Thank you for watching. The only way out is 100% alternate source of energy. There are already several optimization done and will keep happening at the chip to data center level. Still, source of energy needs to be 100% environment friendly.

Thank you for watching. In terms of techniques: Chiplet, System-On-A-Wafer (TSMC calls it CoWo-S/R/L), Panel Level Packaging are few that are going to be game changer.

Thank you for watching.

Thank you for the comment. Agree and to gain more insights into the semiconductor manufacturing, foundational courses are very crucial.

Thank you for asking this question. Semiconductor manufacturers are already doing so by enhancing the possibilities of a single node by leveraging different types of voltage-current profile it offers. That means, developing, validating and utilizing new types of devices that slightly different either in structural or physical profiles. Many manufacturers also combine this by providing different types of memory or even performance features, that enables customers to pick and select which flavor to use, as long as design rule comply.

Indeed. There is another argument that a new FAB does not create high-number of jobs, but I believe there needs to be a more forward looking view to capture impact from both direct and in-direct along with numerous RnD activities a new FAB can start.

Thank you for asking the question. Part of seminometrics has already been incorporated, but is very distributed. Like management schools having different business courses and specializations around econometrics. I foresee that apart from the technical semiconductor education, there will be more fusion courses that will connect econometrics directly to semiconductors, not as a standalone econometrics only coursework. This will allow students to gain insights about different ways the cost and production BOM is built and factors affecting it, and also the business around it. In summary, as an elective to start with, then certification (as a major), and then a full-fledge program offered by multiple departments.

Thank you for asking this question. Few suggestions below (from new FAB/OSAT point of view): -- JV with a known FAB/OSAT as a technology partner, in order to bring business and technical knowledge. Thus, speeding up initial ramp -- Mitigating cost by utilizing new and old equipment for new FAB/OSAT -- Investing in Cost Optimization/Reduction team that can enable efficient flow from Day 1 -- On environment impacts: This is more about focusing on practice like net-zero, utilizing renewable sources (mainly for electricity demand/use), making use of sustainable materials for building infrastructures and so on. In the end, a lot also depends on the city/state authorities who may already have guidelines on deploying such big infrastructures. Thank you.

Thank you for watching the video.

Thank you. Agree. More so when EV, Everything Wireless, and Beyond 5G like technologies are getting better traction.

Thank you for asking this question. It is going to take enable new technological tools that reduces time to market, while also reducing errors and further proofing the defect-free goals. And, coopetition in AI + Semiconductor is already at an advanced stage. Examples: 1) NVIDIA, ASML, TSMC and Synopsys have already set foundation for Next-Generation Chip Manufacturing (using heavily utilizes learning based solutions: developer.nvidia.com/culitho 2) Renesas and Cadence have collaborated to develop a novel approach to address verification and other design based challenge by leveraging generative AI's LLM capabilities: www.cadence.com/en_US/home/resources/success-stories/renesas-and-cadence-ss.html 3) Arm and Synopsys have partnered to accelerate custom silicon on advanced nodes using AI-driven approaches: investor.synopsys.com/news/news-details/2023/Arm-and-Synopsys-Strengthen-Partnership-to-Accelerate-Custom-Silicon-on-Advanced-Nodes/default.aspx 4) TSMC and Synopsys are collaborating to advance 2nm development. AI-Powered tools from Synopsys will be at the core of this progress: news.synopsys.com/2023-09-25-Synopsys-and-TSMC-Collaborate-to-Accelerate-2nm-Innovation-for-Advanced-SoC-Design-with-Certified-Digital-and-Analog-Design-Flows

Thank you for asking. There are several known/de-facto reasons like data processing demand, compute demand, interconnects, networks and so on. However, for me, the main reason for AI-Ready data centers is to enable model based application development without worrying about underlying hardware and software architecture. This is to ensure, the reusability, portability and adaptability of any existing model (or AI Application) works when moving to new data training or model enhancement. Once a data center is AI-Ready, you simply take out the worry about re-working the the environment knobs.

Thank you for asking. Real World: Medical diagnosis and discovery is the one application that I believe will benefit a lot. As, this requires lot of research work and if Ghiplet architecture can process and present factual (up-to-date) data to physicians, who can then sound decision. Another Implication (positive): Is discover of new materials by performing operations on trillions parameters with ease. This could pave way for solutions that can further improve efficiency (by new material discovery) of several solutions including battery materials that are (in a sense) backbone of (mobile) computing world and slowly also become the pillars of automotive industry.

Thank you for watching.

Thank you. Agree. Still a long-way to go before one can see the impact via reduction in design time. Also, semiconductor design companies will take sometime to adopt. As IP/Data leak is a major concern. Nevertheless, it is clear that EDA industry is at least moving towards AI-Powered EDAs.

Thank you for watching this video. Agree. ATE full diagnostics often takes a lot of time. If AI-Enabled solution can help reduce via prediction, pin-pointing the issue without going through detailed checks, and thus improving OEE. Then it is a win-win scenario for equipment vendor and the production floor.

