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Spatial‐temporal time series analysis and forecasting are crucial for understanding dynamic systems and making informed decisions. Recurrent neural networks (RNNs) have paved the way for reservoir computing (RC), a method enabling effective temporal information processing at low training costs. While software‐based RC performs well, physical RC systems face challenges like slow processing speed and limited state richness, leading to high hardware costs. This study introduces an innovative approach, i.e., the antiferroelectric field effect transistor‐based RC (AFeFET‐based RC) system for efficient temporal data processing. By exploiting the fading memory property inherent in hafnium oxide‐based antiferroelectric material, this system demonstrates promise for physical RC implementation. Moreover, it leverages the light sensitivity of 2D molybdenum disulfide (MoS2) channels for controllable temporal dynamics under electrical and optical stimuli. This dual‐mode modulation significantly enriches the reservoir state, boosting overall system performance. Experimental tests on standard benchmarking tasks using the AFeFET‐based RC system yielded impressive accuracy results (95.4%) in spoken‐digit recognition and a remarkable normalized root mean square error (NRMSE) of 0.015 in Mackey–Glass time series prediction. [ABSTRACT FROM AUTHOR]

Two‐dimensional Materials (2DMs) offer significant promise for advancing device miniaturization and extending Moore's law. Despite the challenges posed by high contact resistance in transistors, recent discoveries highlight semimetals as an effective approach for achieving ohmic contact with near‐quantum‐limit contact resistance. The energy band hybridization between semimetal and MoS2 is found to create degenerate states and heavily doped contact, which is proposed as the underlying mechanism responsible for reducing contact resistance. However, a quantitative and comprehensive characterization of the semimetal‐MoS2 interface is lacking, leaving the physical interactions elusive. This study reveals that semimetals induce n‐type doping and tensile strain in monolayer MoS2 grown using CVD, which serve as the contact resistance and mobility boosters. Among the semimetals investigated, including Bismuth (Bi), Antimony (Sb), and their alloy, Bi results in the highest electron doping of 2 × 1013 cm−2 and a 0.5% tensile strain, leading to reduced contact resistance and enhanced mobility. First‐principles calculations and spectroscopy measurements unveil the impact of electron doping and strain in MoS2, and the thermal effects are subsequently explored. This research underscores the potential of semimetals in boosting device performance and lays the foundation for reducing contact resistance in transistors made from 2D materials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

While transition‐metal thiophosphate (MPX3) materials have been a subject of extensive research in recent years, experimental studies on MPX3‐based memristors are still in their early stages, with device performance being less than ideal. Here, the successful fabrication of high‐yield, high‐performance, and uniform memristors are demonstrated to possess desired characteristics for neuromorphic computing using a single‐crystalline few‐layered manganese phosphorus trisulfide (MnPS3) as a resistive switching medium. The Ti/MnPS3/Au memristor exhibits small switching voltage (<1 V), long memory retention (104 s), fast switching speed (≈20 ns), high On/Off ratio (nearly two orders of magnitude), and simultaneously achieves emulation of synaptic weight plasticity. The microscopic investigation of the structural and chemical characteristics of the few‐layer MnPS3 reveals the presence of structural defects and residual Ti throughout the stacked layer following the application of voltage, which contributes to the uniformity of switching with a low set voltage. With highly linear and symmetric analog weight updates coupled with the capability of accurate decimal arithmetic operations, a high accuracy of 95.15% in supervised learning using the MNIST handwritten recognition dataset is achieved in the artificial neural network. Furthermore, convolutional image processing can be implemented using hardware multiply‐and‐accumulate operation in an experimental memristor crossbar array. [ABSTRACT FROM AUTHOR]

Time‐series analysis and forecasting play a vital role in the fields of economics and engineering. Neuromorphic computing, particularly recurrent neural networks (RNNs), has emerged as an effective approach to address these tasks. Reservoir computing (RC), a type of RNN, offers a powerful and efficient solution for handling nonlinear information in high‐dimensional spaces and addressing temporal tasks. CuInP2S6 (CIPS), a van der Waals material with ion conductivity, shows promise for sequential task processing. Here, a synapse device based on CIPS is demonstrated that exhibits temporal dynamics under electrical stimulation. By controlling Cu+ ion migration, this study successfully emulates synaptic performance, including potentiation and depression characteristics, and RC. Migration of Cu+ ions is confirmed using piezoresponse and Kelvin probe force microscopy. The device achieves low normalized root mean square errors (NRMSE) of 0.04762 and 0.01402 for the Hénon map and Mackey‐Glass series tasks, respectively. For real‐life time‐series prediction based on the Jena temperature database, an overall NRMSE of 0.03339 is achieved. These results highlight the potential of CIPS ion conductivity for real‐time signal processing in machine learning, expanding applications in neuromorphic computing. [ABSTRACT FROM AUTHOR]

