An artificial neural network chip based on two-dimensional semiconductor

Adaptative machine vision with microsecond-level accurate perception beyond human retina

Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as feedforward inhibition in bionic two-dimensional (2D) transistor is proposed for fast and high-frequency visual adaptation behavior with microsecond-level accurate perception, the adaptation speed is over 104 times faster than that of human retina and reported bionic sensors. As light intensity changes, the bionic transistor spontaneously switches between avalanche and photoconductive effect, varying responsivity in both magnitude and sign (from 7.6 × 104 to -1 × 103 A/W), thereby achieving ultra-fast scotopic and photopic adaptation process of 108 and 268 μs, respectively. By further combining convolutional neural networks with avalanche-tuned bionic transistor, an adaptative machine vision is achieved with remarkable microsecond-level rapid adaptation capabilities and robust image recognition with over 98% precision in both dim and bright conditions.

Compositional effects of hybrid MoS2-GO active layer on the performance of unipolar, low-power and multistate RRAM device

Currently, 2D nanomaterials-based resistive random access memory (RRAMs) are explored on account of their tunable material properties enabling fabrication of low power and flexible RRAM devices. In this work, hybrid MoS2-GO based active layer RRAM devices are investigated. A facile hydrothermal co-synthesis approach is used to obtain the hybrid materials and a cost-effective spin coating method adopted for the fabrication of Ag/MoS2-GO/ITO RRAM devices. The performance of the fabricated hybrid active layer RRAM device is analysed with respect to change in material properties of the synthesized hybrid material. The progressive addition of 0.5, 1.5, 2.5 and 4.5 weight % of GO to MoS2, results in a hybrid active layer with higher intermolecular interaction, in the case of Ag/MoS2-GO4.5/ITO RRAM device, resulting in a unipolar resistive switching RRAM behavior with low SET voltage of 1.37 V and highIon/Ioffof 200 with multilevel resistance states. A space charge limited conduction mechanism is obtained during switching, which may be attributed to the trap states present due to functional groups of GO. The increased number of conduction pathways on account of both Ag+ions and oxygen vacancies (Vo2+), participating in the formation of conducting filament, results in higherIon/Ioff. This is the first report of unipolar Ag/MoS2-GO/ITO RRAM devices, which are particularly important in realizing high density crossbar memories for neuromorphic and in-memory computing as well as enabling flexible 2D nanomaterials-based memristor applications.

Advanced lithography materials: From fundamentals to applications

The semiconductor industry has long been driven by advances in a nanofabrication technology known as lithography, and the fabrication of nanostructures on chips relies on an important coating, the photoresist layer. Photoresists are typically spin-coated to form a film and have a photolysis solubility transition and etch resistance that allow for rapid fabrication of nanostructures. As a result, photoresists have attracted great interest in both fundamental research and industrial applications. Currently, the semiconductor industry has entered the era of extreme ultraviolet lithography (EUVL) and expects photoresists to be able to fabricate sub-10 nm structures. In order to realize sub-10 nm nanofabrication, the development of photoresists faces several challenges in terms of sensitivity, etch resistance, and molecular size. In this paper, three types of lithographic mechanisms are reviewed to provide strategies for designing photoresists that can enable high-resolution nanofabrication. The discussion of the current state of the art in optical lithography is presented in depth. Practical applications of photoresists and related recent advances are summarized. Finally, the current achievements and remaining issues of photoresists are discussed and future research directions are envisioned.

A synapse with low power consumption based on MoTe2/SnS2heterostructure

The use of two-dimensional materials and van der Waals heterostructures holds great potential for improving the performance of memristors Here, we present SnS2/MoTe2heterostructure synaptic transistors. Benefiting from the ultra-low dark current of the heterojunction, the power consumption of the synapse is only 19pJ per switching under 0.1 V bias, comparable to that of biological synapses. The synaptic device based on the SnS2/MoTe2demonstrates various synaptic functionalities, including short-term plasticity, long-term plasticity, and paired-pulse facilitation. In particular, the synaptic weight of the excitatory postsynaptic current can reach 109.8%. In addition, the controllability of the long-term potentiation and long-term depression are discussed. The dynamic range (Gmax/Gmin) and the symmetricity values of the synaptic devices are approximately 16.22 and 6.37, and the non-linearity is 1.79. Our study provides the possibility for the application of 2D material synaptic devices in the field of low-power information storage.

Memristor-Based Neuromorphic Chips

In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.

