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Automotive

V2X Technology: Revolutionizing Transportation and Our Future

V2X Technology

Technology keeps pushing the limits of innovation in the quickly changing field of transportation. Vehicle-to-Everything (V2X) communication technology is one such ground-breaking development that is transforming how vehicles interact with their environment. V2X refers to a group of communication technologies that allow vehicles to interact with networks, infrastructure (RSU), pedestrians, and other vehicles (V2V, V2P, and V2N).

V2V communication

V2V communication, which involves direct communication between vehicles, is part of V2X technology. Vehicles can increase traffic efficiency, increase road safety, and enable cooperative driving by communicating real-time information. Vehicles can exchange information about their position, velocity, acceleration, and trajectory through V2V communication. Advanced safety features including collision warnings, emergency braking assistance, and cooperative adaptive cruise control are made possible by this information sharing.

V2I communication

Establishing a connection between cars and the surrounding infrastructure, such as traffic lights, roadside sensors, RSU, and road signage, is the main goal of V2I communication. Vehicles can get updates on the state of the roads, traffic light timings, and real-time traffic data through a V2I connection. Informed judgments may be made, routes can be optimized, and driving behavior can be modified as a result. Traffic management systems may also monitor and regulate traffic flow, improve signal timings, and give precedence to emergency vehicles. Additionally, V2I integration is essential for the development of smart cities and intelligent transportation systems.

V2P communication

By enabling vehicles to identify and interact with road users including bicycles and pedestrians, V2P communication seeks to improve pedestrian safety. This variation of V2X technology makes use of sensors, such as cameras and radars, to find pedestrians who are close to the car. Once the pedestrian has been identified, the car can share data with them, giving both of them alerts or cautions. For instance, when a car is near a crossing, it can send out a signal to pedestrians to let them know it is going to stop after spotting them. Increased awareness, fewer accidents involving pedestrians, and safer cohabitation between automobiles and road users are all benefits of V2P communication.

V2N communication

Data is exchanged between cars and external networks, including cloud-based applications, traffic management hubs, and mobility service providers, using V2N communication. Vehicles may get real-time information regarding traffic patterns, weather forecasts, and parking spots thanks to V2N networking. This knowledge provides drivers with useful insights for effective route planning, traffic avoidance, and parking spot location. Furthermore, the V2N connection makes it possible for automakers to remotely install performance upgrades, bug repairs, and new features, assuring the best possible vehicle performance and safety.

V2X technology has multiple benefits, and has a potential impact on our future:

“Traffic Efficiency and Management”

V2X technology is essential for improving traffic management and efficiency. V2X systems help intelligent traffic management systems make wise decisions by gathering real-time data on traffic flow, congestion, and road conditions. Based on the actual traffic demand, traffic lights may be dynamically changed to shorten wait times and improve traffic flow.

“Enabling Autonomous Driving”

Self-driving cars with V2X capabilities may communicate with other vehicles and infrastructure to share information, which enables them to safely and effectively manage challenging traffic situations. Autonomous cars can make educated judgments and respond quickly by receiving real-time data through V2X communication on the state of the roads, traffic patterns, and possible dangers. This innovation speeds up the incorporation of autonomous cars into our transportation infrastructure by improving their dependability and safety.

“Enhancing Road Safety”

Enhancing road safety is one of V2X technology’s main objectives. V2X systems provide cars the ability to interact with one another and their surroundings, allowing them to share useful information that can lower risks and avert accidents. V2V communication, for instance, might warn drivers of impending crashes, abrupt braking, or perilous road conditions. By informing drivers of construction zones, traffic signal timings, and traffic congestion, V2I communication can improve traffic flow and lessen congestion. Additionally, V2P communication makes it possible for cars to recognize and react to vulnerable road users including walkers, cyclists, and others, improving their safety.

“Reduced Fuel Consumption and Emissions”

V2X technology helps optimize fuel economy and lower emissions, especially when paired with autonomous driving features. Vehicles equipped with V2X systems can exchange data on traffic conditions, road gradients, and upcoming traffic signals. This information enables the vehicles to adjust their speed and acceleration patterns efficiently, minimizing unnecessary fuel consumption and emissions.

