Leveraging Predictive AI in Telecommunications with RAN Intelligent Controller (RIC)

 

In the dynamic landscape of telecommunications, the RAN Intelligent Controller (RIC) has emerged as a transformative technology. The transition from SON to RIC represents a significant advancement in network automation and intelligence, with predictive AI playing a central role. By distributing real-time processing to edge computing devices and leveraging the computational power of cloud-native platforms in data centers, RIC provides a comprehensive solution for modern network management, optimization, and automation.

Analysts project that the market for RIC and related AI-driven network management solutions will grow significantly in the coming years. According to a report by Allied Market Research, the global AI in the telecom market is expected to reach $14.99 billion by 2026, growing at a CAGR of 42.6% from 2019 to 2026. This growth is driven by the increasing adoption of AI for network optimization, predictive maintenance, and customer experience enhancement.

The incorporation of predictive AI allows telecom operators to anticipate and mitigate potential network issues, ensuring optimal performance and adaptability in an ever-evolving landscape.

Understanding RIC and its predictive AI capabilities

A RAN Intelligent Controller (RIC) is a software-defined component defined by the O-RAN Alliance. The RIC is responsible for controlling and optimizing RAN functions and enables a vendor-agnostic platform to handle control and management planes. Through control and management planes, RICs access the RAN as a whole: elements, connections, and functions.

Standardized interfaces like E2 and A1 are crucial for communication between RIC and other network components, facilitating interoperability and seamless integration within the broader network architecture.

The RIC is a critical piece of the Open RAN disaggregation strategy, enabling multivendor interoperability, intelligence, agility, and programmability to radio access networks. The architecture of RIC is designed to be highly modular and flexible, supporting a wide array of functionalities through its distinct components. The core of RIC comprises two primary controllers: Near-Real-Time RIC (Near-RT RIC) and Non-Real-Time RIC (Non-RT RIC). These controllers manage specific applications, known as xApps for Near-RT RIC and rApps for Non-RT RIC, each implementing targeted functions such as traffic steering, load balancing, anomaly detection, and predictive maintenance.

• Near-Real-Time (Near-RT) RIC executes functions between 10 milliseconds and one second and communicates between the application layer, the non-RT RIC, and the infrastructure layer (O-CU & O-DU), where O-CU has disaggregated control and user planes to add flexibility to the architecture. It directly controls and optimizes the lower levels of the RAN and uses AI and ML to automate the RAN and enforce policies that control routing and quality of service and experience, crucial for reducing latency-sensitive applications.

For near real-time processing, edge computing hardware is a key. Edge devices equipped with high-performance CPUs and GPUs are deployed close to the data source, minimizing latency and enabling rapid decision-making. This proximity to the data source ensures that Near-RT RIC can perform real-time network optimizations, such as dynamic spectrum management and real-time interference detection and mitigation.

Edge computing hardware must support the intense computational requirements of AI and ML algorithms, necessitating robust processing capabilities and low-latency networking. Additionally, edge devices need to be scalable to handle varying traffic loads and adaptable to different deployment environments.

• Non-Real-Time (Non-RT) RIC takes one second or more to execute and its role is to guide near-RT RIC. Non-RT RIC provides the policies, data, and machine learning models necessary for RAN optimization by the near-RT RIC. Non-RT RIC exists in the service management and orchestration (SMO) framework and houses the policies that reinforce the near-RT RIC. It also manages ML models for the near-RT RIC to use for decision-making based on the network’s condition and is responsible for reporting and analytics.

Non-RT RIC functions, which require extensive data analysis and ML model training, are typically hosted on cloud-native platforms in centralized data centers (DCs). These platforms provide the computational power and scalability needed to process vast amounts of data collected from the network. Non-RT RIC performs tasks such as long-term network performance analysis, trend identification, and policy generation.

Cloud-native technologies, including Kubernetes for container orchestration, play a pivotal role in the deployment and management of RIC applications. Kubernetes ensures that xApps and rApps can be efficiently deployed, scaled, and managed across diverse environments, supporting the modular architecture of RIC.

A critical function of Non-RT RIC is to aggregate and correlate data from various sources within the network. This includes processing historical performance data, user behavior analytics, and network status reports. The correlation and reporting mechanisms within Non-RT RIC enable telecom operators to gain deep insights into network performance, predict future trends, and formulate proactive optimization strategies.

