This update brings more than just polish—it’s the foundation for a faster, more fluid design experience, built around the way Copilot is used today and the way we see it evolving tomorrow.
Streamline component research with Flux Copilot. Copilot links to components for quick part research, offering multiple options tailored to your needs, and find part alternatives effortlessly without switching between tabs and platforms.
At Flux, we’re bridging this gap and bringing all the hardware design process under one roof. Today, we’re giving Copilot the ability to link to parts when it makes suggestions, so that your team can easily research components without having to leave the tool.
Just click the link and instantly access everything you need to know: specs, datasheets, real-time pricing, and even usable models—all within Flux.
When your team needs to find a part for your design, Copilot is your best friend. Simply provide it with your needs, criteria, and project-specific restrictions. Then watch as it offers you multiple component options, each meeting your team’s unique needs.
Now that Copilot can link to components within its responses, your team has a new advantage. You can go from part option to part research immediately. Simply click the link on the Copilot-suggested part, and you’ll be brought to that part’s page within Flux. There, you’ll find everything you ever wanted to know about the part, including performance specifications, datasheets, real-time sourcing and pricing, and even usable schematic symbols, footprints, and 3D models.
No more switching tabs and software. The entire component selection process lives in one tool, meaning your team can work quicker and more efficiently.
Say your team already selected a part, but you’re in need of alternative options. Maybe you need to cost-optimize your design with more affordable components. Or, maybe your part is out of stock and lead-times are months long.
Copilot shines here by leveraging its knowledge and Flux’s vast database of components to find your team the best options for your specific project. Copilot can understand your project architecture and the interplay between components within your schematic. With knowledge of that context, Copilot can find the best part alternatives that meet your needs without risking your designs functionality or performance
And, with parts linked directly in Copilot responses, you can go from searching for alternative options to designing with those alternatives in minutes. No searching DigiKey. No creating footprints or models. Follow Copilot’s link, read up on your new part, and drag and drop it directly into your design.
Imagine a world where hardware design is streamlined, intuitive, and collaborative. That's the future we're building at Flux.
Hardware design doesn’t need to be scattered, confusing, and slow. Want to learn more about how your team can 10x efficiency? Sign up for Flux today.
It can be daunting when you and your team are looking to embrace cloud software for the first time. This blog discusses the major principles for your team to better understand the ins and outs of choosing the right cloud-based software.
But, it can be daunting when you and your team are looking to embrace cloud software for the first time. There are seemingly dozens of options out there, each with its own marketing jargon convincing you that their solution is the best. Let’s cut through the fog to help your team better understand the ins and outs of choosing the right cloud-based software. The following are the 8 Principles of Choosing Cloud-Based Software for PCB Design.
First things first, you need to be clear about what is driving your team to adopt cloud-based software.
Why do you need new software now? What’s pushing the urgency? Maybe your CFO demands a 12% margin increase by year-end. Or perhaps you need to ship a backlog of products by Q3. Define this compelling event, document past attempts to solve the problem, and honestly assess what worked, what didn’t, and why.
Pro-tip: Try to solve your problem internally first—sometimes better processes can work wonders without extra costs.
Your team isn’t buying software; you’re buying a solution to a problem. Therefore, any software purchase must be justified with a clear return on investment (ROI).
By understanding the magnitude of your problem in terms of time and money, your team can better determine which cloud solutions make the most sense for you. This process means gathering all the relevant data—FTE costs, current software expenses, opportunity costs—and using it to create a metric of your needs. A good software provider will help you document this and build a solid business case.
If the ROI isn't clear, you might not be ready to buy.
Technical teams often wish they could advance projects at their own pace, but in practice, everything needs to go through corporate approval first. That means that when your team wants to switch to cloud-based software, the move needs executive backing.
Whether you start from the bottom up or the top down, ensuring alignment with strategic goals is necessary. It’s often good practice to leverage the DACI framework (Driver, Approver, Contributor, Informed) to streamline roles and responsibilities and get all major stakeholders on the same page. By clearly defining who is responsible for each aspect of the project, who has the final say, who provides input, and who needs to be kept informed, the DACI framework helps prevent misunderstandings and ensures efficient decision-making.
As a technical team, it is also advisable to keep the executive sponsor involved at key points to maintain momentum and ensure the project’s priority.
