Thousands of conductance levels in memristors integrated on CMOS.

Neural networks based on memristive devices1-3 have the ability to improve throughput and energy efficiency for machine learning4,5 and artificial intelligence6, especially in edge applications7-21. Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks22-28. This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even 'mortal computing'25,29,30. Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal-oxide-semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Fig. 1 HIGH-PRECISION MEMRISTOR FOR NEUROMORPHIC COMPUTING.: a, Proposed scheme of the large-scale application of memristive neural networks for edge computing. Neural network training is performed in the cloud. The obtained weights are downloaded and accurately programmed into a massive number of memristor arrays distributed at the edge, which imposes high-precision requirements on memristive devices. b, An eight-inch wafer with memristors fabricated by a commercial semiconductor manufacturer. c, High-resolution transmission electron microscopy image of the cross-section view of a memristor. Pt and Ta serve as the bottom electrode (BE) and top electrode (TE), respectively. Scale bars, 1 µm and 100 nm (inset). d, Magnification of the memristor material stack. Scale bar, 5 nm. e, As-programmed (blue) and after-denoising (red) currents of a memristor are read by a constant voltage (0.2 V). The denoising process eliminated the large-amplitude RTN observed in the as-programmed state (see Methods). f, Magnification of three nearest-neighbour states after denoising. The current of each state was read by a constant voltage (0.2 V). No large-amplitude RTN was observed, and all of the states can be clearly distinguished. g, An individual memristor on the chip was tuned into 2,048 resistance levels by high-resolution off-chip driving circuitry, and each resistance level was read by a d.c. voltage sweeping from 0 to 0.2 V. The target resistance was set from 50 µS to 4,144 µS with a 2-µS interval between neighbouring levels. All readings at 0.2 V are less than 1 µS from the target conductance. Bottom inset, magnification of the resistance levels. Top inset, experimental results of an entire 256 × 256 array programmed by its 6-bit on-chip circuitry into 64 32 × 32 blocks, and each block is programmed into one of the 64 conductance levels. Each of the 256 × 256 memristors has been previously switched over one million cycles, demonstrating the high endurance and robustness of the devices.

Risk and protective factors of suicidal tendencies among freshmen in China revealed by a hierarchical regression model.

This study aimed to identify risk and protective factors for suicidal tendencies among college students by exploring current mental health, personal experiences, family environment, and school adaptation. A total of 11,504 freshmen in China were recruited. Suicidal tendencies were assessed using the Adolescents Suicidal Tendencies Scale (ASTS), while explored risk and protective factors included mental health assessed by the Symptom Checklist-90 (SCL-90), campus adaptation using the College Student School Adaptation Scale, and Personal Situation Survey. Single-factor Logistic regression analysis, correlation analysis, and hierarchical regression analysis were used to analyze the risk and protective factors affecting suicidal tendencies. The results showed that in terms of personal experience, self-injury behavior (OR = 3.522, 95% CI [3.256, 3.811]), sexual assault experience (OR = 2.603, 95% CI [2.374, 2.855]) and lack of friendship relationship (OR = 2.249, 95% CI [2.076, 2.436]) were the most significant risk factors. Regarding family environment, parenting style (OR = 2.455, 95% CI [2.272, 2.652]), parent-child relationship (OR = 2.255, 95% CI [2.092, 2.429]) and violent conflict (OR = 2.164, 95% CI [2.015, 2.324]) were the most prominent risk factors. For protective factors, life satisfaction (OR = 0.330, 95% CI [0.304, 0.359]) and rest quality (OR = 0.415, 95% CI [0.386, 0.447]) were the most significant protective factors. In addition, Symptom Checklist-90 was positively correlated with suicidal tendencies (r = 0.541, 95% CI [0.522, 0.560], p < 0.001), while school adaptation was negatively correlated with suicidal tendencies (r = - 0.590, 95% CI [- 0.579, - 0.601], p < 0.001). After considering demographic variables, psychological symptoms, school adaptation and other risk and protective factors, the hierarchical regression model could explain 48.9% of the variance of suicidal tendencies. The study emphasizes a range of multidimensional risk and protective factors for suicidal tendencies. This enhanced understanding is crucial in aiding the design of future intervention studies targeted at improving the mental health of college students.

