factory connectivity

Ride the Wave of Smarter Manufacturing The year 2020 sparked a tremendous acceleration in the digital transformation worldwide, driving a sharp rise in demand for semiconductors and escalating pressure on chip factories to reduce manual functions on the shop floor. The mindset of the semiconductor industry saw a remarkable shift as it recognized with heightened urgency the need to deploy data-driven visualization, analysis, scheduling and dispatching solutions to increase automation to improve production speed and efficiency. Amidst the new excitement around Industry 4.0, chip manufacturers are rapidly deploying new technologies including IIoT, big data, machine learning and Autonomous Intelligent Vehicles (AIVs). Yet for many chip manufacturers, the path to building a smart factory is far from clear because they lack an overall digital transformation strategy. Smart manufacturing is a broad concept covering an array of technologies and solutions, making a holistic, mid- to long-term digitalization strategy rooted in the overall business strategy crucial. There are no shortcuts that can move a manufacturer instantly to Industry 4.0. Instead, this transformation is a step-by-step undertaking with a natural evolution. Some Factory Tasks Must Remain Manual – For Now The semiconductor industry has reached a point where manual processes are no longer efficient enough to support mass chip customization and remote operations. The many technological and standardization advances behind automation can help streamline some of a factory’s most labor-intensive tasks including the loading or unloading of machines or lot tracking and data collection while reducing operational costs. Still, some tasks remain very difficult to automate. For example, handling errors and exceptions presents the greatest challenge since some errors are hard to anticipate. What’s more, the cost of automating error handling can be prohibitive. Eliminating Gaps in Connectivity Often, critical data sources aren’t available due to lack of equipment integration, incomplete product quality monitoring or gaps in material tracking. Closing these gaps in connectivity enables the collection of data and provides rich, reliable information for analysis and reporting that can drive continuous operational improvements, optimizations and efficiencies throughout a factory. But keep in mind that data integration alone can be a challenging task. The selection and proper enrichment of relevant data is, in many cases, not just a technical problem but requires a detailed and in-depth knowledge of the manufacturing steps to be analyzed and optimized. Even when data is available, it might be still difficult to make decisions or implement improvements if it is in siloed systems that require manual processes to integrate and translate into useful information. Problem solving at this level is possible but extremely time-consuming. Manual integration is not only ineffective but costly, draining time, human resources and money from the factory. The right contextual information for the data is vital to unleash its potential and make improvements possible. Dispersed solutions cannot control processes because they span functional areas and people, physical and business entities. Backbone software for shop-floor operations that controls all other applications is central to smart manufacturing. Data-Driven Manufacturing The semiconductor industry is expert in data collection and leads many other industries in this area. The problem is often that chip companies use only a fraction of the information they collect for the analysis and insights needed to improve operational efficiency. By comprehensively integrating all distributed data into a single version of truth – in one location where it is always available – companies can make data analysis and problem solving almost frictionless. Keep in mind that data platforms and edge solutions, within the context of manufacturing, will not be adopted as part of a greenfield initiative. Building a solid automation architecture is only feasible and beneficial by deploying new technologies such as machine learning and artificial intelligence (AI). Analysis of historical data provides important context and reveals deviations such as unexpected process time, uncommon material accumulations or issues with material transport. By integrating swift control actions for new data point collected, manufacturing operations can shift from reactive problem-solving to proactive analysis and operational improvements. The tremendous increase in interest and investment in AI for manufacturing automation only became possible with the availability of low-cost sensors that generate huge volumes of data and solutions for storing and processing that at low cost. AI and other leading-edge technologies transform the tedious but critical process of extracting insights from data, making it instantaneous, streamlined and achievable for every manufacturer. The maturity of smart manufacturing hinges on the extent to which a factory is data-driven. This requires foundational investments to improve traceability, connectivity and real-time operations – and finally making sure that data helps us what to do and when to do it. Ricco WALTER is managing director of SYSTEMA Automation in Singapore.

