Collaborative computing between edge devices and cloud servers over wireless communication is critical for energy-constrained edge devices to perform complex tasks that exceed their processing capacities. However, current wireless collaborative systems face challenges in terms of energy efficiency and latency due to the separation of memory and computing, the separation of signal processing and transmission and/or reception, and the separation of neural networks and wireless communication. Here we report communication-aware in-memory wireless neural networks. The approach uses analogue in-memory computing technology to implement both edge computing and wireless communication, and integrates wireless communication as a learnable module of the wireless neural network. We build a prototype that comprises an edge inference accelerator and a wireless communication system. The prototype exhibits an experimental inference accuracy of 93.71% on the Street View House Numbers dataset, and can maintain inference accuracy when using low-resolution analogue-to-digital converters in wireless communication. We also show that the approach can adapt to various wireless conditions and can reduce communication costs.

The advent of the big data era and the proliferation of artificial intelligence have exposed the fundamental limita-tions of the traditional von Neumann computing architecture.The physical separation between the central processing unit and memory units necessitates frequent data shuttling,leading to the severe"memory wall"bottleneck that is characterized by high latency and excessive energy consumption.By contrast,the biological brain exhibits remarkable computational effi-ciency,performing complex cognitive tasks,such as pattern recognition,associative memory,and autonomous learning with an exceptionally low power budget of approximately 20 W.This biological efficiency intrinsically arises from the brain's massive parallelism,event-driven processing,and dense colocation of memory and computation.Consequently,neuromor-phic computing,specifically the development of computing-in-memory(CIM)architectures,has emerged as a pivotal fron-tier in post-Moore's law electronics.This comprehensive review provides a holistic and detailed survey of materials,devices,and system-level strategies that are currently driving the development of neuromorphic hardware,aiming to bridge the gap between fundamental material physics and brain-inspired intelligence meticulously.To realize this paradigm shift,extensive research has focused on the development of novel materials that can physically embody neuronal and synaptic-dynamics at the hardware level.We first systematically analyze the state-of-the-art material systems that are utilized for these purposes,beginning with advanced silicon-based field-effect transistors,where optimization in a floating gate allows for a highly compact implementation of analog weight updates.Moving beyond traditional silicon,we deeply explore emerg-ing nonvolatile memory technologies.Memristive materials are highlighted for their highly scalable metal-insulator-metal structures,where the mechanisms of ion migration and conductive filament formation provide the nonvolatile multilevel states that are necessary for high-density synaptic arrays.Similarly,phase-change materials utilize the reversible transition between amorphous and crystalline states via localized Joule heating,offering distinct advantages in array scalability and multi-bit storage despite challenges related to resistance drift.Furthermore,we investigate ferroelectric materials,which utilize the polarization reversal of ferroelectric domains to provide highly linear and symmetric weight updates with sub-nanosecond switching speeds and ultralow switching energy.The integration of spintronic and magnetic materials is also discussed,leveraging electron spin dynamics and spin transfer torque to achieve exceptional endurance.Optoelectronic materials are examined for their capability to couple optical and electrical signals,enabling ultrafast signal transmission that mimics the high-dimensional connectivity of biological networks.Crucially,a central theme of this review is the para-digm shift from suppressing material non-idealities to actively utilizing intrinsic device dynamics.Physical complexities,such as volatile relaxation,stochastic switching,and nonlinear current-voltage characteristics,are increasingly utilized as rich computational resources for implementing short-term memory,reservoir computing,and probabilistic learning rules.Building upon these fundamental material properties,this review elaborates on the engineering of artificial synapses and neurons,which are the two foundational pillars of neural networks.The artificial synapse,which acts as the primary locus of learning and memory,is designed to regulate connection weights in response to external stimuli.We analyze the precise hardware implementation of essential bio-plasticity rules,including long-term potentiation,long-term depression,and spike timing-dependent plasticity,across various device architectures that range from two-terminal electrical memristors to complex multi-physics synapses.Complementary to synaptic memory,the artificial neuron serves as the nonlinear process-ing engine.We review the physical realization of neuronal models,particularly the leaky integrate-and-fire model,by using threshold switching mechanisms found in Mott insulators or volatile diffusive memristors.These devices dynamically mimic the accumulation of membrane potential and the subsequent generation of action potentials,enabling highly efficient event-driven processing wherein power is consumed strictly during spiking events.Beyond the engineering of individual devices,the realization of practical neuromorphic intelligence necessitates robust system integration and hardware-software codesign.This review extensively discusses the topology of crossbar arrays,where synaptic devices are strategically located at the cross points.This architecture enables highly parallelized vector-matrix multiplication in a single computational time step via the direct application of Ohm's law and Kirchhoff's current law,drastically accelerating neural network inference and training.The transition to analog neuromorphic computing introduces significant challenges,notably the"reality gap"caused by device-to-device variability,cycle-to-cycle noise,and limited operational endurance.To mitigate these perva-sive issues effectively and ensure the reliable execution of complex cognitive tasks,researchers are increasingly focusing on comprehensive hardware-algorithm codesign strategies.This action involves the development of robust network architec-tures and adaptive learning algorithms that are inherently resilient to underlying hardware imperfections.By establishing a closed-loop optimization framework that seamlessly integrates device-level physical traits with system-level computational models,significantly enhancing the overall reliability and fault tolerance of the neuromorphic hardware becomes possible.Such synergistic approaches are indispensable for translating the theoretical advantages of analog computing into tangible performance gains.Moreover,the relentless pursuit of these advanced computing architectures represents a deeply interdis-ciplinary endeavor,intrinsically uniting the latest breakthroughs in materials science,solid-state physics,and computa-tional neuroscience.As the field continues to mature,the seamless convergence of these diverse scientific domains will be critical in overcoming the remaining technological barriers and establishing universally accepted testing standards.Finally,this review showcases the practical deployment of these integrated materials and devices in transformative applications.From highly energy-efficient edge computing nodes for the Internet of things to adaptive brain-computer interfaces that are capable of real-time neurological signal decoding,and autonomous sensory-motor systems in advanced robotics,the poten-tial of neuromorphic hardware is vast.In conclusion,the continued advancement of neuromorphic materials and devices represents a profound shift from rigid,clock-driven logic to adaptive,biologically inspired intelligence.By harnessing the rich underlying physics of emerging materials,researchers are successfully replicating the functional building blocks of the biological brain,paving the way for a future wherein CIM architectures will enable sophisticated artificial intelligence sys-tems that are autonomous,ultraefficient,and deeply integrated into the physical world.