Thank you for watching the video. Quantum Computing (QC) will only speed up the existing use cases at the intersection of semiconductor design and AI. Major implication/impact of QC will be on the manufacturing processes like enhancing computational lithography including coming up new "raw materials" that could improve silicon efficiency.

PCIe and CXL are standards. PCIe based products are out in the market for many years. CXL based is slowly gaining traction. Read more about CXL here: community.intel.com/t5/Blogs/Products-and-Solutions/FPGA/CXL-Adoption-Ramps-with-New-Product-Announcements-from-Intel-and/post/1553306 And, here too: www.chetanpatil.in/the-case-of-high-speed-data-transfer-between-semiconductor-components-pcie-vs-cxl/

Kindly read this to start with: www.mckinsey.com/industries/semiconductors/our-insights/sustainability-in-semiconductor-operations-toward-net-zero-production And, you asked me same question earlier and also please refer to previous discussion.

@@ChetanArvindPatil Thank you very much for replying again sir

Thank you for the comment. Please consider subscribing to Semiconductor And Beyond Newsletter: www.ChetanPatil.in/NewsLetter And, connect on LinkedIn and other social platforms: - LinkedIn: www.linkedin.com/in/ChetanArvindPatil - Twitter: twitter.com/iChetanPatil - Facebook: facebook.com/iChetanArvindPatil - Instagram: instagram.com/ChetanArvindPatil - Threads: threads.net/ChetanArvindPatil

Thank you for reaching out. You might want to read more about what type of research work is on going in the sustainable chip development and then you can enhance your skills accordingly. Example: github.com/mlco2/codecarbon

@@ChetanArvindPatil Thank you very much sir but how carbon emissions from computation is related to sustainable chip development. Also, is the research going on just making ICs Or other components. Please clarify sir

@@murikirenuka8998 Kindly search and read relevant research papers.

@@ChetanArvindPatil Thank you

Thank you for the feedback. I will plan accordingly. Do connect on LinkedIn: www.linkedin.com/in/chetanarvindpatil/

Thank you for watching the video.

Hi Robert Quinn, It is across different process steps. Mainly due to the increase in mask steps for every new advanced node. It restricts the layers processed at a given time. And if a FAB handles different products for the same advanced nodes, then managing and scheduling impact the wafers processed per day, thus impacting the capacity. Thank You, Chetan Arvind Patil

Hi Kal Gandikota, Thank you for taking the time to watch the videos. Appreciate your feedback, you are correct, and I should plan for it. Thank You, Chetan Arvind Patil

@@ChetanArvindPatil Thank you for understanding. Please do not be discouraged about the number of views because there is a beginning for everything. See you could join India's VLSI groups on facebooks and other social media to promote your channel. May be you could come up with a udemy course curriculum based on all the videos you are presenting here. Wish you the best once again!

Hi @@kalgandikota7075, Thank you for the input. Appreciate it. Thank You, Chetan Arvind Patil

Hi @ctrltheory, There are several ways to enhance the skills. - Thesis - Internships - Publishing And Reading Research Papers - Open Source Projects Universities should also align the curriculum to meet the goals and aspirations of students. Thank You, Chetan Arvind Patil

Hi Akshay, Thank you for watching. Thank You, Chetan Arvind Patil

Hi @CHALLA KONDA REDDY, Please connect on LinkedIn: www.linkedin.com/in/ChetanArvindPatil Thank You, Chetan Arvind Patil

Hi Sharvil, Thank you for your comments. 🙂 "Which companies should it acquire and what revenue synergies will result from such a transaction?" The above questions are not easy to answer. More than the margin, I believe the M&A moves are driven mainly by the long-term roadmap (products) the given company has and how well this roadmap aligns with the market demand. So, it all boils down to which are such promising semiconductor companies. The best process, I believe, will be to first focus on regions/countries that have well-established mid-scale embedded/semiconductor companies. Then gauge how M&A will help the acquirer in the long run. Another avenue could be to focus on companies with strong IP/Patent/R&D and then acquire their assets for driving new product development. You might find a list of region specific companies here: - www.Semiconductor360.com/ - www.HollandSemiconductors.nl/ I hope this helps. Thank You, Chetan Arvind Patil

@@ChetanArvindPatil That does help a lot actually. What sort of niche technologies such as mixed signal integrated circuits which have surfaced recently do you see becoming beneficial for Renesas? Also, there are some drawbacks with the mixed signal model so what technology do you think is beneficial to overcome these drawbacks? Thank you. Regards, Sharvil

Hi @@jawaharlalnehru4992, I cannot comment on what can and cannot work for a given company. Decisions to target analog, digital, or mixed-signal products are often based on the technology market any given company wants to target. Thank You, Chetan Arvind Patil

Hi@@jawaharlalnehru4992 , You can easily find all such information online. I will let you do that as part of your competition. 🙂 Thank You, Chetan Arvind Patil

@@ChetanArvindPatil Just one last question: Can a company own manufacturing facilities (wafer fab) in one country and testing and packaging facilities in another? Is this financially viable?