Constructing van der Waals heterostructures by dry-transfer assembly for novel optoelectronic device.

Since the first successful exfoliation of graphene, the superior physical and chemical properties of two-dimensional (2D) materials, such as atomic thickness, strong in-plane bonding energy and weak inter-layer van der Waals (vdW) force have attracted wide attention. Meanwhile, there is a surge of interest in novel physics which is absent in bulk materials. Thus, vertical stacking of 2D materials could be critical to discover such physics and develop novel optoelectronic applications. Although vdW heterostructures have been grown by chemical vapor deposition, the available choices of materials for stacking is limited and the device yield is yet to be improved. Another approach to build vdW heterostructure relies on wet/dry transfer techniques like stacking Lego bricks. Although previous reviews have surveyed various wet transfer techniques, novel dry transfer techniques have been recently been demonstrated, featuring clean and sharp interfaces, which also gets rid of contamination, wrinkles, bubbles formed during wet transfer. This review summarizes the optimized dry transfer methods, which paves the way towards high-quality 2D material heterostructures with optimized interfaces. Such transfer techniques also lead to new physical phenomena while enable novel optoelectronic applications on artificial vdW heterostructures, which are discussed in the last part of this review.

Preparation of fluorescein-modified polymer dots and their application in chiral discrimination of lysine enantiomers.

Fluorescein-functionalized fluorescent polymer dots (F-PDs) were prepared by a facile one-pot method by magnetic stirring under mild conditions based on carboxymethylcellulose (CMC) and fluorescein as the precursors. The obtained F-PDs exhibited a nanoscale size of 3.2 ± 1.1 nm, excellent water solubility, and bright yellow fluorescence emission with a fluorescence quantum yield of 12.0%. The fluorescent probe displays rapid and sensitive chiral discrimination for lysine focused on different complexation abilities between lysine enantiomers and Cu2+. The concentration of L-lysine in the range 4 to 14 mM (R2 = 0.997) was measured by the fluorescence intensity ratio (I513/I429); the exitation wavelength was set to λex = 365 nm. The detection limit was 0.28 mM (3σ/slope). Importantly, this sensor accurately predicted the enantiomeric excess (ee) of lysine enantiomers at the designed concentration (lysine: 20 mM; Cu2+: 10 mM) ranges. The proposed sensor was successfully applied to determine L-lys (recovery: 95.8-101%; RSD: 0.465-3.34%) and ee values (recovery: 98.5-102%; RSD: 2.61-3.21%) in human urine samples using the standard addition method.

Cyclin D1 promotes secretion of pro-oncogenic immuno-miRNAs and piRNAs.

The molecular mechanisms governing the secretion of the non-coding genome are poorly understood. We show herein that cyclin D1, the regulatory subunit of the cyclin-dependent kinase that drives cell-cycle progression, governs the secretion and relative proportion of secreted non-coding RNA subtypes (miRNA, rRNA, tRNA, CDBox, scRNA, HAcaBox. scaRNA, piRNA) in human breast cancer. Cyclin D1 induced the secretion of miRNA governing the tumor immune response and oncogenic miRNAs. miR-21 and miR-93, which bind Toll-Like Receptor 8 to trigger a pro-metastatic inflammatory response, represented >85% of the cyclin D1-induced secreted miRNA transcripts. Furthermore, cyclin D1 regulated secretion of the P-element Induced WImpy testis (PIWI)-interacting RNAs (piRNAs) including piR-016658 and piR-016975 that governed stem cell expansion, and increased the abundance of the PIWI member of the Argonaute family, piwil2 in ERα positive breast cancer. The cyclin D1-mediated secretion of pro-tumorigenic immuno-miRs and piRNAs may contribute to tumor initiation and progression.

Long non-coding RNA CCAT2 promotes oncogenesis in triple-negative breast cancer by regulating stemness of cancer cells.