Some potential disadvantages and challenges associated with V2x

Some potential disadvantages and challenges associated with V2x:

“Infrastructure Deployment”

The installation of communication infrastructure, such as roadside devices, traffic sensors, and network connectivity, is necessary for the implementation of V2X technology. Particularly when it comes to comprehensive coverage throughout a whole area or nation, this may be a pricey and time-consuming operation. Particularly in rural or resource-constrained places, the initial investment and infrastructure maintenance expenses may be problematic.

“Interoperability and Standardization”

V2X technology depends on the creation of standard communication protocols and guidelines to guarantee compatibility between various cars and infrastructure parts. However, because different regulatory frameworks, competing corporate interests, and various regional agendas exist, establishing global standardization can be challenging. The successful use of V2X systems may be constrained by a lack of compatibility, which might impede the efficient flow of information.

“Security risks” The technology involves the transmission of sensitive data, such as location and speed information, between vehicles and infrastructure. This data is vulnerable to cyberattacks, which could compromise the safety and privacy of drivers and passengers. Hackers could potentially gain access to the V2X system and use it to cause accidents or steal personal data. In order to address these security risks, V2X systems will need to be built with robust cybersecurity measures in place. This will require a significant investment in security technologies and protocols, as well as ongoing monitoring and updates to ensure that the system remains secure over time. Additionally, stakeholders will need to develop clear policies and regulations around data privacy and security to ensure that personal data is protected and used only for its intended purposes.

Categories
Semiconductors

Different Semiconductor Packaging Technology

Semiconductor packaging

Semiconductor packaging is a crucial component of modern electronics fabrication since it protects and connects integrated circuits to the outside world. IC packaging has developed to satisfy the demands of many applications as electronic devices have grown smaller, quicker, and more adaptable. This article discusses the various kinds of semiconductor packaging, and their significance, used for manufacturing in the semiconductor industry.

Types of Semiconductor Packaging

Semiconductor packaging entails enclosing the semiconductor die within a protective package may be made of ceramic or plastic and connecting the device electrically. The packaging type chosen is determined by criteria such as form factor, thermal concerns, electrical performance, and cost. Here are some examples of several different types of IC packaging:

different types of IC packaging
  • Dual In-line Package (DIP): DIP is one of the oldest semiconductor packaging types, and it remains popular in the industry. It is inserted through the hole-type package in the circuit board. Its pin counts range from 8 to 64.
  • Quad Flat Package (QFP): QFP is a surface-mount package that is rectangular in shape and has leads protruding from all four sides. QFP packages are offered in pin counts ranging from 32 to 304.
  • Small Outline J-lead Package (SOJ): SOJ is a surface-mount package that features J-shaped leads on both sides. SOJ packages are available with pin counts ranging from 8 to 44.
  • Pin Grid Array (PGA): PGA is a through-hole package with pins positioned on the bottom of the package in a grid arrangement. PGA packages come in a variety of pin counts, ranging from 84 to 1,520.
  • Ball Grid Array (BGA): BGA is surface-mount type packaging in which pins in the earlier packaging are replaced with a variety of solder balls. BGA packets come in a variety of ball counts, ranging from 4 to 2,500.
  • Wafer-Level Packaging (WLP): WLP is a packaging method that involves encapsulating the ICs at the wafer level before being divided into individual chips. WLP packages come in a variety of sizes, ranging from 1 mm to 10 mm. Examples include RDL-based, flip-chip, and TSV-based packages. There are main two types of Wafer-level Chip-Scale Packages (WLCSP) that are Fan-In WLCSP and Fan-out WLCSP. Wherein the word “Fan” refers to the chip’s size.
  • Fan-In WLCSP: The Fan-In WLCSP includes the insulating layer, solder balls right on top of the wafer, and package wiring all contributing to the different benefits of a fan-in WLCSP. It has an interposer that is the same size as the die. The electrical transmission channel is relatively short since the solder balls are connected to the chip directly rather than through a medium like a substrate, which enhances electrical properties.
  • Fan-out WLCSP: Fan-out WLCSP overcomes the drawbacks of fan-in WLCSP while retaining its benefits. Fan-out WLCSP has package-mounted solder balls that can be “fanned out” away from the chip. The interposer’s size is the same as that of the die. It also offers good electrical characteristics like FI-WLCSP.
Importance of IC packaging