In‐memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time‐consuming challenges associated with the von Neumann architecture. The ferroelectric field‐effect transistor (FeFET) technology, with its fast and energy‐efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study, the capabilities of an integrated ferroelectric HfO2 and 2D MoS2 channel FeFET in achieving high‐performance 4‐bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET‐based in‐memory computing for future neuromorphic computing applications. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two‐terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field‐effect transistor (Fe‐FET) based on α‐In2Se3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in α‐In2Se3 is sufficiently investigated by polarization dynamic model. The proposed Fe‐FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time‐series real‐life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro‐RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient (R2) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 × 10−3 are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system. [ABSTRACT FROM AUTHOR]

For Schottky barrier‐modulated memristors based on 2D semiconductors, it has, to date, not been possible to achieve control over defect type and concentration as the measured switching characteristics vary considerably even under similar fabrication conditions. In this work, four distinct types of memristors are identified based on the combination of low and high resistance sequences, as well as volatile and nonvolatile characteristics. All these four types of memristors were previously observed experimentally by different research labs. It is found that the specific behavior of each memristor type can be explained by the Schottky barrier height modulation and current rectification arising from the concerted effects of the concentration, charge polarity and mobility of defects. The conditions required to realize the four types of 2D semiconductor‐based memristors are analyzed and design guidelines for fabricating each of these four types of memristors are provided. [ABSTRACT FROM AUTHOR]

Memristors are intensively studied as being regarded as the critical components to realize the in‐memory computing paradigm. A novel electrochemical metallization memristor based on solution‐processed Pt/CuI/Cu structure is proposed and demonstrated in this work, with a high resistance switching ratio of 1.53 × 107. Owing to the efficient drift paths provided by Cu vacancies for Cu cations in CuI, very small operating voltages (Vset = 0.64 V and Vreset = −0.19 V) are characterized, contributing to ultralow standby power consumption of 9 fW and per set transition of 8.73 µW. Using CuI memristor arrays, a set of Boolean logic operations and a half‐adder are implemented. Moreover, by building the model for a 75 × 48 one‐transistor‐one‐memristor array, the feasibility of hardware encryption and decryption for images is verified. All these demonstrate that solution‐processed CuI memristors possess great potential in constructing energy‐efficient logic‐in‐memory computing architectures. [ABSTRACT FROM AUTHOR]

2D semiconductor field‐effect transistors (2D FETs) have emerged as a promising candidate for beyond‐silicon electronics applications. However, its device performance has often been limited by the metal‐2D semiconductor contact, and the non‐negligible contact resistance (RSD) not only deteriorates the on‐state current but also hinders the direct characterization of the intrinsic properties of 2D semiconductors (e.g., intrinsic charge carrier mobility, μint). Therefore, a proper extraction technique that can independently characterize the metal‐2D semiconductor contact behavior and the intrinsic properties of a 2D semiconducting layer is highly desired. In this study, a universal yet simple method is developed to accurately extract the critical parameters in 2D FETs, including characteristic temperature (To), threshold voltage (VT), RSD, and μint. The practicability of this method is extensively explored by characterizing the temperature‐dependent carrier transport behavior and the strain‐induced band structure modification in 2D semiconductors. Technology computer aided design simulation is subsequently employed to verify the precision of RSD extraction. Furthermore, the universality of the proposed method is validated by successfully implementing the extraction to various 2D semiconductors, including black phosphorus, indium selenide, molybdenum disulfide, rhenium disulfide, and tungsten disulfide with top‐ and bottom‐gated configurations. [ABSTRACT FROM AUTHOR]

Due to their unique chemical and physical properties, 2D inorganic materials (2DMs) have attracted a lot of attention in recent years as an attractive platform for diverse applications in opto‐electronics, energy generation and storage, and sensing. This article presents the progress being made in sensors designed for measuring volatile organic compounds (VOCs) in ambient air. It considers commercially available sensors, such as the amperometric and conductometric types, and the recent advancements in VOCs sensors employing 2DMs and their nanocomposites. Their performance is summarized in a graphical form and the challenges and possible mitigation methods for these types of VOC sensor are highlighted. [ABSTRACT FROM AUTHOR]

Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high‐density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large‐scale CBAs. [ABSTRACT FROM AUTHOR]