Physics,as the cornerstone of modern technological civilization,has its scientific frontiers profoundly shaping social development and changing the contemporary world.How-ever,there is a significant cognitive gap among humanities and social science students regard-ing modern physics frontiers.Bridging this knowledge gap is crucial for cultivating future tal-ents with interdisciplinary vision.To this end,Renmin University of China has launched the general education course"Nobel Prize in Physics and Modern Physics,"aiming to fill the gap in modern physics knowledge for humanities and social science students and enhance their sci-entific literacy.The course uses the Nobel Prize in Physics as a thread,combines it with 21st-century engineering technology,and integrates modern physics with practical applications to help students understand the core ideas of modern physics and its impact on technological de-velopment and social progress.The course effectively breaks down the barriers between arts and sciences,cultivates students'comprehensive literacy,and provides a reference for the con-struction of general education courses in science at universities.

Kagome lattices facilitate various quantum phases, yet in bulk materials, their kagome flat bands often interact with bulk bands, suppressing kagome electronic characteristics for hosting these phases. Here, we use density functional theory calculations to predict the geometric and electronic structures as well as the topological and magnetic properties of a series of MoTe2-x kagome monolayers formed by mirror-twin-boundary (MTB) loops. We analyze 13 MTB-loop configurations of varying sizes and arrangements to assess their impacts on various properties. Within the intrinsic band gap of MoTe2, we identify two sets of kagome bands, primarily originating from in-plane and out-of-plane Mo d orbitals at MTB-loop edges and vertices, respectively. Four configurations exhibit superior stability in certain ranges of Te chemical potentials, respectively, while others show comparable stability. Among these configurations, four display band gaps and potentially nonzero Z2 topological invariants, suggesting possible topological phases, while the remaining two are metallic and feature Stoner magnetization. These findings guide the design of kagome-based two-dimensional materials with tunable electronic, topological, and magnetic properties.