Importance of IC packaging:

  1. Electrical Performance: The packaging has an impact on the device’s electrical properties, and well-designed packaging reduces signal deterioration and improves overall performance.
  2. Thermal Control: Modern ICs produce a lot of heat. Heat dissipation is facilitated by efficient packaging designs, which helps avoid overheating and ensures long-term dependability.
  3. Form Factor & Size: As electronics get smaller and more portable, miniature packaging techniques make it possible to make devices that are svelte and tiny.
  4. Protection: Different packaging types protect the IC from various conditions including moisture, dust, and temperature changes. The packaging acts as a shield to protect the semiconductor die from these factors.
  5. Interconnect Density: The number of interconnects that may be accommodated depends on the kind of packaging. Complex interconnections are made possible by high-density packing, such as BGAs and CSPs, which is essential for contemporary electronics.

Techinsights images of Wafer level chip scale-type packaging

Samsung Exynos 9110 Processor

Figure 1 Samsung Exynos 9110 Processor (Source)

Apple A14 Bionic Processor (APL1W01)
Apple A14 Bionic Processor (APL1W01)

Figure 2 Apple A14 Bionic Processor (APL1W01) (Source)

IP Trends in Wafer-Level Chip Scale Packaging in Semiconductor

As the importance of packaging is increasing, there is a rapid growth in the patent filing trends witnessed across the globe. The Highest number of patents granted was in 2020 with 3339 patents and the highest number of patent applications filed was ~4553 in 2022.

IBM is a dominant player in the market with ~9319 patent families. So far, it has 1.5 times more patent families than TSMC, which comes second with 5686 patent families. Samsung is the third-largest patent holder in the domain.

Other key players who have filed for patents in WLCSP technology are Intel, Micron, Globalfoundries, SanDisk, Infineon Technology, SK Hynix and so more.

Key players who have filed for patents in WLCSP technology

(Source: Lens.org)

Legal status of patent applications
Patent Documents over Time with Publiation Date

(Source: Lens.org)

Conclusion

IC packaging is a crucial step in the semiconductor business because it shields semiconductor components from corrosion and physical harm from the outside world. The many distinct varieties of IC packages are based on various circuit designs and requirements for the outside shell. The most popular IC packaging designs are wafer-level packaging, pin grid arrays, dual in-line packages, quad flat packages, compact outline J-lead packages, and chip carriers. The IC package you choose depends on the particular application requirements.

Categories
Computer Science

Artificial Intelligence-Based Software Engineering Metrics Defect Identification

Nowadays we see the use of Artificial Intelligence (AI) in every field of study, even in image processing, data analytics, text processing, robotics, industries, software technologies, etc. Day by day increasing the use of AI helps users to perform automated tasks without involving the human physically present. The work can be completed automatically within the specified time as per the requirement. Even in the field of Software Engineering, the use of AI is growing day by day to perform automated tasks without causing errors. AI in software engineering provides automated non-error calculations, in the field of software engineering it provides the automated testing of software, automatic debugging of software, etc. to provide high quality (quality assurance), and efficiency of software applications within the budget.

Software Engineering is the method of developing, testing, and deploying computer software to solve problems and issues in the real world by utilizing software principles and best practices. It provides an organized and professional approach from developing the software to the deployment of the software. In the field of software engineering software metrics play a very important role. The software metrics are used to evaluate the reusability, quality, portability, functionality, and understandability. The AI-based software metrics are error-free and automated, they will used to identify or predict the defects in the software and efficient solutions in the real world scenario without involving humans.

Background

Many researchers describe different software metrics and provide efficient solutions for the software metrics. Some research also describes automated solutions using deep learning techniques to improve the software metrics. Without using Artificial intelligence, some chances degrade the quality of the final software products. It may also include a lack of functionality and require more human interactions due to which it increases the cost of the software. Some researchers describe deep learning-based techniques to solve software metrics problems and provide efficient results. However, the can be a lack in using the dataset and training of the data. Using the correct dataset may cause erroneous training of data and provide the wrong results. The wrong results may cause issues and degrade the quality of the software metrics. Therefore, this blog provides the proposed solution that aims to solve the problem of software metrics using Artificial Intelligence.

