The year 2023 may well be remembered as a watershed moment for the integration of artificial intelligence (AI) and machine learning into various industries. Regardless of your professional field, it’s likely that machine learning has made significant headlines. From everyday consumers interacting with AI like ChatGPT, which saw a million users in its first five days, to the burgeoning relationship between machine learning and the Internet of Things (IoT), the impact is undeniable. The advent of ultra-low-power microcontrollers and compact tinyML frameworks has unlocked a world of possibilities for intelligent devices at the edge.
But how does this translate to the electronics industry, particularly when it comes to the selection of components for new designs? A recent survey by electronics distributor Farnell on its element14 community site provides some answers. The survey revealed that 86% of the 528 respondents, or 454 individuals, are open to AI playing a role in their component-selection process. Moreover, 23% said they would fully trust AI to select components.
Cliff Ortmeyer, Farnell’s global head of technical marketing, noted that the survey results hint at a growing acceptance of AI in component selection, particularly where safety and innovation are key considerations. He added, “As AI models become more advanced, they will undoubtedly become more instrumental in modeling designs, selecting components, and accelerating design cycles, thereby reducing time to market for new products.”
However, the survey also highlighted some concerns about potential biases, both intentional and unintentional, in AI’s involvement. One respondent in favor of AI’s role in component selection said it’s only a matter of time before people find ways to use online netlists and schematic PDFs as valuable AI training data. The respondent further noted that AI could enhance these resources once there are improved digital representations of devices and their internal components, coupled with telemetry from all subsystems.
Circuit Mind, a UK-based startup founded in 2018, aims to liberate engineers from the laborious task of sifting through datasheets and help engineering teams save time and resources. The company has developed an electronic design assistant, ACE, which consists of two parts: Commodore and ACE. Commodore is a database of electronic component digital twins, converting datasheet information into a machine-readable model of the component. ACE, on the other hand, is a set of deterministic mathematical algorithms developed by the company’s internal electronics engineers and algorithm engineers.
Tomide Adesanmi, co-founder and CEO of Circuit Mind, explained that ACE handles the design process, with its first module focusing on the digital aspects of a design. The subsequent design modules focus on power and analog. The value proposition for the digital module is that engineers shouldn’t have to spend time researching components, reading datasheets, understanding pin assignments, or determining whether a component has the necessary registers. Instead, they should focus on areas that require their expertise, like the analog aspects of the circuit.
Adesanmi emphasized that the platform lets engineers maintain control over their designs. They can specify their requirements and generate a design in just 10 minutes. They can also quickly develop multiple designs and compare them against each other. The platform creates schematic files, project files, and bill-of-materials listings with pricing data sourced from leading electronic distributors.
In conclusion, the growing trust in AI for the component-selection process is a promising development for the electronics industry. As AI models continue to advance, they will likely become increasingly valuable tools for modeling designs, selecting components, and reducing time to market for new products. However, it’s crucial to address potential concerns about biases and ensure that engineers maintain control over their designs. With careful management, AI can be a powerful ally in the electronics industry.