Applications of Transistor-Based Biochemical Sensors

Transistor-based biochemical sensors feature easy integration with electronic circuits and non-invasive real-time detection. They have been widely used in intelligent wearable devices, electronic skins, and biological analyses and have shown broad application prospects in intelligent medical detection. Field-effect transistor (FET) sensors have high sensitivity, reasonable specificity, rapid response, and portability and provide unique signal amplification during biochemical detection. Organic field-effect transistor (OFET) sensors are lightweight, flexible, foldable, and biocompatible with wearable devices. Organic electrochemical transistor (OECT) sensors convert biological signals in body fluids into electrical signals for artificial intelligence analysis. In addition to biochemical markers in body fluids, electrophysiology indicators such as electrocardiogram (ECG) signals and body temperature can also cause changes in the current or voltage of transistor-based biochemical sensors. When modified with sensitive substances, sensors can detect specific analytes, improve sensitivity, broaden the detection range, and reduce the limit of detection (LoD). In this review, we introduce three kinds of transistor-based biochemical sensors: FET, OFET, and OECT. We also discuss the fabrication processes for transistor sources, drains, and gates. Furthermore, we demonstrated three sensor types for body fluid biomarkers, electrophysiology signals, and development trends. Transistor-based biochemical sensors exhibit excellent potential in multi-mode intelligent analysis and are good candidates for the next generation of intelligent point-of-care testing (iPOCT).

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Anthropogenic sources of fine particulate matter (PM_2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM_2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM_2.5 concentration in Shanghai. The PM_2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM_2.5 concentration in Shanghai. The PM_2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM_2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM_2.5 concentration predicting models were the PM_2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM_2.5 in the next day and the input influence factors, the PM_2.5 showed the closest relation with the PM_2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM_2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM_2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM_2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

Integrated Memory Devices Based on 2D Materials

With the advent of the Internet of Things and big data, massive data must be rapidly processed and stored within a short timeframe. This imposes stringent requirements on memory hardware implementation in terms of operation speed, energy consumption, and integration density. To fulfill these demands, 2D materials, which are excellent electronic building blocks, provide numerous possibilities for developing advanced memory device arrays with high performance, smart computing architectures, and desirable downscaling. Over the past few years, 2D-material-based memory-device arrays with different working mechanisms, including defects, filaments, charges, ferroelectricity, and spins, have been increasingly developed. These arrays can be used to implement brain-inspired computing or sensing with extraordinary performance, architectures, and functionalities. Here, recent research into integrated, state-of-the-art memory devices made from 2D materials, as well as their implications for brain-inspired computing are surveyed. The existing challenges at the array level are discussed, and the scope for future research is presented.

Two-dimensional devices and integration towards the silicon lines

Despite technical efforts and upgrades, advances in complementary metal-oxide-semiconductor circuits have become unsustainable in the face of inherent silicon limits. New materials are being sought to compensate for silicon deficiencies, and two-dimensional materials are considered promising candidates due to their atomically thin structures and exotic physical properties. However, a potentially applicable method for incorporating two-dimensional materials into silicon platforms remains to be illustrated. Here we try to bridge two-dimensional materials and silicon technology, from integrated devices to monolithic 'on-silicon' (silicon as the substrate) and 'with-silicon' (silicon as a functional component) circuits, and discuss the corresponding requirements for material synthesis, device design and circuitry integration. Finally, we summarize the role played by two-dimensional materials in the silicon-dominated semiconductor industry and suggest the way forward, as well as the technologies that are expected to become mainstream in the near future.

Wearable Sensor for Continuous Sweat Biomarker Monitoring

In recent years, wearable sensors have enabled the unique mode of real-time and noninvasive monitoring to develop rapidly in medical care, sports, and other fields. Sweat contains a wide range of biomarkers such as metabolites, electrolytes, and various hormones. Combined with wearable technology, sweat can reflect human fatigue, disease, mental stress, dehydration, and so on. This paper comprehensively describes the analysis of sweat components such as glucose, lactic acid, electrolytes, pH, cortisol, vitamins, ethanol, and drugs by wearable sensing technology, and the application of sweat wearable devices in glasses, patches, fabrics, tattoos, and paper. The development trend of sweat wearable devices is prospected. It is believed that if the sweat collection, air permeability, biocompatibility, sensing array construction, continuous monitoring, self-healing technology, power consumption, real-time data transmission, specific recognition, and other problems of the wearable sweat sensor are solved, we can provide the wearer with important information about their health level in the true sense.

TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design

The reliability of memory is an important issue. The rapid development of transistor technology makes the memory more prone to soft errors. Several recent efforts have proposed various designs to avoid the corruption of stored data by using Error Correction Codes (ECC). However, these designs tend to focus on one indicator, which means they cannot balance the electrical timing, area and power consumption constraints with the increasing of the chip-scale and the operating frequency. In this paper, we propose a design named TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design. We achieve higher energy-efficiency and lower hardware cost by using a two-dimensional error correction codes, and evaluate the design by considering the overall system performance. Comparing with the traditional Hamming code, the evaluation shows that the TECED reduces most of fifty percent of the area overhead and twenty-eight point five percent power consumption of the memory at a specific storage capacity.