More Than Inspection: It’s Process Improvement

Estimated reading time: 10 minutes

Barry Matties speaks with Brian D’Amico, president of MIRTEC, about the current state of machine-to-machine communication in the industry, and how the inspection company is interfacing with the different manufacturing languages currently available to gather predictive data and feedback from every inspection step to eliminate future defects.

Barry Matties: First, can you tell our readers a little bit about MIRTEC?

Brian D’Amico: Certainly, Barry. MIRTEC is a leading global supplier of inspection equipment to the electronics manufacturing industry. Our company is basically comprised of three separate divisions serving the SMT, LED, and semiconductor industries. We also own a camera division—ISVI Corporation—which manufactures cameras for all of the various systems across these divisions.

Matties: Let’s start with SPI. In the SPI realm, what challenges or trends do you see from your customers?

D’Amico: In terms of the SPI machine, there is a trend toward closed-loop feedback. In other words, the SPI system must be capable of communicating upstream with the screen printer/dispenser as well as downstream with the pick-and-place machine. The goal is to provide actionable data to the screen printer and pick-and-place systems that may be used to reduce defects and maximize efficiency. Industry 4.0 is a topic of much discussion within the electronics manufacturing industry. Manufacturers and vendors are trying to come to terms with what that means. In the most simplistic of terms, Industry 4.0 is a trend toward automation and data exchange within the manufacturing process. This requires connectivity and communication from machine to machine within the manufacturing line. The challenge is to collect data from each of the systems within the line and make that data available to the rest of the machines.

Matties: When you mentioned communicating, are machines upstream and downstream making adjustments and fine tuning to that particular panel?

D’Amico: Yes; however, these adjustments are typically based on trends within the inspection data. For example, let’s say that the SPI machine detects an offset trend in solder depositions with regard to pad location. Under these circumstances, the SPI machine would report the trend to the screen printer. The screen printer, in turn, may then use this data to adjust the location of the stencil to the pads to eliminate the offset. Please keep in mind that the goal is not to control the screen printer, but to provide actionable data that may be used to eliminate the offset trend and potential defects.

Matties: Does the machine do this without human intervention?

D’Amico: Yes and no; it’s really up to each vendor to determine whether adjustments are to be made manually or autonomously. Most vendors will provide a means of selecting between manual and automatic mode.

Matties: This is a big deal in yield performance.

D’Amico: Agreed.

Matties: Is there a proprietary or a universal language that people can tap into their data models?

D’Amico: That is a very good question. There's a trend towards standardization right now. Keep in mind that there are two basic requirements for machine-to-machine communication. The first is connectivity, and the second is data sharing from machine to machine. The goal is to format the available process data from each respective machine in a manner which is usable by other machines within the line.

If we take this one step further, the ultimate goal is to analyze big data. This is where we want to use artificial intelligence (AI) to help determine where the manufacturing process broke down and make recommended corrective actions. That's a while off yet; it requires a lot of data and AI to sort through it and come up with predictions.

Matties: If we can predict it, then the machines can adjust on the fly, which eliminates the need for personnel in line.

D’Amico: That's the key. There's no way to beat a human being right now because humans can evaluate all of the various process information a lot better than a machine can at this time. The goal of any AI engine is to try to approximate the decision-making capability of a good process engineer, but I think that's quite a bit off. It’s going to require a lot of data and processing power to get there, but that's the holy grail—to have the machines communicate with each other and come up with predictive data that eliminates defects at each stage of the manufacturing process.

Matties: How many of your customers or prospects are demanding this, or is this a requirement for doing business with them?

D’Amico: With the movement toward Industry 4.0, I find that a lot of people are asking the same question, “Are these machines Industry 4.0 capable?” I understand the reason for this concern. When a manufacturer invests in a new piece of assembly equipment, they expect to use the equipment for a period of five to 10 years or more. So, it stands to reason that any new piece of assembly equipment must provide the required process data as well as the ability to communicate with other machines within the line. These are the basic requirements for Industry 4.0 capability.

Matties: You started by saying there's this movement toward Industry 4.0, but what does that really mean? How do customers define that or distill it down to something that's meaningful? Do they come with you and say, "We already have this language in our shop, and we want you to adhere to that?"

D’Amico: It’s important to keep in mind that Industry 4.0 is still evolving. Thus, it’s not a matter of somebody saying, "This is what I have now, and you have to adhere to it;" it's more like, "Can you tell me what this is all about?" The bottom line is that all of us are still in the midst of defining Industry 4.0 and how it pertains to the electronics manufacturing industry.

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