We’ve all experienced the pain and red tape associated with requests for proposals (RFPs). What most people won’t admit, however, is that RFPs are often a waste of time.
Your decision-making and needs are complex and nuanced, but RFPs try to reduce complex needs to binary yes/no answers. The result? Mismatches and frustration.
Instead, after following the initial steps in this guide, your list of potential providers should be narrow enough to handle individually and more intimately. It’s far better to talk to 1-3 companies and focus on in-depth discussions rather than going through the cursory checkbox comparisons.
When you’re choosing a new software, you want to know that it has a proven track record of success with other customers. Most software providers will assuage these fears by inundating you with case studies, trials, and product demos. But is all of this collateral really relevant to your team’s needs?
When going through the software courting process, you should ensure that the case studies you request are relevant. For demos, focus on your top concerns rather than generic capabilities. Trials should be approached with clear goals, success criteria, and structured test plans.
Remember, the UI/UX shouldn’t overshadow actual value.
Too often, we get caught up in arguing dollars and cents when the ROI is so much more valuable than the upfront cost. Our advice: don’t haggle for the sake of it.
If the solution offers a clear ROI, like solving a $10M problem for $500K, embrace it. At the end of the day, the upfront cost of your software solution is just a drop in the bucket compared to the return your company will actualize from it.
Instead of haggling, it’s ideal to collaborate with the provider to articulate your problem's size and expect a reasonable investment proposal. High-value solutions shouldn’t feel like a negotiation battle but a mutual agreement. A good transaction will be one in which both sides walk away feeling like winners.
Flux, like other cloud-based EDA tools, offers real-time collaboration, AI integration, seamless updates, and scalability. These are game-changers for moving hardware teams from the 90s into the modern age, matching the pace of their software counterparts.
These advantages collectively streamline the design process, enhance productivity, and enable hardware development to be innovative and agile.
It often feels like the hardware industry is stuck in the past, fearing new technologies like cloud and AI. Understand that these fears are rooted in the industry's slow evolution and long-established practices. Unlike the software industry, which has rapidly adopted and benefited from cloud computing, AI, and other innovations, the hardware sector has traditionally been more cautious, prioritizing stability and reliability over agility and innovation.
Flux and similar tools are designed to help you overcome these fears and leverage modern capabilities to stay competitive.
Adopting these technologies can transform your workflow from rigid and outdated to agile and innovative. By embracing cloud and AI, you can reduce time-to-market, improve product quality, and respond more swiftly to changing market demands. The shift may seem daunting, but the long-term benefits far outweigh the initial challenges.
Buying cloud-based PCB design software doesn't have to be daunting. By following these steps, you’ll make informed, confident decisions. Evaluate your needs honestly, quantify your problems, secure executive alignment, ditch the outdated RFP process, and focus on relevant case studies and structured trials. Embrace the cloud for its real-time collaboration, AI benefits, and scalability.
Want to learn more about how to select the cloud software for your team’s needs? Contact our sales team today.
Discover how Copilot transforms hardware design from concept to creation through an end-to-end example of designing a webcam, showcasing the power of AI hardware design at every step.
This project will be designing the camera circuit for an open-source laptop. We’ll be starting completely from scratch to design a functional webcam around OmniVision’s OV02740 HD image sensor, and we’ll be covering everything from the image sensor itself to the supporting power management and passive circuitry. At the end of this project, you’ll see exactly how AI can reinvigorate your PCB design process from start to finish.
The major AI-powered steps in this project will include
You can check out the projects at your own pace using the links below
The first challenge when starting a new project is taking your idea for a product and deciding on the design architecture. The architectural design phase is foundational in hardware development, sometimes determining the success or failure of a project. It's complex, requiring the balancing of numerous variables and aligning diverse stakeholders on a unified vision.
Creating architectures with Flux Copilot is easy and straightforward. You can simply have a conversation with Copilot about what you intend to build using as much information as you know. Copilot can use your requirements and constraints to explore many different architectural ideas and variations quickly.
In this project, we started by asking:
Copilot then provides us with multiple architectural options, each containing suggestions for circuit blocks, components, and their interconnections. Later, Copilot helps us create block diagrams to better visualize and intuitively understand our chosen architecture. Leveraging AI to rapidly generate and evaluate a wider range of options against your specific product requirements ensures a more effective selection process that leads to optimal outcomes.