Memristors with biological synaptic behaviors and functions have been intensively studied as an important component for neuromorphic computing system, which hold promise to address the power consumption issue in modern computers based on von Neumann architecture. However, the resistive switching mechanism that relies on the stochastic formation of conductive filaments leads to poor cycle‐to‐cycle (temporal) and cell‐to‐cell (spatial) variations for filamentary memristors. The emergence of memristors based on 2D ferroelectric materials can potentially avoid these issues. Here, a vertical Au/CuInP2S6 (CIPS)/Ti diode is demonstrated using exfoliated ferroelectric CIPS flake. Through ferroelectric switching, the CIPS diode realizes resistive switching with a ratio larger than 6 × 103. The endurance measurement shows a small set and reset voltage variation of 5.3% and 9.1%, respectively. Key synaptic behaviors including spike‐time‐dependent plasticity, paired‐pulse‐facilitation, and paired‐pulse‐depression are successfully mimicked, manifesting the potential application of CIPS diode in a neuromorphic computing system. Moreover, pattern learning and memory behaviors are emulated using a 3 × 3 CIPS crossbar array. [ABSTRACT FROM AUTHOR]

Black phosphorus (BP) offers unique opportunities for mid‐infrared (MIR) waveguide photodetectors due to its narrow direct bandgap and layered lattice structure. Further miniaturization of the photodetector will improve operation speed, signal‐to‐noise ratio, and internal quantum efficiency. However, it is challenging to maintain high responsivities in miniaturized BP waveguide photodetectors because of reduced light–matter interaction lengths. To address this issue, a method utilizing the slow light effect in photonic crystal waveguides (PhCWGs) is proposed and experimentally demonstrated. A shared‐BP photonic system is proposed and utilized to fairly and precisely characterize the slow light enhancement. Close to the band edge around 3.8 µm, the responsivity is enhanced by more than tenfold in the BP photodetector on a 10 µm long PhCWG as compared with the counterpart on a subwavelength grating waveguide. At a 0.5 V bias, the BP PhCWG photodetector achieves a 11.31 A W−1 responsivity and a 0.012 nW Hz−1/2 noise equivalent power. The trap‐induced photoconductive gain is validated as both the dominant photoresponse mechanism and the major limiting factor of the response speed. The BP slow light waveguide photodetector is envisioned to realize miniaturized high‐performance on‐chip MIR systems for widespread applications including environmental monitoring, industrial process control, and medical diagnostics. [ABSTRACT FROM AUTHOR]

Neuromorphic computing on the hardware level is promising for performing ever‐increasing data‐centric tasks owing to its superiority to conventional von Neumann architecture in terms of energy efficiency and learning ability. One key aspect to its implementation is the development of artificial synapses that can effectively emulate the multiple functionalities exhibited by their biological counterparts. Here, building on an inorganic ferroelectric gate stack integrated with a 2D layered semiconductor (WS2), a new type of ferroelectricity‐based synaptic transistor that differs from those relying on interface traps or floating gate configuration is reported. By virtue of a 6 nm thick ferroelectric hafnium zirconium oxide by atomic layer deposition and postannealing treatment, the device shows a channel resistance change ratio above 105 corresponding to opposite ferroelectric polarization direction. Furthermore, by applying electrical stimulus to the gate, it demonstrates good capability to mimic various synaptic behaviors including long‐term potentiation, long‐term depression, spike‐amplitude‐dependent plasticity, and spike‐rate‐dependent plasticity. Given the inherent compatibility of the ferroelectric gate stack with existing fabrication technology, and the reliability of ferroelectricity engineering, this work paves the way toward practical implementation of synaptic devices in neuromorphic circuits. [ABSTRACT FROM AUTHOR]

Abstract: Black phosphorus (BP) has attracted enormous interest for logic applications due to its unique electronic properties. However, pristine BP exhibits predominant p‐type channel conductance, which limits the realization of complementary circuits unless an effective n‐type doping is found. Here, a practical approach to transform the conductivity of BP from p‐type to n‐type via a spatially controlled aluminum (Al) doping is proposed. Symmetrical threshold voltage for the pair of p‐type and n‐type BP field‐effect transistors can be achieved by tuning the Al doping concentration. The complementary inverter circuit shows a clear logic inversion with a high voltage gain of up to ≈11 at a supply voltage (VDD) of 1.5 V. Simultaneously, a high noise margin of 0.27 × VDD is achieved for both low (NML) and high (NMH) input voltages, indicating excellent noise immunity. Moreover, a three‐stage ring oscillator with a theoretical frequency above 1.8 GHz and microwatt level power dissipation is modeled, which shows a low propagation delay per stage. This study demonstrates a practical approach to fabricate high performance complementary integrated circuits on a homogenous BP channel material, paving the way toward complex cascaded circuits and sensor interface applications. [ABSTRACT FROM AUTHOR]