I recently spoke with Chan Pin CHONG, Executive Vice President and General Manager of Products and Solutions at Kulicke Soffa, about how smart manufacturing is driving new production efficiencies in the semiconductor industry. During our conversation, he also provided practical steps for factory operators to follow in evaluating their smart manufacturing needs in order to ensure successful implementation and discussed the potential payoffs. Based in Singapore, Kulicke Soffa is a leading global provider of ball bonding, advanced packaging, wedge bonding, and electronic assembly equipment for the semiconductor, power and automotive industries.Ng: Industry 4.0 and smart manufacturing are critical to the growth of the semiconductor industry. What does the smart manufacturing movement mean to you or Kulicke Soffa?Chong: The future of smart manufacturing is the vision of building a digital connected factory to drive new manufacturing efficiencies by combining physical and cyber technologies. Industry 4.0 integrates discrete systems and harnesses the power of large volumes of data to move towards greater automation.At K S, we define smart manufacturing across the following four key areas embedded in our roadmap for all K S products, from wire bonders and advance placement tools to pick and place machines: Interoperability – This is about machines, devices and sensors connecting to each other. In fact, the very basis of smart manufacturing is that all devices are connected. Information transparency – Through simulation, various artificial intelligence (AI) tools use contextual information to emulate the actual world. Technical assistance – Robots or machines support humans in making decisions or solving problems. Autonomous decision-making – This is our vision that robots or machines can learn from machines to make decisions on their own. Ng: Please elaborate on some of these areas and how they’re the relevant to smart manufacturing. Chong: The need for machines, devices and people to communicate with each other forms the basis of connectivity, the idea of all machines communicating with each other or a host. Connectivity protocols now in place for machine-to-machine connectivity include SEMA, SECS/GEM, SEMI-ELS and IPC-CFX. Machine technology uses various sensing technologies. For example, for a pick and place machine such as SMT platform on K S Hybrid, the algorithm to recognize and align processes is part of the sensor needed in each machine before to can process and add thousands of components to the substrate or panel. In a wire bonder, the ultrasonics or EFO signal can provide some form of sensing technology for a machine to detect process conditions. Importantly, these sensing technologies can be used to collect feedback for process improvements.One example of how K S machines are connected to the host is our use of an intermediate server or host named KNeXt to connects to all assembly equipment in the fab. The equipment can then, in turn, connect to an external secured cloud or K S Global Cloud.Ng: What are the objectives for smart manufacturing?Chong: The ultimate goal is to achieve higher factory productivity or better OEE (Overall Equipment Effectiveness) by improving machine yields, productivity and efficiency. The key is to leverage AI, 5G, the Internet of Things (Iot) and other industry 4.0 technologies to drive automation and process improvements. Ultimately, each factory must meet productivity, yield and cost goals. Smart manufacturing enables factory operators to meet these goals. That is the focus of smart manufacturing.Ng: What is the biggest potential benefit of smart manufacturing?Chong: Smart manufacturing uses data to predict outcomes of a process step or machine operation. Once data is available in the global cloud, analytics can start to build data sets to run statistical modelling and examine factory operation trends. We can also use the data to identify past machine behaviors in order anticipate outcomes, including undesirable ones that we can then prevent.In the SMT example, if we can systematically examine days or weeks of historical performance, we can plot some statistical variations in the process specifically to a pick or placer or a robot and anticipate or avoid problems. However, all sensors must be in place in the bond head or the robot so that we can detect changes or variations in the robot’s movements.Kulicke Soffa smart manufacturing facility Ng: What are some recent factory improvements smart manufacturing has enabled? Chong: Kulicke Soffa has contributed to the hierarchical architecture of the smart factory and key technologies. COVID-19 is driving demand for greater factory connectivity, and K S offers solutions that are key to remote management and full control of smart equipment from a central control and embedding Internet of Things (IoT), big data, cloud computing and sensors in manufacturing. Using these technologies, a small smart factory can be remotely operated and managed.With COVID-19 limiting air travel around the world and access to support engineers, the need has grown for remote machine access to reduce the downtime per machine. Remote factory access enables off-site engineers to remotely identify and diagnose machine problems.Ng: What are barriers to faster adoption of smart factories?Chong: While most smart factories are capable of network connectivity and data collection, a key challenge is the lack of a business model for smart factories and smart equipment. Most factories must justify major capital investments by demonstrating ROI (Return of investments) potential. Capital improvements for every factory usually take several years to implement and are based on a complex business model. Factory connectivity requires substantial investments and years to implement. The same is true of the cloud infrastructure buildouts necessary to generate big data and meaningful analytics. The executive mandate for factory management to install capability usually calls for specific business targets in the planning stage.Another longstanding barrier to entry is the lack of compatibility of existing tools with new factory protocols, raising the question of whether the cost of replacing legacy tools justifies the need for a smart factory. If new factory investment is required for the latest tools to support the production of new products, the ROI will be much easier to justify.Ng: How is AI is important in smart manufacturing?Chong: AI interprets and learn from data to perform tasks and meet specific goals. Good examples of AI implementations include Amazon’s Siri and Alexa voice-command devices and self-driving cars being developed by Google and Tesla.At K S, over the years we’ve implemented AI in our smart wire bonders to reduce human intervention in our ProCu-7, PSP-2, ProCu Loop 2, Pro Bump and overhang processes.Thanks to AI, with senses of signals from the bonder, we can reduce the amount of parameters that an engineer or technician have to do trial and error. With on bonder metrology, PBI, loop height, wire sway features, AI allows us to measure process efficiency and provide feedback.Over several years of AI development, we have leveraged the technology to monitor machines and provide real-time performance feedback in order to provide better closed loop control such as short tail recovery in our bonder process. We can also use the data to predict machine behavior, monitor its health and track maintenance. Ultimately, AI enables fabs to improve manufacturing efficiency, productivity, yields and device quality.Ng: What’s an example of how AI has solved your manufacturing equipment problems?Chong: We’ve used AI to set RPM (real time monitor) limits, identify defective P-parts and monitor various conditions such as wire size and capillaries. These types of cases can arise in any manufacturing environment as humans make process mistakes or use the wrong part for a machine. With AI, we can prevent these problems and reduce the risk of further material lost from the wire bonding process.Ng: What advice do you have for factories looking to implement smart manufacturing systems?Chong: To build a smart factory, start by focusing on a clear set of business objectives and how smart manufacturing will help minimize or eliminate current factory inefficiencies. In other words, start with the end in mind – the problems that needs to be solved and the business goals – and identify the information you need to demonstrate ROI. Do you need to resolve, automate or improve processes or just to be more efficient? Before investing millions or billions of dollars to build a smart factory, identify those clear goals upfront. Then map out the particulars of implementation to avoid major problems around standards, protocols and connectivity.Bee Bee Ng is president of SEMI Southeast Asia.