By leveraging sophisticated data correlation techniques, Non-RT RIC can identify patterns and anomalies that may not be apparent in real-time analysis. These insights are then used to refine and update the policies and models employed by Near-RT RIC, creating a continuous feedback loop that enhances overall network performance and reliability.

RIC’s modular and scalable architecture enables efficient deployment and management of network functions, paving the way for a more intelligent and resilient network infrastructure. By harnessing the power of predictive AI, RIC optimizes network operations, enhancing performance and efficiency.

As networks become increasingly complex, the need for intelligent, real-time management systems is critical. The RIC serves this purpose by analyzing vast amounts of data and making informed decisions to enhance network performance and efficiency.

Predictive AI in RIC

The integration of predictive AI into the RIC framework represents a transformative step in network management. Predictive AI leverages historical data and real-time inputs to forecast future network conditions and behaviors. This capability is crucial for proactive network management, allowing operators to anticipate and address potential issues before they impact service quality.

In the context of Near-RT RIC, predictive AI can enhance real-time decision-making by providing foresight into imminent network states. For example, predictive models can forecast traffic surges, enabling dynamic resource allocation to prevent congestion and ensure smooth user experiences.

Non-RT RIC benefits significantly from predictive AI by utilizing long-term data trends and patterns to improve strategic planning and optimization. Predictive analytics can inform the development of advanced ML models and policies that preemptively address network challenges, such as capacity planning, fault management, and user experience enhancement.

Predictive AI model training and deployment

The effectiveness of the RAN Intelligent Controller (RIC) relies heavily on the quality and precision of its predictive AI models. The process of training and deploying these models involves several key steps, starting with the extensive collection of data from various sources, including performance metrics, user behavior, traffic patterns, and network events. This data, gathered through standardized interfaces like E2 and A1, is cleaned and transformed into a structured format suitable for machine learning (ML) model training, which includes removing inaccuracies, normalizing values, and extracting relevant features.

Model training leverages advanced ML algorithms. Supervised learning algorithms are used for tasks such as classification and regression, providing predictions based on labeled data. Unsupervised learning algorithms are employed for anomaly detection, identifying patterns and outliers in the data without predefined labels. The training process involves feeding historical and current network data into these algorithms and performing hyperparameter tuning to optimize model performance.

To ensure accuracy and reliability, rigorous validation and testing are conducted. This includes cross-validation, which involves training the model on various subsets of the data to ensure it generalizes well, and evaluating the model using performance metrics such as accuracy, precision, recall, and F1-score. Testing on real-time network data further refines the models, ensuring they perform well in live environments.

Once validated, the models are integrated into the RIC platform. They are containerized using technologies like Docker and managed with Kubernetes for consistent deployment across different environments. The models are then deployed into the Near-RT RIC for real-time tasks, such as dynamic resource allocation, and into the Non-RT RIC for long-term optimization and predictive analytics.

How 3D CAD Software is Revolutionizing Packaging Design and Engineering

 

Why 3D CAD Software is Essential Today

3D CAD (computer-aided design) software has revolutionized the way industries design, develop, and produce products. Unlike traditional 2D design methods, 3D CAD allows for a more realistic and detailed visual representation of objects, making it an indispensable tool across sectors like automotive, healthcare, and architecture. Companies can now rapidly prototype, run simulations, and analyze designs in ways that were previously impossible, speeding up product development and reducing costs.

Read also: AI and the Future of the Packaging Industry

Key Market Insights

As of 2023, North America dominated the global 3D CAD software market, with the Asia-Pacific region expected to grow significantly over the forecast period. This is largely driven by technological advancements, increased industrialization, and growing demand for customized design solutions in industries like automotive, construction, and healthcare.

The on-premises deployment model held the largest market share in 2023. On-premises solutions offer enhanced data security, control, and customization, making them ideal for industries with sensitive data, such as aerospace, automotive, and manufacturing. However, cloud-based solutions are expected to gain momentum due to their flexibility, collaboration features, and reduced infrastructure costs.

The global 3D CAD software market size is projected to witness remarkable growth in the coming decade. With a market value of USD 11.75 billion in 2023, it is expected to more than double, reaching USD 24.22 billion by 2034. This growth, at a robust compound annual growth rate (CAGR) of 6.8%, is being driven by increasing demand for advanced design capabilities, innovative technologies like artificial intelligence (AI), and the growing need for efficiency in various industries.

