The rise of counterfeit parts, substandard quality, and unforeseen defects in the modern manufacturing industry has left manufacturers grappling with compromised product integrity, escalating costs, and eroding consumer trust.
Amidst this chaotic scenario, traditional approaches to quality assurance are proving insufficient. The consequences of a single malfunctioning component can ripple through entire supply chains, leading to costly recalls, reputational damage, and, most critically, endangering end-users.
Enter Cybord AI, a groundbreaking software solution at the forefront of innovation. Based in Herzliya, Israel, Cybord has recently secured a $4M seed investment led by IL Ventures, a visionary VC fund dedicated to transforming legacy industries through cutting-edge technologies. Co-investing alongside is the Israel Innovation Authority, recognizing Cybord’s potential to revolutionize the electronics manufacturing sector.
At the core of Cybord’s offering is a sophisticated blend of AI and Big Data technologies. Cybord utilizes Visual Deep-AI to meticulously analyze the physical attributes of electronic components – their age, quality, and potential defects. Remarkably, Cybord’s technology extends its prowess to detecting counterfeit parts, ensuring a comprehensive evaluation that goes beyond conventional measures.
Under the seasoned leadership of Oshri Cohen, Chief Executive Officer at Cybord, the company is poised to disrupt the industry norms, setting new standards for excellence and reliability.
In the following interview, Oshri Cohen unveils what’s next for Cybord.
Can you provide an overview of your journey from your early career at Mellanox to your current role as CEO at Cybord?
Before joining Cybord, I served as the VP of Supply Chain at Nvidia, and before that, I was the VP of Operations at Mellanox Technologies. In those roles, I had complete ownership of the entire supply chain process from beginning to end.
During my tenure, I witnessed quality-related issues associated with bad components that quickly resulted in painful warranty claims or, even worse, devastating recalls. This experience made me realize the importance of addressing poor-quality components while still on the production line.
To help Tier 1 OEMs like Nvidia and others significantly reduce their risk exposure, I joined Cybord as CEO. Today, the tools available for people who manage supply chains are not advanced enough and rely on statistics and assumptions. This old fashion approach made the supply chain progress slower than other areas in the industry. With the available, almost unlimited technologies, we can do much better today than what has been done in the past.
I decided I would like to utilize my experience in supply chain and operations to minimize manufacturing disruptions by preventing bad quality components from getting into our customers’ products and build a surgical traceability capability to serve our customers in case of bad products appeared in the field. The usage of AI combined with Big-data technology will change the way the world is managing its supply chains.
Can you tell us more about Cybord’s deep visual AI platform and how it works?
Our platform is a software-only solution that does not require any additional capital investments or modifications to production line layouts to deliver quality assurance. It relies on AI and big data analytics to analyze component images and production metadata received from the production line, ensuring all electronic components’ quality and visual traceability.
The Cybord AI platform is installed on a dedicated server within the EMS, which collects images from existing surface mount technology machines (SMT) and automated optical inspection (AOI) machines. This sets us apart from others in the industry.
Cybord AI platform seamlessly connects to component assembly machines, enabling near real-time inspection of 100% of components before they are assembled onto the boards. By analyzing all components within the assembly process, Cybord’s solution empowers customers to identify and eliminate faulty components, reducing costs by minimizing assembled boards’ scrap/rework rate and ensuring smooth and efficient product delivery times. Moreover, the platform is compatible with standard assembly machines, making it compatible with virtually every assembly line globally.
How does Cybord help OEMs and Electronics Manufacturing Services (EMSs) navigate risks in electronic components?
Cybord platform assists OEMs and EMSs in managing risks related to electronic components. We use advanced technology and statistics to ensure the components are high quality and reliable.
Studies show that almost 90% of component failures consist of some visual indications over the components themselves. Cybord solves this issue by meticulously identifying these failures, verifying the reliability of the component sources, and ensuring high homogeneity levels within the same reel. We conduct inspections that are free from visual defects.
As a result, our platform provides a complete inspection of 100% of the components integrated into a Printed Circuit Board Assembly (PCBA). This level of scrutiny is crucial for achieving surgical traceability, especially in a zero-trust supply chain environment where component integrity is vital.