Basic Concept

According to the authors, there are several studies considered and provided many definitions. Therefore, before discussing the proposed approach let us discuss some basics about the software metrics as discussed.

As discussed above the software metrics are used to evaluate the reusability, quality, portability, functionality, and understandability of the software to achieve high-quality software. The software, metrics are of two types: the system-level metrics and component-level metrics as described in Figure 1.

Software Metrics Categories

Figure 1: Software Metrics Categories

  1. System Level Metrics: The system level metrics are further divided into three types that is Complexity, Metrics Suite, and System Complexity.
  2. Component level Metrics: The component level metrics are further divided into four types that are Customization, component complexity, reusability, and coupling and cohesion metrics.
  • Complexity Metrics: The complexity metrics are the type of system-level metrics. There are many definitions given by many authors. According to IEEE, complexity is defined as the quality where the component or any system design and implementation is complex to understand and authenticate.
  • Metric Suite: It provides the requirements and functionality of the software that is needed by the users. It ensures to provide users a high quality, satisfactory fault-free software products.
  • System Complexity Metric: It is defined as the component metric having a set of components in the system.
  • Customization: The customization metric identifies whether the component can be customized according to the user’s needs or not.
  • Component Complexity Metrics: The Component Complexity Metrics have component plain complexity (CPC), Component Static Complexity (CSC), and Component Dynamic Complexity (CDC).
  • Component Reusability Metric: This type of metric is the ratio of the sum of interface methods that provides common reuse component features used in a feature.
  • Coupling & Cohesion metrics: It is the degree or power with which software components are related to each other.

Proposed Approach

The proposed approach describes the AI-based method to identify as well as predict the defects in the software metrics. In this proposed approach, first, the real-time software metrics dataset is collected from multiple sources. The software metrics dataset may include the data regarding the identified software metrics with labels. The Figure 2 describes the architecture of the proposed approach and systematic details process of this approach.

Figure 2: Proposed Approach

  1. Obtaining data: This step is very essential and very important step of the approach. Correct data means high performance with minimum defects and results in high-quality software. Therefore, in this step, the labeled dataset of the software metrics will be collected. The dataset will include above 50K software modules and the number of defects predicted in the software module. The predicted defects can be binary values in the form of zero and one.
  2. Data Pre-Processing: After data, collection data pre-processing is an essential step of this proposed approach. After data collection, the data must be cleaned and normalized properly to make the analysis simpler and more efficient. In this step, the data is cleaned by removing empty rows, duplicate values, indexing of data, etc.
  3. Artificial Intelligence Model: After data, cleaning the Artificial Intelligence Model will be applied that analyzes the data such as determining the combinations, and automating the detection procedure without human interaction. AI model can be any algorithm applied such as Linear Regression, Logistic Regression, Naïve Bayes, Support Vector Machine, etc. These algorithms will analyze the data and determine the combinations. In this step, the data can also be converted into some kind of vectors also known as numbers based on the software metrics given in the dataset. The AI model is also known as the machine-learning model.
  4. Training and Testing: After the AI model is applied, the data will be split into train sets and test sets that are different from each other. 75% data is given for the train set and the remaining 25% of the data is given for the test set. Then the model is trained on the train set that will train the model based on the combinations and the test set will be used for our model that does not contain any labels, the machine will identify the faults automatically based on the training of the model.
  5. Results: After applying the AI or ML model on the test set the results are obtained on the test set that will determine how best the model works. The results will be determined in terms of accuracy, the area under the curve, f1-score, confusion matrix, etc. The expected accuracy will achieve 98% by applying logistic regression on the test set.

This blog is inspired and in contributed with Dr. Kiran Narang, Department of Computer Science Engineering, SRM University

REFERENCES

[1]. https://www.techtarget.com/whatis/definition/software-engineering
[2]. https://ieeexplore.ieee.org/document/8443016
[3]. https://en.wikipedia.org/wiki/Software_metric
[4]. https://www.sciencedirect.com/science/article/abs/pii/S0925231219316698
[5]. https://www.mdpi.com/2227-7390/10/17/3120
[6]. https://www.sciencedirect.com/science/article/pii/S0164121222002138
[7]. https://viso.ai/deep-learning/ml-ai-models/