To learn more about AI-powered architecture design, read our blog.
Once our architecture is determined, we need to choose the core components that will turn our idea into a real circuit. Fortunately, selecting components is one place where Copilot really shines.
Copilot is guided by your company’s guidelines, including regulatory requirements, pricing, power consumption, operating conditions, and more. With these parameters defined in a Template, Copilot finds the best components that fit your specific project requirements.
In this project, given our architecture, we ask Copilot:
Copilot then provides specific components that are interoperable and achieve the needs of our design in an organized, tabular manner.
Learn more about how AI powers component research.
Designing electronics is about more than just creating a system that works; it also requires creating a system that is sourceable, compliant, and robust against a volatile supply chain. Once we have decided upon our main components, we can use the power of AI to help find alternative component options in case we need backups for whatever reason.
For space savings in our webcam project, we ask Copilot to help us find leadless package alternatives for our components. Specifically, we can ask Copilot
Copilot then provides us with an alternative component that meets our architectural needs and even describes the differences between the original and alternative components.
However, we run into a problem here: the new component doesn’t have a part in the Flux library yet. Fortunately, we can use Copilot to automatically generate a part (i.e., schematic symbol, footprint, and 3D model). Our AI-powered workflow allows you to create parts by importing PDF datasheets directly. Copilot will automatically extract part information, generate components, and enable easy review, validation, and editing—all within the browser.
This AI-powered workflow for handling datasheets and part creation offers an entirely new way to approach the task, replacing the tedious and time-consuming manual part creation process.
Want to learn more about AI-powered part creation? Read our blog.
Thanks to Copilot, we have a schematic and component alternatives for our webcam project. But next, we want to try to make this design as affordable as possible. It’s time to cost-optimize our design.
First, we can save costs by eliminating any unnecessary components in our bill of materials (BoM).
BoM consolidation involves identifying outlier components that can be merged with existing values, reducing the number of unique components needed. For example, a circuit may require a unique resistance value, such as 31.23kΩ, but such a unique value is costly to source. Instead, Copilot can suggest implementing this resistance with two more standard and affordable solutions, such as a 30kΩ and 200Ω in series.
Then, Copilot can help us save costs by evaluating components for over-specification. This ensures that no component exceeds the necessary performance requirements, which can reduce costs. Learn more about AI-driven PCB Cost optimization.
For example, assessing whether a high-performance microcontroller is necessary or if a lower-cost alternative can meet the project’s needs without compromising performance. Copilot can investigate the components in your schematic against the operating conditions of your circuit — be it power, temperature, or frequency — to ensure that no component is unnecessarily over-specified.
Now that we have a highly optimized design, it is time for a design review. Follow this blog to learn more about AI-powered design reviews.
Reviews can be meticulous and tedious, demanding the near-impossible task of considering hundreds of variables and comparing your design against organization-specific standards and constraints. AI like Flux Copilot can automate these menial tasks so that engineers can save time and instead focus their efforts on more high-leverage tasks.
In this project, we ask Copilot first to review the voltage ratings of our passive components. Copilot can handle this task without further instruction, but in this case, we give Copilot detailed steps on how to complete the review so that we’re confident that the review is up to our standards. Copilot then compares every passive component’s ratings against the maximum applied voltage in our circuit and provides this data, as well as a pass/fail status, in an organized table for our review.
Then, we take things one step further by asking Copilot to conduct a precise design review focused on verifying the pin-out and configuration accuracy of the components in our schematic. Copilot gives us insight into its design review process by first explaining the procedure it will follow during its review. With our permission, Copilot then validates the pin-out of our components against the datasheet and provides the results in an organized table.
The final step in our design process is now the testing and debugging phase. Flux uses AI to help you test and debug your circuit designs, including everything from test plan creation to failure mode and effects analysis (FMEA).
In our webcam project, we first asked Copilot to help us develop an automated FMEA report that identifies critical failure modes, assesses their impact, and recommends mitigation actions based on severity, occurrence probability, and detectability. Copilot took to the challenge, generating a detailed tabular report of each component type and explaining the table. With an understanding of the detailed interplay between all the components in your design and expert-level knowledge of electronics fundamentals, Copilot creates a thorough and comprehensive FMEA report for us.
