Microchips – Demand, Industry, and Shortage

The microchip became one of the most important strategic materials in the 21st century. Almost everything we use depends on microchips. From your iPhone, your toaster to fighter jets, and automobiles. Microchips became a part of our daily lives and, therefore, the heart of our modern society. The development of AI, the internet of things, and the self-driving car revolution won’t stop this trend.

From Semiconductors To Microchips

All this technological advancement builds on top of a simple group of materials called semiconductors. When passing through a conductor, electricity faces little resistance, creating a free-flowing current. In an insulator, electrical current cannot travel due to high levels of resistance. Semiconductors sit somewhere between these two extremes, allowing a degree of control over the flow of electricity by providing a change of electric fields. Silicon semiconductors are the industry standard for most transistors. Transistors are devices that regulate current and act as switches for electronic signals. These transistors are crucial to microchip manufacturing, from processors to memory cards.

Semiconductor Industry

The semiconductor industry has professionalized, and today, companies in the field typically specialize in one of the following domains:

Mining: China is with two-thirds of the worldwide production by far the world’s largest producer of silicon and therefore the producer of the essential material for microchips. Other producers are Russia, the USA, Norway, and Brazil.

Chip Design defines how many cores a microchip should have, how those and other components such as memory are arranged on the silicon, and how the circuits should actually look like. Chip Designers normally outsource the chip manufacturing to fab foundries (microchip manufacturers). Famous chip designers are AMD, Apple, Amazon, Alphabet, and a lot more.

Fabrication: There are a handful of fab foundries. Intel, Samsung, and TSMC are the Top 3 leading companies by sales revenue in this field. While Intel designs its own microchips, there are other companies like TSMC specializing in manufacturing microchips for other companies and is, therefore, a pure-play fab foundry. In the field of fab foundries, TSMC (ca. 50% market share, Taiwan), Samsung (ca. 20% Market Share, South Korea), Global Foundries (ca. 8% market share, USA), UMC (ca. 7%, Taiwan), SMIC (ca. 5%, China) are the most noticeable ones. TSMC delivers its microchips to famous tech players like AMD, Apple, ARM, Broadcom, Nvidia, and Qualcomm. TrendForce and ReportLinker estimated a foundries revenue in 2020 with 70 Billion dollars and an average turnover of 10% per year over the next decade.

Equipment: The high-tech industry of semiconductors needs one of the advanced engineered machines in the world. Without the most advanced machines, no manufacturer would keep up with the competition. The Dutch company ASML makes lithography systems, which are machines that are used to make chips. All major chipmakers use their technology because ASML lithography systems are the most advanced systems in this field with years of distance.

Semiconductor Microchip Shortage

The 2020 global microchip shortage is an ongoing crisis. The demand for microchips is greater than the supply and has led to major shortages and queues amongst consumers, not only in the information technology sector. According to AlixPartners, the chip shortage could cost the automotive industry around the world a loss of 61 Billion dollars. So how did this shortage started?

One major reason is the tech war between the USA and China. The outsourcing of AMD’s chip production to TSMC created additional pressure on TSMC production plants during the pandemic, and the Covid-19 crisis itself.

Semiconductors are no longer just components, but strategic resources that all major economies must secure.

Arisa Liu (Analyst, Taiwan Economic Research Institute)

Amazingly there are only a handful of major microchip manufacturers in the world (TSMC, Samsung, Intel). Whoever has secure access to microchips can make their economy more robust against these global shortages. In this way, microchips became more or less the new oil of the 21st century.

So, there will be an increased effort for all countries to secure the demand for microchips for their economies in the future. This effect can be already observed in various countries like the USA, strengthening their semiconductor microchip production.

Defense against Slaughterbot Attacks

Slaughterbots is a video that presents a dramatized near-future scenario where swarms of inexpensive microdrones use artificial intelligence, explosives, and facial recognition to assassinate political opponents by crashing into them. In my opinion, it is one of the most dystopian and depressing near-future scenario that I know.

When I watched the video the first time in 2017, I had no idea how we could defend towns against such terror attacks. Shooting against so many microdrones does not make sense. It also makes no sense to block the radio signals because the microdrones fly totally autonomous. Now, some years later, I realized that we could use secure machine learning to defend security-critical areas like shopping malls or train stations.

Many practical machine learning systems, like self-driving cars, are operating in the physical world. By adding adversarial stickers (patches) on top of, e.g., traffic signs, self-driving cars get fooled by these stickers.

Patch attacks projected on monitors in hallways, train stations, and so on could fool facial recognization systems on such suicide bombers. In this scenario, it is important to iterate over a lot of pretested patch attacks on test classifiers to find a potential weakness in the mini drones. After an effective attack was found, we could project this successful attack on all available screens in the attacked area.

When we imagine that nowadays shopping malls have physical barriers against terrorist truck attacks, nuclear underground utilities, and explosives in Swiss bridges it is not hard to imagine that we could develop an emergency program for public available monitors which could help defense against adversarial Slaughterbot attacks.

Problems of Generating Real-World Patch Attacks

Of course, there are still some problems left for generating real-world patch attacks. Images of the same objects, for example, are in real-world conditions unlikely exactly the same. To successfully realize physical attacks, attackers need to find image patches that are independent of the exact imaging condition, such as changes in pose and lighting. For that, we need to find adversarial patches that generalize beyond a single image. To enhance the generality of the patch, we look for patches that can cause any image in a set of inputs to be misclassified. For that reason, we formalize the generation of real-world patch attack as an optimization problem.

To find a quite universal patch for a certain classifier, it is important that we solved the optimisation problem for a lot of different classifiers before the actual attack.

AI Safety Concerns in Warfare

Governments around the world are increasingly investing in autonomous military systems. Many are already developing programs and technologies that they hope will give them an edge over their adversaries.

AI in Warfare

Target Systems Analysis (TSA) and Target Audience Analysis (TAA) are intelligence-related methods to develop a deep understanding of potential areas for operations. Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. For military forces on the ground, the challenge is to conceal their presence as much as possible from discovery from the air. A common way of hiding military assets from sight is camouflage, for example, by using camouflage nets.

Algorithmic Targeting: Today autonomous weapon platforms are using computer vision to identify, track targets, and shoot targets. These algorithmic targeting technologies are nowadays so precise, that even the military adds randomness to the targeting process to spread the bullets over the target. Otherwise, the bullet would just fly through the already generated holes.

Problem

These previously mentioned AI systems outperform human experts dramatically. But there are still major issues with these technologies. These systems are vulnerable to adversarial noise. You can imagine this (adversarial) noise as blurry pixels on your Instagram photos, usually, these pixel perturbations are so small that none of your human followers will see them but when Instagram uses an image classification software to check your account for illegal posts, it could happen (in the WORST CASE) that Instagram will block you.

Manipulating these AI systems

So what can you do when you forgot your camouflage net or when you don’t want a huge hole in your battleship? Well, you could try to generate this previously mentioned adversarial noise and stick it on your battleship or airplane. So from the point of mobility, it would be much easier to print a specific pattern on top of your battleship to hide from adversarial drones as carrying camouflage nets.

A plane with an adversarial noise patch camouflage that can hide it from being automatically detected from the air (Source: https://arxiv.org/pdf/2008.13671.pdf).

Summary

In this post, I wanted to describe the current weaknesses of AI systems, especially in warfare. The biggest problem of the current AI technology is (not only in warfare), that they are quite vulnerable against (adversarial) noise. This should be considered before releasing fully autonomous war machines that could make huge damage because of perturbated pixels in input data.