Breathing kagome materials Nb3X8 (X = F, Cl, Br, I) have attracted broad interest owing to their Mott insulating behavior and stacking-dependent magnetic ground states. However, the role of interlayer coupling in modulating these properties remains underexplored. Here, using density functional theory with Hubbard U corrections, we systematically investigated how interlayer coupling affects the Mott insulating states and magnetic ground states across 24 bilayer stacking configurations for each compound. We found that all bilayers remain Mott insulators, demonstrating robust Mottness. Driven by the competition between interlayer Pauli repulsion and hopping, most stackings favor interlayer AFM order, including conventional and compensated AFM, while some exhibit AFM-FM degeneracy or stabilize interlayer FM. This robustness of Mott states coexisting with tunable interlayer magnetism provides novel analysis and insights for research on breathing kagome Mott insulators.

Luttinger compensated antiferromagnets (LcAFMs), combining spin polarization with vanishing net magnetization, offer distinct advantages for next-generation spintronic applications. Using first-principles calculations, we demonstrate that conventional antiferromagnetic CrCl2 double chains can be transformed into one-dimensional LcAFMs under an external electric field, exhibiting pronounced isotropic spin splitting. The magnitude of the splitting, as well as the bandgap, can be effectively tuned by both in-plane and out-of-plane fields, thereby providing greater controllability than in two-dimensional counterparts. To further enhance the tunability, we design a nearly lattice-matched CrCl2/MoTe2 heterostructure and uncover that interfacial charge transfer generates a built-in electric field, inducing spin splitting comparable to that driven by external fields. These results establish interfacial engineering as a highly efficient route to realize and manipulate LcAFM states in low-dimensional magnets, expanding the design principles for spintronic functionalities at the nanoscale.

Current Vision-Language Models (VLMs) often struggle to handle complex visual tasks that require consistent and fine-grained reasoning. Recent methods aim to train models to facilitate self-reflective reasoning, i.e., reviewing and improving the generated reasoning. However, they require large volumes of annotated data and lack explicit reflective behavior during test time. By contrast, humans perform explicit and efficient self-reflection through mechanisms such as backward prediction, i.e., predicting which current states are likely to precede a given future state. Inspired by neuroscience, this work proposes a novel solution to address these challenges. We first observe and investigate the phenomenon that mainstream VLMs can perform backward prediction, similar to the human brain. A label-free training framework named Brain-inspired Unsupervised Self-reflection (BUS) is proposed to leverage and exploit backward prediction capability to enhance reflective reasoning in complex visual tasks. BUS enables self-verification of reflective reasoning based on backward prediction, providing explicit learning signals under unsupervised conditions. In this way, BUS eliminates reliance on annotated data while improving reasoning performance. Designed as a model-agnostic plug-in, our framework is compatible with popular fine-tuning methods, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Initialized from Qwen3-VL-8B, it improves HR-Bench-8K (+8.0

Mechanical computing represents a highly promising paradigm for environment-adaptive information processing. However, existing implementations are generally constrained by limited architectural scalability, and their modes of application in practical scenarios remain insufficiently defined. Here, we develop a light-programmable mechanical computing system that not only performs scalable logic operations but also enables environment-adaptive optical camouflage. The system is based on a polyaniline composite film (PCF) that integrates light-responsive expansion-contraction elements with a flexible conductive layer. Light illumination dynamically modulates the conductive pathways, giving rise to optically controlled single-pole single-throw (SPST) and single-pole double-throw (SPDT) relays that reconfigure signal transmission routes. Interconnecting these relays enables the construction of basic logic gates and 2-bit full-adder circuits, establishing a scalable paradigm for light-programmable mechanical computation. Moreover, we implement an adaptive camouflage function that senses environmental textures and generates matching optical patterns, demonstrating potential for intelligent skin applications capable of environmental interaction. This work establishes a light-programmable, pathway-reconfigurable mechanical computing framework, expanding possibilities for autonomous and adaptive intelligent systems.

Real-time sensing and processing of a large amount of tactile information is essential for intelligent robotics and wearable technology. However, physical separation between sensors and processors in the traditional tactile sensing scheme makes these functionalities inaccessible, posing a major roadblock to the rapid advance of skinomorphic electronics. Here, we propose a massively parallel in-sensor skinomorphic computing scheme and demonstrate its promising applications in intelligent tactile perception. This scheme allows for achieving parallel sensing and processing of tactile information directly within sensor. We implement this proposed scheme by fabricating a 32×32 flexible capacitive pressure sensors array with excellent uniformity and endurance, and by cascading the sensors array with a memristive crossbar array. We experimentally demonstrate that the broken pressure patterns of the letter 'NJU' loaded on the sensors array can be sensed and restored in parallel, which is inaccessible with previously reported tactile technologies. Moreover, by networking the pressure sensors array with two memristive crossbar arrays, we show that textural features of the loaded complex pressure patterns can be directly extracted in a parallel manner and the tactile information can thus be compressed. Our work opens up an avenue for developing intelligent skins capable of real-time and high-throughput tactile perception.

