Rapid advancements in electronics technology have directly translated into a growing emphasis on semiconductor manufacturing worldwide. This emphasis is being replicated in India with support from the very top levels, with developmental efforts being poured into both the government and private sectors. However, with more emphasis on semiconductors, chip development and manufacturing processes integrate more data than ever before. This has led to visible challenges in integrating the raw data into actionable insights, and addressing this critical challenge has become of paramount importance.
For a detailed context, it is important to understand that semiconductors have widespread applications and defects are generally measured with parts per billion. But as these chips are used across an extended range of functionalities, mitigating the defects is of utmost importance. For instance, chips used in cars are used to control a wide range of controls, including critical systems, malfunctioning of which could lead to serious ramifications like accidents. This is why a zero-defect approach is being adopted by industry stakeholders, and data analytics is increasingly becoming important to sustain the overall development in the sector.
Data Analytics: Enhancing Efficacy
Semiconductors generate a considerable amount of data in Petabytes. The sheer volume of data makes it increasingly difficult for the majority of traditional analytical tools or analysts out there to analyse. This is where modern Data Analytics come into play, offering its huge capacity and intelligence to reap benefits in terms of engineering productivity, enhancing silicon efficiency and more often than not, scalability. With data analytics as a supplementary tool, engineers are empowered to make informed decisions regarding improvement metrics of production process and capacity, while also garnering critical functional metrics in relation to power and performance. Furthermore, this technology helps engineers to promptly identify and work on minuscule issues, providing them with intelligent and automated SWOT analysis of designs, manufacturing processes, diagnostic efforts and test data.
Additionally, data analytics opens up a new avenue for consolidating analytical capacity from all manufacturing aspects, helping to reduce man-hours, tool usage, and enhancing diagnostic traceability and debugging processes. By gathering this silicon data, the accuracy of pre-silicon designs is lifted significantly, whereas post-silicon designs are also impacted in terms of enhancing performance.
While the majority of the conversation regarding the presence of data analytics tends to focus on the chip manufacturing process itself, acquiring significant amounts of testing data is another aspect of it. Analysing the testing data helps to single out faults and challenges in future production processes and designs, leading to better quality chips. As a result, aspects like reliability, longevity and performance are not only addressed, but a strategic acceleration of quality and yield is achieved.
Turning Manufacturing Data into actionable insights
The semiconductor development process is separated into several parts, and from designing to manufacturing, generates a significant amount of data that is often lost in the process. The volume of data remains the primary hurdle to analysing it, an aspect that can be addressed through data analysis.
For instance, if particular challenges or issues are not identified, how would one determine the development aspects of the existing technology of semiconductors? Data analysis provides a seamless way of improving the overall quality, yield and general output of chip manufacturing by automatically analysing the huge amount of data and turning it into actionable insights. This is of particular interest regarding enhancing the power and performance of chips, while also receiving significant data regarding the chips’ lifespan.
By turning data into actionable insights, data analytics also helps to enhance the general efficiency and productivity of the fabrication process, while also directly impacting the high-volume tests, engineering productivity, silicon efficiency and tool scalability. This leads to accelerated fabrication schedules, high-quality manufacturing and in general, premium quality chips.
Unification of the Manufacturing process
Semiconductors are quickly becoming one of the fundamental technologies behind the technological push of the global society. This emphasis is replicated not only in the developed world but also in India, where semiconductor manufacturing has picked up pace in the last few years. However, to effectively incentivise the entire process and support engineers with valuable insights into development and manufacturing, data analytics is becoming more relevant than ever. With this technology, the quality, yield and output can be essentially enhanced, leading to a more streamlined and methodical approach to the overall development process.
It’s also imperative to use data analytics to target diverse datasets generated from different life cycles of chips, irrespective of usage. Data analytics helps to comprehensively transform this aspect with a solution that is integrated and offers end-to-end services, directly translating into a competitive edge.