Triple-negative breast cancers (TNBC) are more aggressive due to lacking receptors for hormone therapy and maintaining stemness features in cancer cells. Herein we found long non-coding RNA CCAT2 overexpressed specially in TNBC, and in breast cancer stem cells (BCSC) as well. Enforced overexpression and targeted knockdown demonstrated the oncogenic function of CCAT2 both in vitro and in vivo. CCAT2 promoted the expression of stemness markers including OCT4, Nanog and KLF4, increased mammosphere formation and induced ALDH+ cancer stem cell population in TNBC. A chromosomally adjacent gene OCT4-PG1, as a pseudogene of OCT4, was upregulated by CCAT2, and positively regulated the stemness features of TNBC cells. miR-205 was identified as a target gene of CCAT2 in TNBC. Point-mutation in CCAT2 impaired the sponge inhibition of miR-205. Overexpression of miR-205 rescued the oncogenic phenotypes induced by CCAT2. In addition, Notch2, as a target gene of miR-205, was downregulated by miR-205 and upregulated by CCAT2 in TNBC. Collectively, the current study revealed a novel function of CCAT2 in promoting tumor initiation and progression in TNBC through upregulating OCT4-PG1 expression and activating Notch signaling. These findings not only demonstrated a lncRNA-based therapeutic strategy in treatment of TNBC, but also added a node to the regulatory network of CCAT2 that controls aggressiveness of breast cancer stem cells.

Tamoxifen therapy benefit predictive signature combining with prognostic signature in surgical-only ER-positive breast cancer.

Tamoxifen treatment is important assistant for estrogen-receptor-positive breast cancer (BRCA) after resection. This study aimed to identify signatures for predicting the prognosis of patients with BRCA after tamoxifen treatment. Data of gene-specific DNA methylation (DM), as well as the corresponding clinical data for the patients with BRCA, were obtained from The Cancer Genome Atlas and followed by systematic bioinformatics analyses. After mapping these DM CPG sites onto genes, we finally obtained 352 relapse-free survival (RFS) associated DM genes, with which 61,776 gene pairs were combined, including 1,614 gene pairs related to RFS. An 11 gene-pair signature was identified to cluster the 189 patients with BRCA into the surgical low-risk group (136 patients) and high-risk group (53 patients). Then, we further identified a tamoxifen-predictive signature that could classify surgical high-risk patients with significant differences on RFS. Combining surgical-only prognostic signature and tamoxifen-predictive signature, patients were clustered into surgical-only low-risk group, tamoxifen nonbenefit group, and tamoxifen benefit group. In conclusion, we identified that the gene pair PDHA2-APRT could serve as a potential prognostic biomarker for patients with BRCA after tamoxifen treatment.

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.

The accumulation and extrusion of Ca2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.

Upregulation of the solute carrier family 7 genes is indicative of poor prognosis in papillary thyroid carcinoma.

BACKGROUND: The solute carrier (SLC) 7 family genes comprise 14 members and function as cationic amino acid/glycoprotein transporters in many cells, they are essential for the maintenance of amino acid nutrition and survival of tumor cells. This study was conducted to analyze the associations of SLC7 family gene expression with mortality in papillary thyroid carcinoma (PTC). METHODS: Clinical features, somatic mutations, and SLC7 family gene expression data were downloaded from The Cancer Genome Atlas database. Linear regression model analysis was performed to analyze the correlations between SLC7 family gene expression and clinicopathologic features. Kaplan-Meier survival and logistic regression analyses were performed to characterize the associations between gene expression and patients' overall survival. RESULTS: Patient mortality was negatively associated with age and tumor size but positively increased cancer stage and absence of thyroiditis in PTC patients. Kaplan-Meier survival analysis indicated that patients with high SLC7A3, SLC7A5, and SLC7A11 expression levels exhibited poorer survival than those with low SLC7A3, SLC7A5, and SLC7A11 expression levels (P < 0.05 for all cases). Logistic regression analysis showed that SLC7A3, SLC7A5, and SLC7A11 were associated with increased mortality (odds ratio [OR] 8.61, 95% confidence interval [CI] 2.3-55.91; OR 3.87, 95% CI 1.18-17.31; and OR 3.87, 95% CI 1.18-17.31, respectively. CONCLUSION: Upregulation of SLC7A3, SLC7A5, and SLC7A11 expression was associated with poor prognosis in PTC patients, and SLC7 gene expression levels are potentially useful prognostic biomarkers.

