Deep Dive Digital Twins — The Next Big thing or Bust for Semiconductor Industry?
What is Digital Twins?
Digital Twins is a virtual replicas for most of the time physical asset such as machines and building. Why do those assets need replicas — simple they need to tract and manipulate it digitally for the purpose of mapping, gaining insight, understand the process all for decision makers (stakeholders ranging from infrastructure developer, real estate manager, traffic controller, to even courier and supply chain manager able to create efficiency and productivity). The opportunity is still have yet to be fully explored.
Most of the time this digital simulation is a living digital simulation, which by any mean incorporate most of things like sensors, AI, drones to understand update and survey physical installation.
Not to mention advance company in the transportation industry and automotive can use digital twins where they can thoroughly test, debug, refine system virtually. These are running in the virtual world across the electrical and mechanical domains with real-world input before committing to manufacturing. Digital twins will become even more useful as autonomous driving becomes connected to the grid of smart infrastructure.
Most of Digital Twins technology can be classified in this sector :
- Digital Twin can be used for development purpose (mostly used for semiconductor flow which usually involve in software development);
- Digital twins that are related to “the” data (digital healthcare replica, digital twin of a person);
- Digital twin that are related to “maintained” the data.
Wait… is Digital Twin similar to The Sims Game?
In a sense some application of digital twins actually can be imagined as something similar as playing “the sims” game, this simulation and digital copy of ourselves are now being utilised by corporation to create and prepare for certain scenario. Check this case study below to understand more the application of digital twins in the market currently.
Case Study of Digital Twins :
Notable new entry of digital twin company in 2020
- Case Study : 1 “Escape” — Cybersecurity for Developer
With the emerging new cyberattack, Escape is the come in as a SaaS platform entirely dedicated to developers security processes, allowing them to focus on their core business: shipping and maintaining features.
Escape is based on the creation of a minimal digital twin containing only the data useful for simulating computer attacks. The analysis of the different attack scenarios, powered by AI, enables the detection, prioritizations and correction of vulnerabilities. They Explore attack scenarios and use an interactive interface to visualize and explore attack scenarios leading to your critical services, in a format compatible with the MITRE ATT&CK framework.
2. Case Study : 2 “Mindbank” — Store a Digital Copy of Yourself
Store your wisdom, grow through self reflection, and let your story live forever
This application called mindbank sounds really similar and perhaps can also be also from blackmirror movies.
It’s an online platform intended to store and secure personal knowledge using structured learning algorithms. The company’s platform facilitates users to create “digital twin” based on the database of their knowledge, philosophy and personal stories, enabling users to build an artificial consciousness for personal growth and development.
3. Case : 3 Veerum — Replica of Project on your Screen
Developer of an industrial Internet of Things (IIoT) platform designed to increase capital project productivity. The company’s platform uses digital twin technology and artificial intelligence to create a virtual replica of a physical project or environment that can be used to simulate, operate, and analyze, while also building a single source of project truth by generating trustworthy, actionable information to base decisions upon, enabling project teams to predict and resolve issues in the virtual world before they impact cost and schedule in reality.
Why Digital Twin can be big for SemiConductor Industry?
In 2020 alone seminconductor industry has gain tremendous gains from the innovation of Exploratory data analysis (EDA) hailed by the data scientist this technology.
Fueled by the medical application due to Covid19, Chips is a new open market for the new system who are doing project group designing. Most of the time in the recent years chips take years to design, resulting in the need to speculate about how to optimize the next generation of chips.
Some try to use AI and ML. Applying AI and ML semiconductor industry is always looking for the new open-source environments and low-cost implementation tools. The result is an explosion of new, cheaper chip designs, and far more rapid solutions delivery targeting a host of new applications. The winners may, in turn, become the high-end chip designs of the future. With this in mind, Google chip implementation and infrastructure team has recently claim their AI are able to create chip design that completes in under six hours on average, which is significantly faster than the weeks it takes human experts in the loop.
If we take AI and ML to help innovate and accelerate innovation of chips design, Digital Twins come in to help enables manufacturer of semiconductor to make design adjustment prior to beginning mass production, and factory adjustments during production.
With this in mind, semiconductor industry will be able to :
- Better returns for products sent to market due to higher quality designs and tailored products optimize for the target consumer, which leads to increased sales
- Reduced design cycle times decreasing the number of iterations and not having to wait for samples to arrive and send physical samples back and forth with adjustments
- Increased ability to get products to market faster
- Better knowledge of what is going to market, and ability to very products with confidence.
The problem with Digital Twin in Semiconductor
Though it seems like a great idea, but not a reality yet — at least not to the extent of seamlessly covering all abstraction layers of a complex system..
Issue #1 — Interoperability
There is currently few big players that are trying to develop proprietary platforms for modeling a digital twin of a car, for example. In principle, that is great. It would allow manufacturers to have a complete virtual view of the car, including electronic, electrical, mechanical, and physical aspects. The problem is that monolithic platforms are weak by construction.
Issue #2 — Hard to execute
The boundaries between stages is blurring.
“Suppose we tape out a design and target a new production facility,” says Fram Akiki, vice president of electronics industry strategy at Siemens PLM Software.
“The production people may come back and say that based on the design you are using a lot of high-performance transistors, you have added a couple of layers of metal, etc. They may conclude that they need to spend another $30 million or $40 million of capital to make sure they optimize the production line for this design . If they don’t spend the money and it turns out to be a bottleneck, you are dead.