Industry-Wise Adoption: AEC Leading the Pack

The Architecture, Engineering & Construction (AEC) industry is among the top adopters of 3D CAD software, benefiting from enhanced visualization capabilities and streamlined workflows. 3D models allow project teams to collaborate more effectively, reducing miscommunications and errors. Real-time collaboration through cloud-based CAD tools also helps architects, engineers, and contractors coordinate better, which reduces project timelines and costs.

Other industries, particularly healthcare, are also embracing 3D CAD software at a fast pace. The technology allows healthcare professionals to design patient-specific medical devices, improve surgical planning, and create anatomical models that aid in medical education. With increasing reliance on these tools, healthcare is set to be the fastest-growing segment in the 3D CAD software market by 2034.

Technology Driving Growth

One of the key factors propelling the growth of the 3D CAD software market is the integration of emerging technologies such as AI, IoT (Internet of Things), and augmented reality (AR) or virtual reality (VR). AI, in particular, is transforming the way designs are created. By automating repetitive tasks and optimizing design processes, AI helps improve error detection and enhances overall efficiency.

Companies like Ai Build, based in the UK, are already pushing the boundaries of AI-driven 3D modeling. In June 2024, Ai Build unveiled a new version of its cloud-based AI-powered software, AiBuild 2.0, which incorporates enhanced automation and AI assistance. These advances are driving significant improvements in design quality and efficiency.

Challenges in the Market

Despite its promising growth, the 3D CAD software market faces challenges. One significant hurdle is the steep learning curve associated with using the software. The complexity of 3D modeling often requires extensive training, which can slow adoption, particularly in small and medium-sized enterprises. High upfront costs, including software licenses and hardware requirements, can also deter smaller businesses from fully embracing the technology.

The Role of Research and Development

The ongoing focus on research and development (R&D) is crucial to the future of the 3D CAD software market. Continuous improvements in modeling, simulation, and visualization tools are attracting new users and expanding the capabilities of existing systems. Companies are also increasingly investing in AR and VR integrations, further enhancing the functionality of 3D CAD tools.

In healthcare, for instance, Ricoh USA has launched its flagship 3D medical device manufacturing facility in 2024, allowing clinicians to design and produce patient-specific anatomical models for surgical planning. This highlights how 3D CAD tools are being used to revolutionize industries beyond their traditional applications.

Opportunities on the Horizon

The market is ripe with opportunities, particularly for players who focus on innovation and geographic expansion. With the shift towards cloud computing, small and medium enterprises (SMEs) are increasingly adopting 3D CAD solutions that offer scalability and real-time collaboration. Emerging markets, especially in Asia-Pacific, provide fertile ground for growth due to rising industrialization and technological adoption.

Large enterprises, which accounted for a significant share of the market in 2023, will continue to benefit from 3D CAD software’s ability to streamline workflows and reduce material waste. These tools also help in managing increasingly complex product designs, which is essential for companies operating on a global scale.

The Road Ahead

As the 3D CAD software market continues to evolve, the demand for advanced design and engineering tools will only grow. Key players in the market are focusing on mergers, acquisitions, and collaborations to expand their portfolios and tap into new markets. With advancements in AI, cloud computing, and AR/VR, the future of 3D CAD software looks promising.

By addressing current challenges, such as high costs and complexity, and seizing opportunities in R&D and geographic expansion, the 3D CAD software industry is well-positioned for sustained growth and innovation in the coming decade.

As industries move towards more sophisticated design processes, the demand for cutting-edge 3D CAD software is set to soar. Whether it’s revolutionizing automotive design, enabling healthcare innovations, or driving efficiencies in construction, 3D CAD tools are at the forefront of modern industrial advancements. The future of the 3D CAD software market is bright, and it’s poised to shape the way we design and build in the years to come.