Faith in frames: unveiling therapeutic narratives in religion-related cinema through computational analysis.

Introduction: This study explores the emotional impact of religion-related films through a "cinematherapy" lens. It aims to analyze the emotional patterns in a curated selection of religion-related films compared to a broader sample of acclaimed movies using facial recognition with YOLOv5 object detection. The study aims to uncover the potential therapeutic application of religion-related films. Methods: Facial recognition with YOLOv5 object detection was utilized in this study to analyze the emotional patterns in religion-related films. A curated selection of these films was compared to a broader sample of acclaimed movies to identify any distinct emotional trajectories. Results: The analysis of the emotional patterns revealed that religion-related films exhibited a subtler range of emotions compared to the broader film spectrum. This finding suggests that these films potentially create a safe space for contemplation, aligning with the profound themes often explored in religion-related films. Interestingly, the emotional arc observed in the films mirrored the spiritual journeys depicted in them. The films started with a low point of separation, transitioned through challenges, and culminated in a peak representing spiritual transformation. Discussion: These findings suggest promise for the therapeutic application of religion-related films. The muted emotional expression in these films creates a safe space for self-reflection, enabling viewers to connect with the struggles of the characters and explore their own values when faced with complex religious ideas. This emotional engagement may contribute to therapeutic goals such as introspection and personal growth. The study unveils the unique emotional power of religion-related films and paves the way for further research on their potential as therapeutic tools. It emphasizes the need for continued exploration of the emotional impact of these films and their capacity to aid in therapeutic goals.

Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing.

Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and rich synaptic behaviors via ion diffusion and trapping. While these neuromorphic devices hold great promise, they still suffer from challenges such as high leakage currents and power consumption, electrolysis risk, and irreversible conductance changes due to long-range ion migrations and permanent ion trapping. In addition, their response to light is generally limited because of "exciton-polaron quenching", which restricts their potential in in-sensor neuromorphic visions. To address these issues, we propose replacing solid electrolytes with polyzwitterions, where the cation and anion are covalently concatenated via a flexible alkyl chain, thus preventing long-range ion migrations while inducing good photoresponses to the transistors via interfacial charge trapping. Our detailed studies reveal that polyzwitterion-based transistors exhibit optoelectronic synaptic behavior with ultralow-power consumption (~250 aJ per spike) and enable high-performance in-sensor reservoir computing, achieving 95.56% accuracy in perceiving the trajectory of moving basketballs.

Lithium Niobate MEMS Antisymmetric Lamb Wave Resonators with Support Structures.

The piezoelectric thin film composed of single-crystal lithium niobate (LiNbO3) exhibits a remarkably high electromechanical coupling coefficient and minimal intrinsic losses, making it an optimal material for fabricating bulk acoustic wave resonators. However, contemporary first-order antisymmetric (A1) Lamb mode resonators based on LiNbO3 thin films face specific challenges, such as inadequate mechanical stability, limited power capacity, and the presence of multiple spurious modes, which restrict their applicability in a broader context. In this paper, we present an innovative design for A1 Lamb mode resonators that incorporates a support-pillar structure. Integration of support pillars enables the dissipation of spurious wave energy to the substrate, effectively mitigating unwanted spurious modes. Additionally, this novel approach involves anchoring the piezoelectric thin film to a supportive framework, consequently enhancing mechanical stability while simultaneously improving the heat dissipation capabilities of the core.

miR-29a-KLF4 signaling inhibits breast tumor initiation by regulating cancer stem cells.

