06.01.2021
In tandem with the Smartphone’s revolution of consumer and business life, a similar wave of change is underway at the factory and production floor, offering the promise of radically transforming the industrial space with a vision of the ‘Smart Factory’ of the future.
The Fourth Industrial Revolution (Industry 4.0) is essentially the Digital Age, characterised by a heavy focus on automation, real-time data, connectivity, embedded sensors, and machine learning. Its iconic representation is probably the Smartphone – a powerful handheld computer extending the power, reach and versatility of the Internet to all corners of the globe. In tandem with the Smartphone’s revolution of consumer and business life, a similar wave of change is underway at the factory and production floor, offering the promise of radically transforming the industrial space with a vision of the ‘Smart Factory’ of the future.
However, definitions of a ‘Smart Factory’ are many, forcing manufacturing companies all over the world to grapple with uncertainty about the dramatic changes they need to plan for in order to help their business thrive in the future.
A Smart Factory concept is centred around the confluence of digital transformation and intelligent manufacturing trends. The notable components driving these trends are the Internet of Things (IoT) and Artificial Intelligence (AI), and it is important to understand the intersection between the two as an overall concept, in terms of its expression with the manufacturing space, and more specifically, the electronics assembly space in which ASMPT is a leader.
The IoT concerns the ability of all electronically-enabled devices and equipment to be connected, identifiable in a network, collecting and processing of data. The industry has coined the phrase ‘Industrial IoT’ (IIoT) to describe the flavour of IoT within an industrial ecosystem or organisation, basically “interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management.” (Wikipedia).
Then we have AI, when a system itself is capable of completing objectives or tasks, and is also able to learn from the data in a seemingly intelligent way.
The potential synergy lies in the intersection of IoT (or IIoT) with AI, what has been termed an ‘AI of Things’ (AIoT). Essentially, AI is infused into an IIoT environment to enable the devices and equipment to be able to independently examine data, analyse it, make decisions and act on the basis of those decisions, all without any human involvement.
A rough illustration here could be the IIoT components as a body’s nervous system, with the AI capability as the brains of the organism - orchestrating, analysing, deciding and acting.
AIoT represents a fundamental paradigm shift for the industry and a massive step towards a vision of achieving the Holy Grail of zero DPPM manufacturing while boosting quality, yield and cost standards across the entire electronics manufacturing value chain.
ASMPT AIoT approach to realize the Smart Factory for electronics manufacturing is a detailed methodology requiring patience and commitment on a journey that can span years with challenges such as handling/ enabling legacy equipment, software integration efforts, and the ubiquitous ROI justifications to stakeholders, among others.
At its core, the crucial characteristic defining the AIoT promise (and therefore the Smart Factory) is the massive proliferation of intelligent, automated decision making capabilities across the IIoT network. Decisions begin with the analysis of data, so enabling data analytics with an AIoT approach can be a logical and attractive starting point with relatively low initial investment.
Current data analytics capabilities already possess good ROI in their inherent ability to help improve the productivity, quality, yield, or optimise the cost of producing products. An AIoT-enabled data analytics set-up augments these benefits by generating useful data-driven insights that can be used to help the manufacturing system learn from, be optimized, and generate higher performance, or to help users make better decisions.
Let’s look at one example. Wire Bonding is one of the critical processes in semiconductor assembly that can generate defects, and quality assurance via sampling to spot defects at the factory gate is a typical step. However, sampling has its risks, as defective components can escape detection, with serious liability issues when lives are put at risk. Root cause analysis is also very onerous when defects are detected, massive data points are typically involved making traditional analysis very difficult. The cost of product recalls can also be very costly.
Consider that a single defective component that costs USD10 - if allowed to reach the market - could result in a mass recall activity that could cost billions, making the 2014 mass recall of 30 million GM vehicles due to a faulty ignition switch linked to 124 deaths and a recall cost of about USD1.0 billion look like small potatoes in comparison.
The relentless chase towards zero DPPM is thus understandable but a hard vision to achieve using traditional means. An AIoT-enhanced data analytics approach for the Wire Bonding Process flips this scenario on its head, with quality data points analysed in real time; supported by ASM Autonomous Metrology Equipment to provide on time, high integrity data points which are crucial for accurate and timely analysis. Out-of-control process anomalies, and defect generation predicted and automatically corrected before output quality is affected. Predictive, automated decision making such as this - 100% quality assurance without Human Intervention - would totally revolutionize and transform current industrial approaches toward Quality Control.
From this starting point in data analytics, the expertise and domain knowledge in incorporating AI into the IIoT environment could eventually be applied to the entire electronics manufacturing process. You will need a brain to continuously harness and apply the learning and bring intelligence to your automation systems, process equipment and assembly operation so as to create a network of intelligent IIoT devices that drive efficiency, effectiveness and insights. SkyEye – an IIoT platform software developed by ASMPT power your Smart Factory.
SkyEye manages and bring insights & intelligence across your macro and micro automation, process equipment, autonomous inspection and metrology. It revolutionized operation management through its remote functionality equipped with AR & VR capability; for off-site assistance, troubleshooting, recovery, and process development. Connectivity to ASM AIoT Cloud Services provide End to End Data Analytics Life Cycle & Knowledge Management for your AI enabled manufacturing
With an AIoT approach properly put in place, the entire manufacturing value chain can be increasingly equipped with new ways to develop, innovate, and manufacture. Technological advancements and breakthroughs are also more likely to happen due to powerful insights derived from AI-augmented data mining and analytics capabilities. Eventually, organisations that excel in the execution of AIoT will pull ahead of the pack in terms of continuous innovation, and become stronger. The ultimate goal is to produce faster, better and more cost- effective and flexible, without compromising increasingly stringent standards of safety and quality.