Next, we asked Copilot to help us create a detailed step-by-step plan table for this project to verify its functionality. In our case, Copilot responded with a test plan outlining steps for each major component and functional group. To learn more about AI-powered testing and debugging, read our blog.
With a comprehensive test plan, your team can ensure that any design errors get caught early in the process and well before you go to production. This means your team can spend less time correcting errors and less money on unnecessary design revisions. Ultimately, that translates to higher quality products and faster time to market.
AI has the power to completely transform the hardware and electronics design workflow from start to finish. Through this end-to-end project example, we hope to have demonstrated how AI can assist your team’s design process and how each unique AI-powered workflow converges to create an innovative product. To learn more about how your company can streamline processes, reduce costs, and save time with AI-powered hardware design, sign up for Flux today.
Flux Copilot helps your team tackle the complexities of PCB cost optimization, identifying hidden savings and providing engineers with actionable insights to streamline design processes and reduce costs.
The process of cost optimization in PCB design is notoriously multifaceted and time-consuming. Typically, it involves reviewing hundreds of components for cost and necessity and considering multiple optimization options for each. Each alternative option then needs to be evaluated against a sea of competitive products for technical viability and sourceability.
Needless to say, this process is time-consuming, and the chances of missing potential optimizations are extremely high. Realizing design optimizations without sacrificing functionality and reliability can be overwhelming.
Fortunately, we’ve developed Flux Copilot to be an ideal partner in your cost optimization efforts. By automatically evaluating a vast array of potential optimizations across numerous components simultaneously, Copilot can streamline your cost optimization process with unmatched efficiency. With Copilot at their side, engineers can identify more potential cost-saving measures in less time, even ones that might have been overlooked in a manual review.
Copilot then presents engineers with a comprehensive technical validation for each optimization suggestion. Armed with this data, engineers can make informed choices that balance cost savings with performance and reliability. This synergy between AI and human expertise empowers teams to tackle the cost optimization process more effectively, ensuring that no potential savings are left unexplored.
Here are some real-world examples that Flux users are already benefiting from:
BoM consolidation involves identifying outlier components that can be merged with existing values, reducing the number of unique components needed. For example, a circuit may require a unique resistance value, such as 31.23kΩ, but such a unique value is costly to source. Instead, Copilot can suggest implementing this resistance with two more standard and affordable solutions, such as a 30kΩ and 200Ω in series.
Stackup optimization analyzes project requirements such as temperature, humidity, and industry standards to determine the most cost-effective stackup material. For example, dielectric materials like FR-4 effectively balance performance and cost for consumer electronics applications. Copilot can analyze your project requirements and use them to optimize your stackup for cost efficiency accordingly.
Evaluating components for over-specification ensures that no component exceeds the necessary performance requirements, which can reduce costs. For example, assessing whether a high-performance microcontroller is necessary or if a lower-cost alternative can meet the project’s needs without compromising performance. Copilot can investigate the components in your schematic against the operating conditions of your circuit — be it power, temperature, or frequency — to ensure that no component is unnecessarily over-specified.
Identifying components in leadless packages and suggesting alternatives with leads, such as SOIC or TSSOP, can reduce manufacturing costs and improve assembly ease. This approach simplifies the assembly process and cuts down on production expenses. With Copilot, engineers can determine which components are viable for a simplified package, therefore cutting costs.
Standardizing connectors across the design enables bulk ordering, reduces costs, and simplifies inventory management. Identifying similar connectors and standardizing them helps streamline procurement and inventory processes. Copilot can interpret your design and offer suggestions for a single connector type that meets the needs of all of your board’s interfaces.
Organizing your BoM into groupings of functional blocks can allow your team to more easily compare designs across your product line. This kind of organization enables your team to identify inconsistencies across product BoMs, and then optimize your designs for better standardization and cost savings across the product portfolio. This standardization simplifies the design process and reduces component diversity, ultimately making sourcing easier and more affordable. Copilot can intelligently group your BoM into functional blocks to provide your team with better insight into standardization across your product line.
Cost savings can be achieved by replacing discrete components with integrated modules that combine multiple functionalities, such as Wi-Fi and Bluetooth, in a single module. Copilot can help you evaluate the cost-effectiveness of your current design and suggest integrated alternatives that help optimize your design.