Orbital-lattice coupling in transition-metal oxides is usually discussed within a given bonding framework, where orbital occupation is intertwined with local coordination, strain, or symmetry breaking. Here, we show that orbital occupation can also select the bonding framework itself, thereby determining structural dimensionality. Using first-principles calculations, we identify CrO as a prototype in which the single active 3d e_g electron of high-spin Cr^2+ gives rise to two competing orbital-structure states. The d_x^2-y^2 occupation favors a three-dimensionally connected covalent phase, whereas the d_z^2 occupation stabilizes a weakly coupled layered phase. Constrained-occupation calculations show that increasing the d_z^2 filling continuously contracts the in-plane lattice while expanding the structure along the layer normal. The two phases exhibit distinct magnetic ground states and ferroelastic responses. Moreover, the layered phase is robust against exchange-correlation functional and on-site (U) variations, remains dynamically stable down to the monolayer limit, and has a low exfoliation energy of 46 meV/Angstrom^2. Extending the analysis across related 3d binary oxides reveals a filling-dependence relation between accessible orbital filling and the preference for 2D or 3D connected bonding motifs, providing a microscopic basis for exploring low-dimensional oxide materials.

Infrared-visible fused photodetection presents significant potential for target perception in complex scenarios. However, dual-band imaging inherently generates a considerable amount of redundant data, highlighting a pressing need to perform compressive sensing directly at the pixel level. Here, we report a photodetector composed of a MoS2/h-BN/PdSe2 vertically stacked heterostructure with a common metal electrode interconnecting the bottom PdSe2 channel with the top MoS2 channel. By exploiting the property that infrared light can penetrate deeper than visible light, the bottom PdSe2/Au photovoltaic Schottky junction in this photodetector can detect the infrared light and drive the top MoS2 channel able to detect the visible light. Moreover, by applying voltage at the external drain terminal, the output photocurrent can be further enhanced or suppressed depending on the voltage polarity. The detector receives dual-band optical inputs but outputs only a single electrical signal, allowing for in-pixel dual-band intercorrelated compressive sensing. The physical process of infrared photoresponse that drives the visible photoresponse occurs directly within the detector, enabling the filtration and extraction of targets of interest based on the intensity of infrared irradiation at the pixel level. This work offers a compact and energy-efficient solution for multispectral optical information compressive sensing and processing in complex environments.

Memristor-based computing-in-memory (CIM) architectures have emerged as a promising solution for enhancing the computational efficiency of deep neural networks (DNNs). However, memristors are inherently vulnerable to non-idealities, including manufacturing-induced variations and operational fluctuations. These non-idealities cause deviations in programmed weights, compromising the computational accuracy of neural networks. Although software-based approaches exist to mitigate these issues, they typically target specific error types or require additional components and specialized architectures. These requirements significantly limit the scalability and broader adaptation of CIM systems. In this study, we propose a multi-teacher robust distillation training framework to address the discrepancies between analytically derived training information and the imprecision inherent in analog devices. This framework offers a generalized solution that is independent of network architecture and task type, making it applicable to a wide range of tasks, including image classification, object segmentation, and image denoising. Moreover, it can be seamlessly integrated into models exhibiting various non-ideal behaviors. The proposed method significantly mitigates accuracy degradation in memristor-based CIM systems, paving the way for more reliable and scalable deployment in real-world applications. Experimentally, we created a one-transistor-one-memristor (1T1R) chip to verify the classification and denoising tasks. The results demonstrated that, in a statistical distribution with a standard deviation of 0.5 in weight variations, the accuracy rate was 33.7% higher than that of nominal networks on classification with CIFAR-10. Compared to other variation-aware algorithms, it also achieved the best performance and generalization ability.