Abstract: Achieving excellent electrostatic control and immunity to short channel effects are the formidable challenges in ultrascaled devices. 3D device architectures, such as nanoribbon, have successfully mitigated these problems by achieving uniform top‐ and side‐wall control of the channel. Here, by leveraging on the merits of 3D structure, high‐mobility black phosphorus nanoribbon field‐effect transistors (BPNR‐FET) are demonstrated and the anisotropic transport properties are systematically investigated. A simple top‐down reactive ion etching method is used to realize both armchair‐ and zigzag‐oriented nanoribbons with various widths down to 60 nm. The mobility of BPNR‐FET is found to be width‐ and thickness‐dependent, with the highest hole mobility of ≈862 cm2 V−1 s−1 demonstrated in armchair‐oriented device at room temperature by combining high‐κ gate dielectric and hydrogen treatment to reduce sidewall scattering. Furthermore, hydrogenation effectively passivates the nanoribbon dangling bonds, leading to hysteresis and contact resistance improvement. This work unravels the superior electrical performance underscore a conceptually new device based on BP nanoribbons, paving the way toward the development of nonplanar devices on 2D materials platform. [ABSTRACT FROM AUTHOR]

Abstract: Recently, both lateral and vertical p–n junctions have been realized in 2D materials using various strategies, with a number of works on exploring the potential of lateral heterojunctions resulting from thickness‐modulated bandgaps at the interface. Here, electrically tunable all‐black‐phosphorus (BP) lateral heterojunction diodes, without the need of split‐gating or selective chemical doping or transfer‐based vertical stacking, are experimentally demonstrated. The BP heterojunction diode, which exhibits an ultralow off‐state current density of 8 pA µm−1 at a mere Vd of 100 mV and a significant gate‐tunable current‐rectifying behavior with the highest rectification ratio exceeding 600, is able to harvest solar energy at both visible and near‐infrared wavelengths beyond the bandgap limitation of transition metal dichalcogenides. Specifically, at 660 nm, the device achieves an open‐circuit voltage (Voc) of 210 mV and a short‐circuit current (Isc) of 1.5 nA at 3.6 W cm−2 power density, resulting in an external quantum efficiency of 7.4% which outperforms both split‐gating and chemically doped homojunctions. This work paves the way for the exploitation of BP lateral heterojunction for broadband energy harvesting towards future optoelectronic applications. [ABSTRACT FROM AUTHOR]

Monolayer W xMo1− xS2-based field effect transistors are demonstrated for the first time on the monolayer W xMo1− xS2 flake, which is grown by the chemical vapor deposition method under an atmospheric pressure. Detailed material studies using Raman and photoluminescence measurements have been carried out on the as-grown monolayer W xMo1− xS2. Electronic band structure of monolayer W xMo1− xS2 has been calculated using first-principle theory. The thermal stability of monolayer W xMo1− xS2 has been evaluated using Raman-temperature measurement. Carrier transport study on the fabricated W xMo1− xS2 FETs has been analyzed using temperature-dependent current measurement, and a field effect mobility of ≈30 cm2 V−1 s−1 at 300 K is obtained. [ABSTRACT FROM AUTHOR]

2D layered materials based p-n junctions are fundamental building block for enabling new functional device applications with high efficiency. However, due to the lack of controllable doping technique, state-of-the-art 2D p-n junctions are predominantly made of van der Waals heterostructures or electrostatic gated junctions. Here, the authors report the demonstration of a spatially controlled aluminum doping technique that enables a p-n homojunction diode to be realized within a single 2D black phosphorus nanosheet for high performance photovoltaic application. The diode achieves a near-unity ideality factor of 1.001 along with an on/off ratio of ≈5.6 × 103 at a low bias of 2 V, allowing for low-power dynamic current rectification without signal decay or overshoot. When operated under a photovoltaic regime, the diode's dark current can be significantly suppressed. The presence of a built-in electric field additionally gives rise to temporal short-circuit current and open-circuit voltage under zero external bias, indicative of its enriched functionalities for self-powered photovoltaic and high signal-to-noise photodetection applications. [ABSTRACT FROM AUTHOR]