Whether you’re an Electrical Engineer, a Product Manager, or a leader of technical teams, you and your organization will benefit from improving your cost-optimization process. More affordable designs means more affordable products for consumers, less risk for the enterprise, and increased competitive advantage for your brand. By leveraging AI-driven insights, teams can realize all of these benefits in one fell swoop.
Want to learn more about how your team can start using AI to streamline the cost optimization process? Sign up for Flux today.
Integrating AI into hardware development just became easier. Improve your research and planning phase with Flux Copilot—no need to change your existing tools.
The research and planning phase, which can account for up to 90% of project costs, is the perfect stage to introduce AI without major disruptions. By leveraging AI’s ability to digest vast amounts of information and ensure thorough coverage, your team can streamline processes, reduce costs, and lay a robust foundation for successful project outcomes.
Find out how Flux Copilot can optimize this critical phase and improve your hardware development process
The research and planning phase in large-scale hardware projects is crucial for setting a solid foundation for development. This phase involves defining key features, setting technical and business requirements, and aligning all stakeholders on project goals. Engineers and project managers sift through extensive documentation, coordinate with suppliers, and ensure components meet project criteria, making this phase time-consuming and complex.
Hidden costs in this phase are significant. Delays can lead to project overruns, increased costs, and missed market opportunities. Misalignments and last-minute changes often disrupt schedules and escalate costs. Errors made during this phase can result in costly redesigns, delays, and potential product failures.
AI, particularly large language models (LLMs), excels at handling knowledge work efficiently. In the research and planning phase, AI's ability to distill and organize information is invaluable. LLMs can digest, interpret, and synthesize vast amounts of data, helping your team find the best approach for your projects.
Flux Copilot, an advanced multi-modal LLM, integrates seamlessly into your existing hardware design workflows. Regardless of the EDA tools your company uses, Copilot centralizes all relevant data into a comprehensive knowledge graph, including datasheets, requirements documents, and your organization's best practices.
Understanding your project's context—such as the Bill of Materials (BOM), netlist connections, and specific requirements—Copilot automates routine tasks. It can read and interpret datasheets, suggest components, and generate initial architectural designs, allowing engineers to focus on strategic and creative work.
With Flux Copilot, you can efficiently capture and utilize requirements throughout the design process.
You can start by directly telling Copilot your project requirements, which can be captured as properties. These properties provide Copilot with the necessary context to assist you effectively, covering technical specifications, design constraints, performance metrics, and other essential parameters.
Additionally, you can feed Copilot your product meeting notes and other information sources. Copilot will analyze this information to create a complete set of requirements, ensuring that nothing is missed and all stakeholders are aligned. By centralizing requirements, Copilot helps prevent miscommunication and ensures smooth collaboration.
Traditional architectural design processes rely on familiar templates and past experiences, which can lead to missed opportunities for optimization. Copilot changes this narrative by empowering teams to explore a broader range of architectural variations.
By leveraging AI to generate and evaluate different design options automatically, Copilot enables teams to iterate and assess multiple designs in minutes. Then, with a breadth of options to choose between, Copilot helps teams identify the most optimal architecture for their project, leading to improved performance, reduced costs, and faster development times. AI-driven architectural design ensures that all potential configurations are considered, leading to better-informed decisions.
Read our blog to learn more about how Copilot assists in the architectural design process.
AI can revolutionize the architecture design review process by automating the tedious and time-consuming aspects of reviewing architectural plans against system descriptions. Copilot can be seamlessly integrated into your project, providing comprehensive insights into your architectural designs, including material specifications and structural interconnections. By aligning Copilot with your design goals and organizational best practices it ensures compliance with industry standards and your organization’s design constraints.
For example, Copilot can scrutinize material specifications and structural configurations, highlighting areas that require corrections or improvements. It can automatically verify design goals such as sustainability, cost efficiency, and safety conditions. By automating these checks, AI allows architects and engineers to focus on more critical, high-level tasks, thereby enhancing the overall efficiency and accuracy of the design process.
This accelerates the design review process and ensures that architectural designs are robust, reliable, and ready for implementation. Integrating AI into the design review workflow ultimately leads to faster, more efficient development cycles and higher-quality architectural designs.
