Reducing design cycle time for semiconductor startups: The path from MVP to commercial viability

The journey from an initial concept to a market-ready product in the semiconductor industry is complex and resource-intensive. For startups and spinoffs particularly, evolving from a Minimum Viable Product (MVP) to the commercially viable Proof of Market (PoM) stage, requires efficient and strategic use of technology and resources.

The critical race to Proof of Market

In the semiconductor industry, the race to PoM is a pivotal phase for startups. Given the industry’s inherent challenges and substantial financial stakes, accelerating the journey from MVP to PoM is essential for success.

  • Market Competition: 20% of startups fail due to competition, as reported by CB Insights. In this competitive landscape, moving rapidly from PoC to PoM is crucial.
  • Cost of Failure: The average startup cost in the semiconductor industry exceeds $250 million, with respins adding approximately $25 million each, highlighting the high financial stakes.
  • Time-to-Market Pressure: Delays in semiconductor production, which often lead to significant revenue losses, are a major concern.
  • Design Complexity: The increasing complexity of SoC designs, with market demands outstripping engineering capabilities, adds to the challenge of timely market entry.

Startups, including those in incubation stages, initially focus on demonstrating their PoC by developing an MVP. Often built using technology not ideal for mass production, the MVP’s role is to showcase the concept to early adopters and investors and to secure access to low-cost foundry services. This stage is vital, but the true test of market viability occurs in the transition to PoM when startups face the significant challenge of transitioning their design to technology suitable for commercial mass production.

Reaching PoM 40% faster with AMALIA

Thalia’s AMALIA software suite enables startups to efficiently migrate their analog and mixed-signal IPs to technologies appropriate for Tier 1 foundries or to develop a second product. Utilizing AMALIA’s unique blend of automation and AI-enhanced tools, startups can dramatically cut their design cycle time and operational costs. The suite comprises:

  • Technology Analyzer: Automates the comparison between starting and target technologies, generating a list of compatible devices for efficient design migration.
  • Circuit Porting: Utilizes the output from Technology Analyzer to produce schematics in the chosen technology, preserving placement and floorplan for enhanced design reliability.
  • Design Enabler: Employs AI and machine learning algorithms for optimizing circuit performance when porting doesn’t meet design constraints.
  • Layout Automation: Maintains the intelligence gathered during silicon verification in the final layout, automating repetitive tasks and conducting design rule checks.

By streamlining the design process and minimizing manual interventions, AMALIA facilitates a 40% faster (minimum) move to PoM compared to other analog design migration approaches.

For semiconductor startups, achieving PoM swiftly is a crucial milestone in their journey to commercial success. With AMALIA’s capability to expedite the design migration process startups can reduce financial exposure and design cycle time while effectively positioning themselves in a competitive market. This expedited transition is not just about reaching the market quickly; it’s about ensuring sustainable growth in a challenging and rapidly evolving technological landscape.

The crucial role of second source management and IP reuse in the semiconductor landscape

The global semiconductor industry is witnessing rapid transformation, fuelled by an ever-evolving technological landscape, recent geopolitical tensions and expected growth in 2024. This has magnified the need for diversification within the supply chain and a robust second sourcing strategy. Semiconductor businesses need to be able to reuse and migrate their IP seamlessly between foundries. By harnessing the power of IP reuse through the AMALIA software suite, businesses can achieve supply chain security, mitigate business risks, and improve efficiency and overall reliability. 

Understanding second source management

Second source management is essentially the strategy of partnering with alternative foundries to ensure timely manufacturing and delivery of Silicon IP and IP-based electronics. Given the competitive and unpredictable nature of the semiconductor industry, having an exclusive reliance on a single supplier can lead to potential delays or supply chain challenges. 

Diversifying partnerships through a strategic second source approach allows businesses to spread their dependencies. This not only helps them minimize and mitigate potential risks but also provides a buffer against unforeseen supply chain interruptions, for example queues at foundries for certain technologies and nodes. Moreover, having multiple suppliers in diverse geographical regions can act as an insurance against geopolitical uncertainties, regional foundry availability and unexpected price increases.  

Importance of process node availability

Choosing the right process node is pivotal, defined by its electrical characteristics, the availability of desired devices, and the cost implications of manufacturing. In the subsequent sections, we’ll look at how the AMALIA software suite helps businesses in making informed decisions upfront when considering analog IP migration. 