Neuromorphic Computing: A Fully Printed Flexible MoS2 Memristive Artificial Synapse with Femtojoule Switching Energy (Adv. In article number 1900740, Tawfique Hasan, Aaron Thean, Yong-Wei Zhang, Kah-Wee Ang, and co-workers report the demonstration of flexible MoS2 memristive artificial synapses via scalable, low-temperature aerosol-jet printing. Fully printed memristors in a cross-bar structure enable efficient emulation of synaptic plasticity functions with femtojoule energy consumption. [Extracted from the article]

2D transition metal dichalcogenide (TMD)‐based phototransistors generally work under photoconductive, photovoltaic, or photogating mode, in which photocarriers are generated from band‐to‐band excitation. Nevertheless, due to the relatively large bandgap, most TMD phototransistors working under these modes are restricted in visible spectrum. Here, photodetection in 2D multilayer rhenium disulfide (ReS2) transistor via bolometric mode, which relies on light heating induced conductance change instead of band‐to‐band photoexcitation is reported, making it possible for sub‐bandgap photon detection. The bolometric effect induced photoresponse is first revealed by an anomalous sign switching of photocurrent from positive to negative while increasing gate voltage under visible light, which is further validated by the temperature dependent electrical transport measurements. The phototransistor exhibits remarkable photoresponse under infrared regime, beyond the optical bandgap absorption edge of the ReS2 flake. Additionally, it demonstrates a low noise equivalent power, less than 5 × 10−2 pW Hz−1/2, which is very promising for ultra‐weak light detection. Moreover, the response time is below 3 ms, nearly 3–4 orders of magnitude faster than previously reported ReS2 photodetectors. The findings promise bolometric effect as an effective photodetection mode to extend the response spectrum of large bandgap TMDs for novel and high‐performance broadband photodetectors. [ABSTRACT FROM AUTHOR]

Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based artificial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS2 grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS2 grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning. [ABSTRACT FROM AUTHOR]

The rediscovery of graphene in the recent past has propelled the rapid development of exfoliation and other thin layer processing techniques, leading to a renewed interest in black phosphorus (BP). Since 2014, BP has been extensively studied due to its superior electronic, photonic, and mechanical properties. In addition, the unique intrinsic anisotropic characteristics resulting from its puckered structure can be utilized for designing new functional devices. In retrospect, significant efforts have been directed toward the synthesis, basic understanding, and applications of BP in the fields of nanoelectronics, ultrafast optics, nanophotonics, and optoelectronics. Here, the recent development of BP‐based devices, such as nanoribbon field‐effect transistors, complementary logic circuits, memory devices, and the progress made in meeting the challenges associated with contact resistance, in‐plane anisotropy, and advanced gate stack, are reviewed. Finally, the prospects of 2D materials in meeting the International Technology Roadmap for Semiconductor requirements for the year 2030 are discussed. The recent development of black phosphorus based electronic devices, such as nanoribbon field‐effect transistors, complementary logic circuits, memory, and the progress made in meeting the challenges associated with contact resistance, in‐plane anisotropy, and advanced gate stack are reviewed. The prospects of 2D materials in meeting the International Technology Roadmap for Semiconductor requirements for the year 2030 are also discussed. [ABSTRACT FROM AUTHOR]

A high performance photodetector array on transparent substrate is highly sought after for enabling next‐generation imaging technology at the visible wavelengths. 2D materials such as molybdenum disulfide (MoS2) are attractive for such application owing to its superior optoelectronic properties and transparency when scaled to atomic thinness. Here, direct growth of MoS2 on centimeter‐scale transparent Al2O3 substrate is reported using a high yield and scalable chemical vapor deposition approach. This enables a large area photodetector array to be demonstrated, wherein aluminum split bull eye (SBE) plasmonic structure is integrated to achieve further performance boost due to surface plasmon resonance (SPR) effect. For a wavelength of 405 nm, the plasmonic MoS2 detector achieves an ultralow noise equivalent power of ≈6.2 × 10–14 W Hz−1/2 and a high responsivity of 7.26 A W−1 at a small bias of 1.0 V, which is more than 6× larger than the reference detector due to SPR effect. Finite‐difference time‐domain simulation confirms a higher concentration of optical field distribution at the center of the SBE structure, which is responsible for the enhancement of photocurrent and sensitivity even at low‐light condition. A large‐scale transparent MoS2‐on‐Al2O3 plasmonic photodetector array featuring split bull eye structure is demonstrated using CMOS‐compatible aluminum as the plasmonic metal. The split bull eye plasmonic photodetector shows the highest achievable responsivity of 7.26 A W−1 at 405 nm wavelength for a small bias of 1.0 V, which is ≈3× and 6× higher than the reference detector due to surface plasmon resonance effect. [ABSTRACT FROM AUTHOR]