Noncollinear magnetic orders in monolayer van der Waals magnets are crucial for probing delicate magnetic interactions under minimal spatial constraints and advancing miniaturized spintronic devices. Despite their significance, achieving atomic-scale identification remains challenging. In this study, we utilized spin-polarized scanning tunneling microscopy and density functional theory calculations to identify spin-spiral orders in mono- and bilayer NiI2, grown on graphene-covered SiC(0001) substrates. We found two distinct spin-spiral states with Q vectors aligning and deviating by 7° from the lattice direction, exhibiting periodicities of 4.54 and 5.01 times the lattice constant, respectively. These findings contrast with bulk properties and align closely with our theoretical calculations. Surprisingly, the nonmultiples of spin spirals within finite-sized magnetic domains induce net magnetic moments, facilitating collective spin switching behavior under magnetic fields. Our research reveals intrinsic noncollinear magnetism at the monolayer limit with atomic-scale resolution, paving the way for exploring spin phenomena.

Altermagnetism has recently drawn considerable attention in three- and twodimensional materials. Here, we extend this concept to quasi-one-dimensional (Q1D) monolayers assembled from single-atomic magnetic chains. Through systematically examining nine types of structures, two stacking orders, and intra-/inter-chain magnetic couplings, we identify four out of thirty promising structural prototypes for hosting altermagnetism, which yields 192 potential monolayer materials. We further confirm eight thermodynamically stable Q1D monolayers via high-throughput calculations. Using symmetry analysis and first-principles calculations, we find that the existence of altermagnetism is determined by the type of inter-chain magnetic coupling and predict three intrinsic altermagnets,CrBr_3,VBr_3,MnBr_3,due to their ferromagnetic inter-chain couplings and five extrinsic ones,CrF_3,CrCl_3,CrI_3,FeCl_3and CoTe_3, ascribed to their neglectable or antiferromagnetic inter-chain couplings. Moreover, the inter-chain magnetic coupling here is highly tunable by manipulating the inter-chain spacing, leading to experimentally feasible transitions between altermagnetic and nodal-line semiconducting states. In addition, applying external electric fields can further modulate the spin splitting. Our findings establish a highly tunable family of Q1D altermagnets, offering fundamental insights into the intricate relationship between geometry, electronic structure, and magnetism.These discoveries hold significant promises for experimental realization and future spintronic applications.

Kagome materials have attracted extensive attention due to their correlated properties. The breathing kagome material system Nb3X8 (X = F, Cl, Br, I) is regarded as a Mott insulator. However, studies on the influence of interlayer coupling on its magnetic and Mott properties are lacking. In this work, we investigated the effect of interlayer coupling on bilayer properties of each Nb3X8 (X = F, Cl, Br, I) compound via density functional theory (DFT) calculations, considering 24 stacking configurations per material. We found that each bilayer material is a Mott insulator. Due to the competition between interlayer Pauli repulsion and hopping, most interlayer magnetism is AFM, a small number of cases show AFM-FM degeneracy, and the magnetic ground state of 3 configurations is interlayer FM, i.e., tunable interlayer magnetism occurs. This robustness of Mott states coexisting with tunable interlayer magnetism provide novel and comprehensive analysis and insights for the research of breathing kagome Mott insulators.

Two-dimensional multiferroics promise low-power, multifunctional devices, yet the intrinsic coexistence and mutual control of three coupled ferroic orders in a single layer remains elusive. Here, we identify pentagonal monolayer FeO2 as an intrinsic triferroic altermagnet where ferroelectric (FE), ferroelastic (FA), and altermagnetic (AM) orders coexist and are tightly coupled, accompanied by a competing antiferroelectric (AFE) phase using first-principles calculations. The sole presence of glide mirror Mx symmetry in a FeO2 sublayer, with the breaking of fourfold rotation C4z symmetry, induces in-plane vector ferroelectricity and twin-related ferroelastic strains. Both FE and AFE phases break combined parity-time symmetry and display sizable altermagnetic spin splitting with N & eacute;el temperatures over 200 K. Electric-field induced rotation of the FE polarization reverses the sign of the spin splitting, while in-plane uniaxial strain triggers ferroelastic switching that simultaneously rotates the FE polarization vector by 90 degrees and reverses the AM state. These electric-field-and strain-mediated pathways interlink six distinct polarization states that can be selected purely by electric fields and/or mechanical strain. This work extends intrinsic triferroicity to pentagonal monolayers and outlines a symmetry-based route toward mechanically and electrically configurable altermagnetic spintronics.