Cancer stem cells (CSCs) are known for their potent ability to drive tumor initiation and recurrence, yet the molecular mechanisms regulating CSCs are still unclear. Our study found a positive correlation between increased levels of miR-29a and better survival rates in early-stage breast cancer patients, but a negative correlation in late-stage patients, suggesting a dual function of miR-29a in regulating breast cancer. Furthermore, miR-29a showed significant downregulation in the ALDH+ breast cancer stem cell population compared to non-stem cancer cells. Overexpression of miR-29a in human breast cancer cells reduced the proportion of CSCs, suppressed their ability to form mammospheres, and inhibited the expression of stemness genes SOX2, KLF4, and hTERT in vitro. Conversely, knockdown of miR-29a in breast cancer cells showed opposite effects. Tumor xenograft experiments revealed that miR-29a overexpression significantly inhibited tumorigenesis initiated by MDA-MB-231 cell transplantation in nude mice. We further demonstrated that Krüppel-like factor 4 (KLF4), a key gene that regulates cell stemness, was a direct target of miR-29a in breast cancer cells. miR-29a suppressed the expression of KLF4 at both mRNA and protein levels. Reintroduction of KLF4 into breast cancer cells rescued the miR-29a-induced CSC suppression phenotype. In summary, our study is the first to demonstrate that miR-29a-KLF4 signaling inhibits breast tumor initiation by regulating CSCs, which provides novel therapeutic targets for preventing breast tumor initiation.

A 2D Heterostructure-Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing.

The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.

Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing.

The rapid growth of sensor data in the artificial intelligence often causes significant reductions in processing speed and power efficiency. Addressing this challenge, in-sensor computing is introduced as an advanced sensor architecture that simultaneously senses, memorizes, and processes images at the sensor level. However, this is rarely reported for organic semiconductors that possess inherent flexibility and tunable bandgap. Herein, an organic heterostructure that exhibits a robust photoresponse to near-infrared (NIR) light is introduced, making it ideal for in-sensor computing applications. This heterostructure, consisting of partially overlapping p-type and n-type organic thin films, is compatible with conventional photolithography techniques, allowing for high integration density of up to 520 devices cm-2 with a 5 µm channel length. Importantly, by modulating gate voltage, both positive and negative photoresponses to NIR light (1050 nm) are attained, which establishes a linear correlation between responsivity and gate voltage and consequently enables real-time matrix multiplication within the sensor. As a result, this organic heterostructure facilitates efficient and precise NIR in-sensor computing, including image processing and nondestructive reading and classification, achieving a recognition accuracy of 97.06%. This work serves as a foundation for the development of reconfigurable and multifunctional NIR neuromorphic vision systems.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing.

In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS2/CuInP2S6 heterostructure, which integrates the defect engineering-enabled interlayer defects and the ferroelectric polarization in CuInP2S6, to realize a simplified structure device for all-in-one sensing, memory and computing. The plasma treatment-induced defect engineering of the CuInP2S6 nanosheet effectively increases the interlayer defect density, which significantly enhances the charge-trapping ability in synergy with ferroelectric properties. The reported device not only can serve as a non-volatile electronic memory device, but also can be reconfigured into optoelectronic memory mode or synaptic mode after controlling the ferroelectric polarization states in CuInP2S6. When operated in optoelectronic memory mode, the all-in-one RMD could diagnose ophthalmic disease by segmenting vasculature within biological retinas. On the other hand, operating as an optoelectronic synapse, this work showcases in-sensor reservoir computing for gesture recognition with high energy efficiency.

Local Gate Enhanced Correlated Phases in Twisted Monolayer-Bilayer Graphene.

Manipulating the flat band degeneracy and thus getting the correlated insulating phases has been an ideal thread for realizing the exotic quantum phenomenon in the moiré system. To achieve this goal, the delicately tuned twist angle and a substantial displacement field (D) are rigorously requested. Here, we report our scanning tunneling microscope (STM) work on reaching these correlated insulating states in twisted monolayer-bilayer graphene through a decorated tip. It acts as a local top gate, leading to an enhanced local D, and enables us to fully lift the 8-fold degeneracy of the flat bands. With the aid of this technique, we further expand the correlated insulating states into a more tolerant twist angle that is down to 0.92°. Moreover, the correlated insulating phases in the hole-doping regime are realized. Our tip decoration method allows us to integrate the STM study with the high displacement field for the correlated phases in the twisted moiré systems.

MEMS Oscillators-Network-Based Ising Machine with Grouping Method.

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.