Cybord technology is designed as a software solution that seamlessly integrates with pre-existing SMT and AOI machines and processes. This integration process is straightforward, making it a hassle-free installation for manufacturers.
Cybord works with some of the world’s largest machine manufacturers to receive component images as part of the production process without causing delays or requiring modifications to the existing assembly lines.
Moreover, our partnership with SMT machine manufacturers enables us to provide relevant analysis results back to these machines. This feedback loop empowers our customers to benefit from Cybord’s technology while utilizing their existing machine interfaces.
In the future, we aim to enhance our capabilities by enabling the option to provide feedback with even more relevant and valuable analysis results, further improving the overall quality of electronic components in the manufacturing process.
Our collaborative approach with OEMs and EMSs ensures that the risks associated with electronic components are effectively managed and mitigated, ultimately improving the quality and dependability of the final products.
What are the benefits of using AI and big data to visualize data collection to improve traceability standards in the manufacturing process of electronic components?
In recent years, the evolution of AI and big data has ushered in new possibilities for the industry. Cybord’s platform relies on deep neural network analysis, setting it apart from traditional machine vision technologies.
The key distinction lies in the fact that machine vision typically necessitates using a reference or “golden unit” for each component, a cumbersome and resource-intensive requirement. In contrast, AI, through its capacity for learning and adaptability, eliminates the need for such golden units or any specific, onerous requirement.
With AI, Cybord swiftly learns from a representative sample of components, typically ~ 2,000, to generate a robust AI model to analyze the components and identify anomalies.
Another strong benefit AI brings to the industry is related to component’s traceability. Current traceability standards often rely on log collection. However, the percentage of boards delivered to the field with wrong traceability data is standing on 7.5%.
Cybord’s AI-powered visual technology introduces a higher traceability accuracy, offering an efficient and cost-effective alternative to batch logging traceability. Every component on each board is meticulously analyzed, and its traceability data is extracted and identified by advanced AI algorithms.
The strength of Cybord’s traceability system lies in its visual evidence-based approach, setting it apart from existing solutions in the market. This unique approach empowers customers to investigate the root cause of failures before initiating recalls, significantly reducing the magnitude of the potential recall events.
What are the future plans for Cybord, and how does it plan to stay ahead of the competition?
Cybord has recently initiated the development of its real-time platform. This next-generation platform will be capable of reacting within a few milliseconds and providing instant component analysis results directly to the assembly machines.
With this, Cybord will enable a real-time component ‘dump’ in the event of a defective component being detected. This powerful capability will ensure that customers are no longer required to rework their products due to bad components assembled. Preventing the assembly of faulty components will enhance the reliability of future products and reduce the risk exposure for every EMS that adopts our technology.
What industries benefit most from Cybord’s platform? Where do you see the most critical need for visual AI inspection?
The integration of chips into everyday electronic products is becoming increasingly common. Therefore, Cybord’s solution applies to almost all industries. Any company that builds and integrates electronic boards into its products needs such a system.
The platform addresses concerns faced by OEMs and EMSs, and it can be used by various verticals such as automotive manufacturers, datacom companies, defense contractors, and health tech industries. In Industry 4.0, visual AI inspection is no longer a “nice to have” feature but is necessary to serve industries across the board. It is the critical factor in achieving 100% inspection (no sampling is needed) for quality and surgical traceability.
As the market for chips grows, and with it our reliance on these products, how does Cybord’s AI solution enable sustainable business practices?
As an OEM or EMS, ensuring sustainable practices is crucial. We understand that the quality assurance of electronic components is an integral part of achieving such sustainability goals. This is why our platform is designed to thoroughly inspect each component at every production stage, from procurement to assembly and installation.
By leveraging the power of AI, we can detect and eliminate potential errors, defects, and counterfeit components in real time. This, in turn, helps us reduce our e-waste significantly and meet our sustainability goals. Moreover, our efforts to reduce scrap and failure rates simultaneously result in cost savings.