Altermagnetism has recently drawn considerable attention in three- and two-dimensional materials. Here we extend this concept to quasi-one-dimensional (Q1D) monolayers assembled from single-atomic magnetic chains. Through systematically examining nine types of structures, two stacking orders, intra- and interchain magnetic couplings, we identify four out of 30 promising structural prototypes for hosting altermagnetism, which yields 192 potential monolayer materials. We further confirm eight thermodynamically stable Q1D monolayers via high-throughput calculations. Using symmetry analysis and first-principles calculations, we find that the existence of altermagnetism is determined by the type of interchain magnetic coupling and predict three intrinsic altermagnets, CrBr3, VBr3, and MnBr3, due to their ferromagnetic interchain couplings and five extrinsic ones, CrF3, CrCl3, CrI3, FeCl3, and CoTe3, ascribed to their neglectable or antiferromagnetic interchain couplings. Moreover, the interchain magnetic coupling here is highly tunable by manipulating the interchain spacing, leading to experimentally feasible transitions between altermagnetic and nodal-line semiconducting states. In addition, applying external electric fields can further modulate the spin splitting. Our findings establish a highly tunable family of Q1D altermagnets, offering fundamental insights into the intricate relationship between geometry, electronic structure, and magnetism. These discoveries hold significant promises for experimental realization and future spintronic applications.

Noncooperative target detection in real-world scenarios relies on large-scale deep neural networks after image capture. However, directly implementing this detection pipeline under conventional optoelectronic sensors and computing units leads to physical bottlenecks in latency and energy consumption. Here, inspired by the biological visual attention mechanism and leveraging fabricated two-dimensional optoelectronic van der Waals heterostructure devices, we present a highly efficient neuromorphic in-sensor target detection pipeline. The inherent physical process of infrared self-driven visible photoresponse in heterostructures is used to simplify the originally complex processing in artificial intelligence (AI) optical detection algorithms. Specifically, the high-cost target localization and image fusion process can be directly implemented in the sensing unit. This manipulation decreases redundant information at the sensor level, reducing the burden on data transmission and backend computation. The results show that our bioinspired pipeline achieves a mean average precision (mAP) of 95.85% in detecting real-world scenes, even in extreme environments. Meanwhile, significant reductions in computing load (31.65%), latency (95.66%), and energy consumption (21.25%) can be attained compared with previous research. Our work provides a scalable material-for-AI solution for real-time, highly efficient target detection applications in real-world settings.

Defect engineering provides a precise and controlled approach to modify the localized electronic properties through crystalline interruption. In 2D electron-correlated materials, periodic lattice distortions often coexist with charge density waves (CDWs) and Mott insulating states, which are highly sensitive to local electronic environments. However, the influence of complex, inequivalent defect sites on electron-correlated properties, particularly Mott behavior, remains poorly understood. Here, density functional theory calculation is utilized to investigate the electron-correlated properties of monolayer T-NbSe2 with various single selenium/niobium vacancies. It is found that a single vacancy can induce geometric alterations over several nanometers, distinguished from typical 2D materials. A unique selenium vacancy site can precisely eliminate Mott electrons of T-NbSe2 and gradually lead the transitions from a ferromagnetic charge transfer insulator into a non-magnetic band insulator. Moreover, writing in and erasing Mott electrons can be flexibly manipulated by substituting the selenium site with arsenic, bromine, and potassium elements. The modulation mechanism by selenium vacancy originates from a synergistic combination of compressive strain and electron doping. The results systematically reveal that defect engineering is an ingenious strategy for atomically manipulating electron-correlated properties and manufacturing electronic patterns, enabling the control of Mott electrons in 2D materials.

Two-dimensional(2D)moiré superlattices have emerged as a versatile platform for uncovering exotic quantum phases,many of which arise in bilayer systems exhibiting Archimedean tessellation patterns such as triangular,hexagonal,and kagome lattices.Here,we propose a strategy to engineer semiregular tessellation patterns in untwisted bilayer graphene by applying anisotropic epitaxial tensile strain(AETS)along crystallographic directions.Through force-field and first-principles calculations,we demonstrate that AETS can induce a rich variety of semiregular tessellation geometries,includ-ing truncated hextille,prismatic pentagon,and brick-phase arrangements.Characteristic electronic Dirac and flat bands of the lattice models associated with these semiregular tessellations are observed near the Fermi level,arising from interlayer interactions generated by the spatial rearrangement of AB,BA,and SP domains.Furthermore,the real-space observations of electronic kagome,distorted Lieb,brick-like,and one-dimensional stripe lattices demonstrate that AETS enables tunable semiregular tessellation lattices.Our study identifies AETS as a promising new degree of freedom in moiré engineering,offering a reproducible and scalable platform for exploring exotic electronic lattices in moiré systems.