About The Author

Asmita Singh is a distinguished author and consultant in the packaging industry, recognized for her unwavering passion for knowledge discovery and her commitment to providing actionable insights. She holds an MBA from the University of Mumbai and a degree in Packaging Engineering from the Indian Institute of Packaging (IIP), equipping her with a solid foundation in both business and technical aspects of packaging. With extensive experience in packaging consulting, Asmita has successfully implemented advanced research methodologies across various packaging categories, including flexible packaging, rigid packaging, sustainable packaging, and smart packaging. She generates high-quality data and delivers meaningful results that drive innovation and efficiency. Her expertise spans the globe, offering valuable consulting services to businesses seeking to enhance their packaging strategies. Asmitas work is characterized by a dedication to excellence and a keen understanding of the latest trends and technologies shaping the future of packaging.

Insights Source: https://www.towardspackaging.com/insights/3d-cad-software-market-sizing

 

A mechanical device in mechanical engineering refers to any machine, tool, or apparatus designed to perform a specific function by utilizing mechanical principles such as motion, force, and energy conversion. These devices range from simple mechanisms like levers and pulleys to complex machinery such as engines, turbines, and robotic systems. The fundamental purpose of mechanical devices is to facilitate work, enhance efficiency, and improve precision in various engineering applications.

One of the most common types of mechanical devices is the gear system, which consists of interlocking toothed wheels that transmit motion and force between rotating shafts. Gears are widely used in automobiles, industrial machinery, and even in household appliances to control speed and torque. Another essential mechanical device is the hydraulic system, which operates based on Pascal’s Law, stating that pressure applied to a confined fluid is transmitted equally in all directions. Hydraulic systems power heavy machinery such as excavators, cranes, and aircraft landing gear by converting fluid pressure into mechanical work.

In the automotive industry, internal combustion engines (ICEs) serve as a critical mechanical device, converting chemical energy from fuel into mechanical energy through controlled explosions within cylinders. These engines power vehicles, generators, and industrial equipment, making them indispensable in modern transportation and energy sectors. However, with advancements in technology, electric motors are increasingly replacing ICEs in many applications due to their efficiency and environmental benefits.

Another significant category of mechanical devices includes robotic arms, which are programmable machines capable of performing precise and repetitive tasks in manufacturing, medical surgeries, and space exploration. These robotic systems utilize actuators, sensors, and controllers to mimic human hand movements, improving productivity and accuracy in industrial processes.

Mechanical devices also play a crucial role in thermal power plants, where steam turbines convert thermal energy from steam into mechanical energy to generate electricity. These turbines are widely used in power generation plants and rely on principles of thermodynamics to maximize efficiency. Similarly, refrigeration and air conditioning systems employ compressors and heat exchangers to regulate temperature by transferring heat from one space to another.

The development of additive manufacturing, commonly known as 3D printing, has introduced a new dimension to mechanical devices by enabling rapid prototyping and production of intricate components. This technology uses layer-by-layer material deposition to create complex parts, revolutionizing industries such as aerospace, medical implants, and custom manufacturing.

In conclusion, mechanical devices are the backbone of engineering, influencing nearly every aspect of modern life. From basic tools to advanced machinery, these devices enhance efficiency, safety, and functionality in industries ranging from transportation and energy to healthcare and automation. As technology advances, mechanical devices will continue to evolve, integrating smart systems, AI, and automation to further improve their performance and capabilities.

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Advancements in Rapid Prototyping Technologies

 

In the dynamic realm of innovation, the ability to rapidly prototype new ideas has become essential for engineers and entrepreneurs. Recent advancements in rapid prototyping technologies are not only redefining how products are developed but also revolutionizing the entire manufacturing process.

With these breakthroughs, the journey from concept to reality is faster and more flexible than ever.

The Evolution of Rapid Prototyping

Rapid prototyping has transformed from a specialized practice into a cornerstone of modern manufacturing. Long gone are the days of prolonged product development filled with uncertainty. Engineers can now create functional prototypes in record time, enabling quicker iterations and a more responsive design approach. This evolution empowers teams to explore bold ideas and bring novel products to market with impressive speed.

3D Printing: A Revolution in Creation

3D printing stands at the forefront of rapid prototyping, enabling the creation of intricate parts with remarkable precision.

Speed and Innovation

One of the standout features of 3D printing is its ability to convert digital designs into tangible objects almost overnight. This rapid turnaround permits teams to test and refine their ideas without the lengthy delays of traditional manufacturing. With this agility, businesses can swiftly adapt to changing market demands and consumer preferences.