The benefits of a second source

  1. Risk mitigation: By employing a second sourcing strategy, businesses ensure that product deliveries don’t rely on a single supplier or a supply chain with higher risk profile. This dilution of dependency significantly reduces associated risks.
  2. Supply chain resilience: Second sourcing instills confidence regarding capacity and priority within foundries. Collaborating with a mix of large and smaller foundries offers more flexibility in production, timely deliveries, and the ability to navigate large-scale deals and geopolitical uncertainties

A proactive approach with AMALIA

Being proactive with sourcing begins by identifying critical IP components essential for product functionality and performance. The next steps are: 

  1. Technology Analysis: AMALIA’s Technology Analyzer tool provides a comprehensive comparison of process technologies based on electrical characteristics. This allows for the identification of devices in the target technology that are electrically comparable and more efficient compared to the source IP technology. 
  2. Circuit Porting: AMALIA’s Circuit Porting software translates existing IP design schematics from the source technology and integrates them into the target technology, ensuring that 60-70% of IP blocks meet the required specifications without any design alterations.
  3. Design Verification & Centering: AMALIA’s Design Enabler assists in quickly adjusting circuits to fulfil specific requirements and constraints and helps achieve optimal PPA even if some blocks necessitate design tweaks or architectural changes.
  4. Automated Layout Generation: AMALIA’s Layout Automation tool facilitates the creation of layouts based on existing designs, ensuring accurate device placement and routing while minimizing changes to the existing floor plan and layout

What sets the AMALIA software suite apart is its comprehensive end-to-end nature, covering the entire design flow process and every stage involved. The cumulative speed enhancements at each step leads to significant time and cost savings of at least 40% overall giving businesses an edge in a fiercely competitive market.  

The semiconductor industry’s current trajectory requires businesses to view second sourcing not just as an option, but as a pivotal strategy. Whether it’s about securing product deliveries, reducing supply chain risks, or navigating uncertain geopolitical influences, a well-structured second source management strategy is indispensable. 

Thalia’s AMALIA software suite is revolutionizing this space, offering a time-efficient, cost-effective solution to analysing multiple potential target technology nodes and migrating critical IP from one technology node to another. Whether you’re interested in individual software tools or the entire suite, AMALIA provides both licensing and commercial SaaS-based solutions to cater to diverse business needs. When preferred, Thalia can also help set up and facilitate the use of the AMALIA software directly on a customer’s own servers and data center, ensuring that the IP remains securely within the customer’s controlled environment. 

AI will be increasingly important in EDA, reducing design costs and supporting engineers

Artificial intelligence (AI) is having one of its periodic days in the sun, and dominates the conversation at almost any industry event. The Design Automation Conference (DAC 2023) was no exception, with AI seen by the semiconductor community as both an opportunity and a challenge. 

An opportunity, of course, because AI requires so many chips, from the huge and complex system-on-chips that will power the AI engines and models, to the semiconductors that will be embedded in every device to bring AI to every application.  

The complexity of the chips fuels demand for a wide variety of IP, but this is where some of the challenges are seen. Integrating many blocks of sophisticated IP to form an AI system-on-chip – which may also integrate yet more functionality such as 5G – is a long process, and it requires very advanced skills. There may be hundreds of IP blocks that need to be tested and integrated, with the results recalibrated every time one of the blocks is changed or enhanced. Identifying the cause of a fault or failure may take many engineer-weeks. 

This is true of other chip applications too, of course, including 5G. Engineers with the required skills are in short supply in many markets, and that shortage is worsened by two factors – the number of AI-focused chip start-ups that are now competing for talent, and the increasingly long design cycle for a complex chip, which will consume a growing number of engineer hours before it is ready. 

At DAC, Alberto Sangiovanni Vincentelli, from the University of California at Berkeley, said in a presentation: “The scarce resource of the future is talent. Everyone and his brother wants to study AI. But we don’t have the people to design the chips to implement that AI.” 

DAC buzzed with discussion about how to address the skills gap in electronic design and manufacturing. Some of the ideas were conventional – making electronic engineering more attractive to young people at school and college level, for instance. But of course, another option is to use AI itself, to help or even replace the engineers. 

Some attendees were positive about this development, claiming AI could reduce the time to develop new chips, by taking on some of the tasks of design assistants.  

Of course, others believe such an approach would eventually threaten jobs altogether, especially if the skills shortage eases in future, and the use of AI also entails disruption to tried-and-tested processes and organizational structures. 

But, at least with the current state of AI technology, replacement of engineers is fanciful. Where AI excels is in rapidly gaining actionable insights from huge quantities of data, such as that generated by EDA tools, and that can support the engineers and make their design and verification tasks quicker and less onerous. 

An example is Thalia’s AMALIA software platform. This is an IP re-use platform for analog and mixed-signal ICs, that allows designers to re-use IP blocks quickly and optimise existing IP for new applications. The suite of tools are designed to free up engineers’ time for complex and high-value tasks by automating key processes. The powerful combination of two of the AMALIA tools, the Technology Analyzer and Circuit Porting, which use unique AI algorithms, can be combined typically resulting in up to 70% of IP blocks needing minimal or no changes before they are re-used. This saves a significant amount of engineering time because every block doesn’t need to be manually checked and verified. 

This example shows how AI is already being incorporated into design automation toolsets in order to boost efficiency and improve commercial outcomes. In other words, AI can be a valuable way to support engineers and reduce the time to produce and test complex chips – including those that will, themselves, enable AI processing and applications in future.