit the production of reactive oxygen species (ROS). In PTEN knockdown (shPTEN) cells exposed to LPS, rosiglitazone did not inhibit NO release, PGE2 production, and activation of Akt. These cells had elevated basal levels of iNOS, COX-2, and ROS. However, higher doses (25-100 µ M) of rosiglitazone, without LPS stimulation, did not block NO release and PGE2 productions, but they inhibited p38 MAPK phosphorylation and blocked ROS generation in shPTEN cells. In addition, rosiglitazone caused G1 arrest and reduced the number of cells in S + G2/M phase, leading to growth inhibition. These results indicate that the anti-inflammatory property of rosiglitazone is related to regulation of PTEN independent of inhibition on ROS production. However, rosiglitazone affected the dependence of PTEN-deficient cell growth on ROS.

Blockade of reactive oxygen species and Akt activation is critical for anti-inflammation and growth inhibition of metformin in phosphatase and tensin homolog-deficient RAW264.7 cells.

CONTEXT: Metformin is widely used for treatment of type 2 diabetes and has a potential application on the treatment of inflammation and cancer. Phosphatase and tensin homolog (PTEN) plays a critical role in cancer cell growth and inflammation; however, precise mechanisms remain unclear. OBJECTIVE: We aimed to investigate the possible mechanisms of how PTEN regulates metformin against cell growth and inflammation. MATERIALS AND METHODS: We established PTEN knockdown in RAW264.7 murine macrophages (shPTEN cells) to detect inflammatory mediators using commercial kits, production of reactive oxygen species (ROS) by flow cytometry, cell growth by MTT assay and phosphorylated levels of signal molecules by western blot. RESULTS: The shPTEN cells had a significant large amount of inflammatory mediators, such as inducible nitric oxide synthase (iNOS)/nitric oxide (NO) and cyclooxygenase-2 (COX-2)/prostaglandin E(2) (PGE(2)); and also elevated the production of ROS and increased cell proliferation. These effects were accompanied with the activation of Akt and p38 mitogen-activated protein kinase (MAPK), and the inactivation of an AMP-activated protein kinase (AMPK) activator and extracellular signal-regulated kinase 1/2. Pretreatment with metformin not only blocked these inflammatory mediators, but also caused growth inhibition induced by significant apoptosis. Furthermore, inactivation of Akt, blockade of ROS generation and independence of activations of AMPK and MAPK by metformin were also observed. CONCLUSION: Macrophages with PTEN deficiency developed a continuous inflammatory microenvironment, which further aggravated tumor cell growth. Moreover, metformin affected PTEN-deficient cells dependent of inhibition of ROS production and Akt activation against enlarged inflammatory mediators and/or cell growth in shPTEN cells.

Glycogen synthase kinase-3ß regulates anti-inflammatory property of fluoxetine.

A selective serotonin reuptake inhibitor fluoxetine not only is widely used in the treatment of depression but also has an anti-inflammatory property. Glycogen synthase kinase-3beta (GSK-3ß) is a vital factor in the inflammation process. How fluoxetine interferes with inflammation via a GSK-3ß-dependent pathway remains unclear. The aim of this study is to investigate the effects of fluoxetine on lipopolysaccharide (LPS)-induced inflammation. Results showed that fluoxetine decreased mortality rate of the mice. It also inhibited LPS-induced release of nitric oxide (NO) and prostaglandin E2 (PGE2) in serum and RAW264.7 murine macrophages and expressions of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Upon LPS stimulation, fluoxetine caused a delay but increased in the phosphorylated levels of GSK-3ß (ser9), whereas it did not affect LPS-induced activation of mitogen-activated protein kinase (MAPK) and generation of reactive oxygen species (ROS). Fluoxetine in combination with phosphatidylinositol 3-kinases/Akt inhibitors (LY294002 and Wortmannin) did not have a synergistic inhibition on LPS-induced NO release and PGE2 production. In addition, peroxisome proliferator-activated receptor γ (PPARγ) antagonist GW9622 showed no reverse effects of this inhibition of fluoxetine. GSK-3ß knockdown blocked the inhibitory effects of fluoxetine on LPS-induced iNOS/NO release and COX-2/PGE2 production. These results indicated that GSK-3ß regulated anti-inflammatory property of fluoxetine. However, Akt activation, ROS generation, and altered PPARγ activity were not involved in this inhibition of fluoxetine.

Secondary metabolites from the root of Ehretia longiflora and their biological activities.

Bioassay-guided fractionation of the methanolic extract of the root of Ehretia longiflora (Boraginaceae) afforded eight compounds, ehretiquinone (1), ehretiolide (2), ehreticoumarin (3), ehretilactone A (4), ehretilactone B (5), ehretiamide (6), ehretine (7), and ehretiate (8), together with 12 known compounds (9-20). The relative configuration of 1 was determined by single crystal X-ray diffraction. Among the isolates, 1 and prenylhydroquinone (14) showed antitubercular activity against Mycobacterium tuberculosis strain H37Rv with MIC values of 25.0 and 26.2 µg/mL, respectively. Moreover, 1 exhibited inhibitory effects on N-formylmethionylleucylphenylalanine (fMLP)-induced superoxide production, with IC50 value of 0.36±0.03µM.