READ MORE: How Design for Excellence (DfX) Intersects with Rapid Prototyping

Material Versatility

The scope of 3D printing has broadened significantly, allowing engineers to choose from a wide array of materials—from flexible thermoplastics to robust metals. This versatility helps create prototypes that meet specific functional and aesthetic requirements, supporting the development of products that resonate with users and stand out in a competitive marketplace.

Cost Efficiency

By minimizing material waste and removing the need for expensive tooling, 3D printing drastically lowers production costs. This financial advantage enables companies to innovate without excessive financial strain. As technology advances, the costs associated with 3D printing are likely to decrease further, opening new doors for creativity and exploration.

Laser Cutting: Precision Redefined

Laser cutting technology also represents a major advancement in rapid prototyping. While both technologies play key roles, they differ fundamentally in their approaches. 3D printing is an additive manufacturing process, building objects layer by layer from the ground up, which allows for complex internal structures and geometries. In contrast, laser cutting is a subtractive process, removing material from a larger sheet to create shapes. This method achieves exceptional accuracy and offers several benefits, among them:

Achieving intricate designs. Laser cutting enables engineers to produce highly detailed components that traditional manufacturing techniques struggle to replicate. This precision is essential for prototypes that need to mirror final products closely.

Supporting diverse materials. This technology accommodates a wide variety of materials, such as plastics, metals and wood. Its adaptability makes laser cutting invaluable across numerous industries, from automotive to consumer goods, where the quality of prototypes is critical.

Accelerating production timelines. The high speed of laser cutting machines facilitates the rapid production of parts, enhancing the overall efficiency of the prototyping process. This capability is especially advantageous for projects with tight deadlines, enabling teams to deliver results without compromising quality.

Exploring Other Notable Rapid Prototyping Methods

In addition to 3D printing and laser cutting, several other methods contribute significantly to the landscape of rapid prototyping.

CNC machining. Computer Numerical Control (CNC) machining enables the precise shaping of materials, yielding high-tolerance prototypes that meet rigorous industry standards. This method is especially valuable for creating metal parts, making it vital in the aerospace and automotive sectors where precision is paramount.

READ MORE: Discovering the Future of Manufacturing at the International Manufacturing Technology Show

Injection molding for prototyping. While traditionally associated with mass production, injection molding is being increasingly adapted for rapid prototyping. Short-run injection molding allows for swift production of small batches, providing a reliable means of testing designs before full-scale manufacturing begins. This approach is instrumental in identifying potential design flaws early, mitigating the risk of costly mistakes during mass production.

3D-Printing Flexible Devices without Mechanical Joints

 

The lab’s DNGE prototype ‘finger’ with rigid ‘bones’ surrounded by flexible ‘flesh.’ (Image: Adrian Alberola)

For engineers working on soft robotics or wearable devices, keeping things light is a constant challenge: heavier materials require more energy to move around, and — in the case of wearables or prostheses — cause discomfort. Elastomers are synthetic polymers that can be manufactured with a range of mechanical properties, from stiff to stretchy, making them a popular material for such applications. But manufacturing elastomers that can be shaped into complex 3D structures that go from rigid to rubbery has been unfeasible until now.

“Elastomers are usually cast so that their composition cannot be changed in all three dimensions over short length scales. To overcome this problem, we developed DNGEs: 3D-printable double network granular elastomers that can vary their mechanical properties to an unprecedented degree,” said Esther Amstad, Head of the Soft Materials Laboratory in EPFL’s School of Engineering.

Eva Baur, a Ph.D. student in Amstad’s lab, used DNGEs to print a prototype ‘finger,’ complete with rigid ‘bones’ surrounded by flexible ‘flesh.’ The finger was printed to deform in a pre-defined way, demonstrating the technology’s potential to manufacture devices that are sufficiently supple to bend and stretch, while remaining firm enough to manipulate objects.

An example of DNGEs (3D-printable double network granular elastomers). (Image: Titouan Veuillet)

With these advantages, the researchers believe that DNGEs could facilitate the design of soft actuators, sensors, and wearables free of heavy, bulky mechanical joints. The research has been published in the journal Advanced Materials.

The key to the DNGEs’ versatility lies in engineering two elastomeric networks. First, elastomer microparticles are produced from oil-in-water emulsion drops. These microparticles are placed in a precursor solution, where they absorb elastomer compounds and swell up. The swollen microparticles are used to make a 3D printable ink, which is loaded into a bioprinter to create a desired structure. The precursor is polymerized within the 3D-printed structure, creating a second elastomeric network that rigidifies the entire object.