One of the most time-consuming tasks in hardware development is researching and selecting the right components. Copilot streamlines this process by using AI to analyze datasheets and suggest components that meet your project's specific requirements.
By leveraging AI, engineers can quickly evaluate dozens of components and alternatives to guarantee that the final selection aligns with technical specifications and project constraints. Compared to manual component research and selection, AI-powered research reduces the risk of errors and the associated time requirements.
Read our blog to learn more about how Copilot can streamline the component research process.
Creating high-quality parts from datasheets is an integral part of the design process, but its manual nature makes it tedious and time-consuming. Copilot automates this process by generating accurate and consistent parts quickly. Simply upload the PDF of a datasheet to Copilot, and it will create a schematic symbol, footprint, and 3D model for your use.
Compared to creating parts by hand, this automation speeds up the development process and ensures that parts are created to a high standard. Where most PCB layout errors result from incorrect component footprints, AI-generated parts reduce the risk of errors and inconsistencies. And the ability to quickly generate parts from datasheets allows teams to focus on more strategic aspects of their projects.
Read our blog to learn more about how Copilot can create parts from datasheets in seconds.
Optimizing the research and planning phase in hardware development is crucial for ensuring project success. Flux Copilot addresses the inefficiencies in this phase by centralizing data, facilitating collaboration, and automating routine tasks. With these features, Copilot can increase your team's efficiency by up to 10x, all within the confines of your existing EDA tools and workflow.
Ready to revolutionize your hardware development process with Flux Copilot? Be among the first 10 customers to benefit from our preferred partner pricing and gain access to our development team for personalized support. Sign up for Flux today to learn more and start your journey toward a more efficient and innovative hardware development process.
Copilot bridges the firmware<>hardware gap by providing firmware engineers with direct access to hardware information like netlists and pins, streamlining firmware development and reducing delays.
Firmware engineers often need to extract information directly from schematics or communicate with hardware designers to understand the hardware. This correspondence frequently occurs off-tool and is prone to miscommunications, leading to errors and inefficiencies.
Flux Copilot bridges this gap by providing firmware engineers with direct access to hardware information like netlists and pins, streamlining firmware development and reducing delays.
One significant hurdle is interpreting detailed PCB schematics to understand hardware connections and configurations. Firmware engineers are not necessarily experts in hardware, and, more often than not, they are not the ones who designed the hardware they work with. Writing the best firmware possible requires a deep understanding of the underlying electronics, but this process is often time-consuming and prone to errors for the firmware engineer.
Firmware engineers need efficient ways to verify their code against schematic designs, especially when hardware isn’t ready during the initial development phase. The development process becomes more complicated and error-prone without automated tools for testing initialization scripts, configuration files, and test plans.
AI bridges the gap between firmware and hardware by helping firmware engineers understand the target hardware and freeing hardware engineers to focus on design. By leveraging PCB schematic data, AI provides firmware engineers with detailed insights into the hardware implementation. This support allows them to develop, test, and optimize firmware more efficiently. Meanwhile, hardware engineers can concentrate on their core tasks without being bogged down by constant queries and support requests from firmware developers.
Copilot provides a range of powerful applications that significantly enhance the firmware development process. Here are some of the most impactful ways AI can streamline and improve firmware engineering:
Automatically generate mappings of microcontroller pins to connected components, saving time and minimizing setup errors.
Review GPIO assignments and peripheral setups to simplify firmware development with schematic-based guidance.
Provide configuration files and initialization scripts based on netlist data, enhancing resource management and firmware performance.
Generate comprehensive code documentation that includes hardware connections, initialization procedures, and configuration settings, improving code maintainability and team collaboration.
AI-Firmware represents a significant leap in firmware development efficiency. Increased collaboration between engineers creates more efficient workflows, automates tedious tasks, and allows engineers to focus on complex problem-solving and innovation. The benefits of this new feature are clear:
With AI-Firmware, Flux continues to push the boundaries of what’s possible in hardware design and firmware development. AI-Firmware breaks down the barriers between hardware designers and firmware engineers so that your team can bring products to market at a lower budget and in less time.
Want to explore how AI-Firmware can transform your workflow? Sign up for Flux today.
