R&D constitutes 15-20% of the sales in semiconductor companies. Top 10 semiconductor companies spent in excess of $30b in R&D in 2016. Not only is that a significant cost, it is a critical source of competitive advantage.
A vicious cycle: R&D complexity and speed to market put pressure on engineering productivity
Technological advancements are causing a huge escalation in the complexity of R&D projects. According to Gartner, on average IC design costs are have risen from $30 million for 28nm, to $80 million for 14nm, and will go up to $120 million for 10nm. R&D complexity escalation is transforming business models in semiconductor industry. It is one of the major drivers of recent consolidation in the industry. Even though horizontal business models help reuse of IP blocks, SoCs mean a lot more goes into market leading chips. And as the traditional vertically integrated models have being broken by fab-lite companies, the importance of competing on R&D has never been felt more.
With such ever-escalating costs of R&D, to justify such investments, there is even more pressure on semiconductor companies to attain higher market share in the markets they focus on. Technology industry, much like sports and entertainment market is turning into what economist call ‘winner-take-all’. Market leaders get disproportionate share of the economic profits, and slightest delays for others can have them reeling in massive losses. These dynamics in turn has amplified pressure on R&D organizations to launch products faster.
This vicious cycle has put productivity of R&D projects under spotlight. Semiconductor companies need to make sure that they have a robust mechanism for tracking their R&D productivity. The R&D productivity is however is not simple to estimate and track. Time and effort in R&D are not simple functions of transistor count. Extra time and effort can be justified by the type of circuit, density of circuit, the extent of reuse etc. These factors need to be normalized to find the actual causes of varying productivity. Most often such normalization would require certain degree of subjectivity. It is very useful nevertheless to be able to compare normalized productivity of the projects.
Identifying the right lever for productivity improvement:
Once the data has been normalized, it opens up opportunities to identifying the right lever(s) that can help improve productivity of individual projects, and R&D organizations overall. The are two broad categories of levers that can then be explored and applied to to improve the R&D productivity:
Comparing variance between projects: By comparing variance between their own projects, semiconductor companies can help identify best practices being adopted by one project that can be applied to others. There are a number of differences that could be studied, for example,
- Time and effort variance between various phases of the project, e.g. functional specifications, RTL design, physical design, testing, validation etc. Furthermore, there are sub-steps e.g. design review etc, that can also be compared to identify improvement opportunities across projects.
- Number of sites used by design teams and configuration of the design teams can have a significant impact on the project productivity. For example, by increasing the number of sites, it becomes easier it is to scale teams and source talent, but it also adds to complexity in coordination. Comparing productivity of projects with different configurations can help identify the ones that best work for the company.
- Differences in non-standard/mandated tools, e.g project management tools used between different projects. Project teams teams may use different tools to collaborate with colleagues, track progress, manage code repositories, and plan validations etc. These tools can have significant impact on productivity. Differences in usage of tools across projects could unearth significant improvement opportunities.
- Tradeoffs applied by managers in their projects: There are a number of tradeoffs project teams make e.g., between reuse vs design from scratch, higher defect density vs extent of validation in their first release etc. These tradeoffs can vary widely by projects, and comparing projects can help identify a number of opportunities.
Identifying systemic opportunities for productivity improvement: These are opportunities that may exist across the organization, irrespective of the variances across projects. Companies could start by focusing on activities that consume most time, or most effort on average and identify the improvement opportunities. Furthermore, they also need to look at the activities that may have most downstream impact to zoom into focus areas for systemic productivity improvements. There are a number of organizational and process decisions that can help companies improve productivity of the R&D organization. For example:
- Engineering skill level and competence of the organization
- Choice of EDA tools used in the organization
- Main design flows the company adopts in their R&D organization
- The extent of management support and attention given to the R&D teams
- The type and depth of relationship with customers, etc.
Need for improving R&D productivity in semiconductor companies has never been higher. A robust data driven approach to R&D productivity improvements is not just about saving costs. It can make a huge difference in companies’ ability to compete effectively in a competitive landscape that is intensely competitive, where technological advancement is unrelenting and customers are ever more demanding.
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