While the composition of the first network determines the structure’s stiffness, the second determines its fracture toughness, meaning that the two networks can be fine-tuned independently to achieve a combination of stiffness, toughness, and fatigue resistance. The use of elastomers over hydrogels — the material used in state-of-the-art approaches — has the added advantage of creating structures that are water-free, making them more stable over time. To top it off, DNGEs can be printed using commercially available 3D printers.

“The beauty of our approach is that anyone with a standard bioprinter can use it,” Amstad emphasized.

One exciting potential application of DNGEs is in devices for motion-guided rehabilitation, where the ability to support movement in one direction while restricting it in another could be highly useful. Further development of DNGE technology could result in prosthetics, or even motion guides to assist surgeons. Sensing remote movements, for example in robot-assisted crop harvesting or underwater exploration, is another area of application.

Amstad said that the Soft Materials Lab is already working on the next steps toward developing such applications by integrating active elements, such as responsive materials and electrical connections, into DNGE structures.

For more information, contact Celia Luterbacher at celia. luterbacher@epfl.ch; +41-216-938-759.

Manufacturing Processes with AI-Powered Visual Inspection Configurable without programming skills, the MELSOFT VIXIO AI-powered visual inspection software is revolutionising quality assurance across industries.

 

Imagine a bustling factory floor where every product that rolls off the line is a testament to meticulous craftsmanship. Yet, no matter how skilled, the human eye can tire, and defects can slip through unnoticed in those moments of fatigue. The new Mitsubishi Electric MELSOFT VIXIO software helps avoiding this. Tirelessly vigilant it ensures that every product can meet the highest quality standards.

“Our visual inspection software transforms the inspection process, making it more accurate and efficient. It performs the heavy lifting of primary screenings, identifying potential defects with unmatched precision. Doing so liberates human inspectors to focus on what truly matters—ensuring that only the finest products reach the hands of consumers” - emphasises Daniel Sperlich, Strategic Product Manager Controllers at Mitsubishi Electric, Factory Automation EMEA.

How does it specifically work?

• Learning phase: The software learns what a "good" product looks like by reviewing many examples of good and bad items, similar to studying for a test. 
• Image processing: After learning, it analyses product images in real-time using algorithms to compare them against its training data.
• Decision making: Based on its analysis, the software gives products a "thumbs up" if they meet standards or a "thumbs down" if not, ensuring only quality products are sent out. 
• Feedback loop: The software improves over time; if it makes mistakes, engineers can update its learning data to enhance its accuracy in recognising products.

MELSOFT VIXIO is designed for ease of use and requires no specialised programming knowledge. Its intuitive interface allows users to set up three simple processes: making the picture dataset, creating the AI model by configuration, and generating the task via low-code. It demands only building with the help of low code instead of programming, making it accessible to everyone—from seasoned engineers to newcomers in the field.

As the tool debuts across the EMEA region, it targets industries ranging from automotive to food & beverage to life sciences and promises to enhance productivity and reduce waste and energy consumption.

Emerging 2D Materials for Future Electronics

 Emerging and future 2D electronic materials such as graphene have the potential to exceed the capabilities of modern components in terms of carrier capacity, strength, and versatility. This article will discuss some of the potential advantages of two-dimensional electronics and the materials from which they will be constructed.

Image Credit: Golubovy/Shutterstock.com

Silicon has been the primary material used in the construction of transistors, semiconductors, and other electronic components since the 1950s, selected over competitors owing to favorable material and electronic properties and low cost. Since this time, Moore’s law, the observation that the number of transistors on an integrated circuit doubles every two (roughly) years, has vaguely held true, and silicon-based electronics have become increasingly powerful. However, since around 2010, the rate of progress has observably slowed, mainly owing to transistors reaching an almost atomic density that suffers from quantum effects such as electron transfer (tunneling) to neighboring components. 

Why are 2D Electronic Materials Needed?

Silicon-based transistors have reached a scale in the order of nanometers, with numerous innovations having allowed them to reach this scale thus far; copper interconnects, the incorporation of dielectric materials, complementary metal oxide semiconductor field effect transistors (CMOS), and so on. Nanometer-thick silicon sheets provide individual charge carrier channels, though making them thinner significantly limits carrier mobility within the channel when approaching around 3 nm.

