The Digital Age is bringing a wave to change to 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.

Breaking Down the Smart Factory

A Smart Factory concept blends digital transformation and intelligent manufacturing trends. The notable drivers of these trends are the Internet of Things (IoT) and Artificial Intelligence (AI) but it is important to understand the interaction between the two.

IoT concerns the ability of all electronically-enabled sensors, devices equipment and applications to be interconnect, so that each can be identified by others in the network. The industry has coined the phrase ‘Industrial IoT’ (IIoT) to describe the flavour of IoT within an industrial ecosystem or organisation.

Artifical Intelligence is when a digital system itself is capable of completing objectives or tasks, and is also able to learn from the data in a seemingly intelligent way.

The synergy in the intersection of IoT (or IIoT) with AI 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 the brains of the organism – orchestrating, analysing, deciding and acting.

AIoT in Electronics Manufacturing

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.

At its core, the crucial characteristic defining the AIoT promise 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.

An AIoT-enabled data analytics set-up augments 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 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. Root cause analysis is also very onerous when defects are detected, with the massive data points typically involved making traditional analysis very difficult. The cost of product recalls can also be very costly.

The relentless chase towards zero DPPM is hard 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 analyzed in real time and out-of-control conditions 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 – totally revolutionizes and transforms current industrial approaches toward Quality Control.

Taking AIoT Forward

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. Apart from productivity and yield improvements, technological advancements and breakthroughs that 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.

ASMPT’s AIoT approach to realize the Smart Factory for electronics manufacturing involves 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.

But it can be a worthwhile journey. 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. The ultimate goal is to enable customers to produce faster, better and more cost- effectively and flexibly, without compromising increasingly stringent standards of safety and quality.

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