The complexity of the silicon manufacturing industry has onset an explosion of data.
Today, the semiconductor industry is in a pivotal competition, being aware of the fact that chips are set to drive the forefront of future growth and innovation. Countries including South Korea, Germany, and the United States have presented plans for massive new semiconductor facilities to be started. Overall, reports state that there would be investments of almost $1 trillion between the years 2023 and 2030.
India has also been witnessing massive investments flowing in from countries looking to set up semiconductor units, which has led to the creation of thousands of job opportunities. The government has recently approved investments worth $15 billion in the sector. However, plans to set up manufacturing units are just one of the components of the semiconductor sector's boom. The actual problem, in fact, lies in identifying the talent to facilitate this boom.
In the United States, estimates suggest that enterprises across industries are likely to face a shortfall of 300,000 engineers and 90,000 skilled technicians by the year 2030. In order to operate at full steam, they need to ensure that they attract qualified and skilled talent before the massive projects start operations.
Read more: The New Data Economy: Navigating the Future of Data-Driven Value Creation
Traditionally, engineering teams used to have access to data that was significant to their step in the chip development process. However, it has been more challenging to obtain data from other phases of the lifecycle. The raw data is often difficult to distill into useful insights as engineers need to be aware of what to look for and what to query to make sense of the data. It is also critical to tap into the collected data during the early design and manufacturing process. Both the depth and breadth of data support are critical to isolate and solve the root cause of any problems.
With semiconductor content rising across a number of applications, there is a growing urgency to transition toward zero-defect approaches. The reality is that semiconductor defects can now be commonly measured in parts per billion (ppb) rather than parts per million (ppm). Even a miniscule defect rate can prove costly and harmful and, therefore, must be avoided. Today, it is imperative to accelerate the convergence of quality concerns leveraging data analytics.
Different Types of Big Data Analytics in Semiconductor on the Market
- Data Visualization & Dashboard
- Self-service Tools
- Data Mining & Data Warehousing
- Reporting
These tools help gain a comprehensive understanding of the diverse landscape within the big data analytics in the Semiconductor landscape & Electronics market. The categorizations can further change as technology advances and market trends evolve.
Read more: Real-Time Data Analytics is Empowering Business Decision Making - From Data to Action
Power Source for Big Data Analytics in Semiconductor Market's Growth
- Customer Analytics
- Marketing Analytics
- Supply Chain Analytics
- Workforce Analytics
- Pricing Analytics
These applications assist organizations in highlighting the versatility of Big Data Analytics in the Semiconductor & Electronics sector as well as their potential to enhance visual experiences across different settings and industries.
New Paradigm to Turn Semiconductor Manufacturing Data into Actionable Insights
From design to manufacturing, the semiconductor development process produces loads of data. However, considering the volume involved or sometimes gaps in expertise, most of the data tends to remain unearthed. The industry has been finding ways to easily identify ways to improve semiconductor quality, yield, and throughput while filtering significant insights from this massive amount of data automatically. They have also been exploring ways to enhance their chip’s power and performance based on silicon production and monitor data throughout the different stages.
Today, there is a new silicon lifecycle management solution that equips enterprises to process and analyze orders of magnitude more data than ever possible, spanning from design to fabrication to diagnostics through high-volume tests.
Read more: What is Data Processing - Methods, Techniques, and Steps
The Synopsys Silicon.da solution helps enhance engineering productivity, silicon efficiency, and tool scalability. This new end-to-end unified analytics platform can filter and analyze data product manufacturing schedules while also offering higher quality and better-performing products.
Key Benefit Areas from Data Analytics Environment
A significant part of Synopsys Silicon Lifecycle Management or the SLM Family, Silicon.da analyzes heaps of silicon data. With its capacity and intelligence, the solution presents the following key core benefits with a focus on engineering productivity, efficiency, and scalability.
- It enables engineers to determine ways to enhance key chip production metrics and key operational metrics.
- Automatically highlights silicon data outliers through 40+ automated insights, enabling engineering teams to quickly identify and correct underlying issues in the supply chain.
- Offer automated root-cause analysis through push-button correlation across design, manufacturing, diagnostics, and test data.
- Consolidates analytics across manufacturing processes within a sharable environment, thereby preventing time-consuming usage of multiple tools.
Tapping into actual chip monitor data collected from process, voltage, and temperature or PVT monitors facilitates high-value power and performance optimization flow. The silicon data gathered can further help enhance the accuracy of the models during the design stage, enabling the reduction of unnecessary guard bands while maintaining the required performance. Silicon.da further helps in supporting the advanced multi-die systems that are becoming prevalent for compute-intensive designs like AI and high-performance computing. The solution offers the flexibility to process and store data in the cloud.
Read more: Synthetic Data Generation - Meaning, Benefits, Methods & Use Cases
The Power of Unification in Semiconductor Manufacturing
Intelligence and insights generated from data assist in further enhancing the semiconductor chips. At the in-design phase, they can integrate big data analytics to improve power and performance with the aim of reducing yield ramp bring-up time. And in production, enterprises can work to enhance chip quality and yield.
However, while every phase of the lifecycle has its unique set of challenges and solutions, having a master solution that presents a holistic approach to unify the lifecycle phases will offer enterprises the fastest route to solve any problem that can originate in the earlier lifecycle phase. Applying analytics intelligence from design through manufacturing will further help follow an integrated end-to-end approach.
In summary, with this platform, engineering teams can turn this explosion of data in design and manufacturing into a competitive advantage.
A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance.
About SG Analytics
SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.
Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