2D semiconductors of atomic thickness below 1 nm thick are innately thinner than possible for silicon sheets with superior carrier mobility; they are self-passivated in the third dimension and thus do not require any additional shielding in this direction and can be fine-tuned using layering strategies. Layered 2D materials with differing properties can be combined and connected via various gating methods to produce novel electronic heterostructures with precise electronic functions.

What are the Applications of 2D Electronics?

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2D electronic materials are highly touted in sensing applications, mainly owing to their large and highly customizable surface chemistry. Any particle or molecule capable of adsorbing or chemically absorbing to the surface of a 2D electronic material may induce a change in electronic properties, namely impedance and, thus, current. The surface can be functionalized with complimentary molecules to one of interest, such as an antibody specific to a pathogenic antigen, and thus act as a highly sensitive and selective detector in a variety of mediums, both gas and liquid phase.

Two-dimensional electronic materials may be the solution to neuromorphic computing in the future; circuitry inspired by the architecture of brains. Within these devices, synapses and neurons are mimicked using computing-in-memory and memristive devices, the latter of which relates electric charge to magnetic flux linkage. These devices are rarely used in modern electronics and remain under intense development, but they have powerful potential applications as memory devices in quantum computing, physical neural networks, and reconfigurable computing.

Reconfigurable computing is a computer architecture that allows substantial changes to the datapath and control of flow through the circuit, allowing them to be configured for a specific task and then reconfigured for another, unlike ordinary microprocessors. Layered 2D heterostructures are ideally suited to reconfigurable computing, as they have the potential to be broken down layer-by-layer and the gating between layers adjusted. Complex overlapping circuitry is possible using 2D electronic materials owing to the aforementioned shielding in the third dimension, allowing the space to be utilized optimally.

What 2D Materials Will Be Used in Future Electronics?

Graphene may be amongst the most popular two-dimensional materials with potentially exciting applications in future electronics; it is constructed only from carbon atoms arranged in a hexagonal lattice that shares an extensive conjugated electron system. This is a common feature of 2D electronic materials, such as hexagonal boron nitride, which is structured similarly to graphene but contains alternating boron and nitrogen atoms.

This material is typically used in lubrication and coating applications where high temperature and chemical resistivity is desired, and unlike graphene, it acts as an insulator, though it can be used in short sections within 2D electronic circuits to act as a tunneling barrier.

Another 2D material with potential applications in future electronics is tungsten diselenide, which, rather than forming a one-atom thick planar structure, has a repeating monomeric unit containing two selenium atoms connected above and below one tungsten atom. This material is employed in solar cell applications, as it has a high bandgap and relatively low-efficiency loss with increasing temperature, and is used in particular gating components of 2D electronics, such as in reconfigurable computing.

Another inorganic 2D electronic material is black phosphorous, which exhibits a unique electronic structure, allowing for high charge carrier mobility. Of all the forms of phosphorous, black phosphorous is most thermodynamically stable at room temperature and again possesses a hexagonal lattice structure that allows overlapping p-type orbitals between atoms and contributes to high electrical conductivity.

Black phosphorous is of particular interest owing to its tunable bandgap by adjusting layer thickness, which fills the range between the aforementioned large bandgap of tungsten diselenide and other transition metal dichalcogenide monolayers and the zero band gap graphene.

Emerging System-on-a-Chip Trends to Watch Out For

References and Further Reading 

Lemme, M. C., Akinwande, D., Huyghebaert, C., & Stampfer, C. (2022). 2D materials for future heterogeneous electronics. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-29001-4

Fei, W., Trommer, J., Lemme, M. C., Mikolajick, T., & Heinzig, A. (2022). Emerging reconfigurable electronic devices based on two‐dimensional materials: A review. Infomat, 4(10). https://doi.org/10.1002/inf2.12355

Cheng, J., Gao, L., Li, T., Mei, S., Wang, C., Wen, B., Huang, W., Li, C., Zheng, G., Wang, H., & Zhang, H. (2020). Two-Dimensional Black Phosphorus Nanomaterials: Emerging Advances in Electrochemical Energy Storage Science. Nano-micro Letters, 12(1). https://doi.org/10.1007/s40820-020-00510-