C is for Cycle Time [Part 2]
Part 2
In the first part of 'C for Cycle Time', we explored the essence of cycle time in front-end wafer fabs and its significance for semiconductor companies. We introduced the operating curve, which illustrates the relationship between fab cycle time and factory utilization, as well as the power of predictability and the ripple effects cycle time can have across the supply chain.
In part 2, we will explore strategies to enhance cycle time through advanced scheduling solutions, contrasting them with traditional methods. We will use the operating curve, this time to demonstrate how advanced scheduling and operational factors, such as product mix and factory load, can significantly impact fab cycle time.
How wafer fabs can improve cycle time
By embracing the principles of traditional Lean Manufacturing, essentially focused on reducing waste in production, cycle time can be effectively reduced [1]. Here are a few strategies that can help improve fab cycle time:
- Improving maintenance strategies, for example moving from reactive to more proactive maintenance can improve cycle time with fewer breakdowns and more predictable tool availability [2].
- As noted in part 1, minimizing wasted time in batch formation and reducing the frequency of rework due to defects improves cycle time.
- Purchasing faster tools. Although, this can be a time-consuming and costly undertaking. In-facility expansion may take up to a year, while the commencement of a new facility could extend to three years [3].
- Establishing optimal batching in diffusion poses a considerable challenge, given the intricate process constraints within the diffusion area, such as timelinks, as we’ve explained in a recent blog.
- Balancing cycle time of hot lots with average fab cycle time. Fabs often assign higher priority to hot lots, which can negatively impact the average cycle time of production lots [4].
- Developing the skills of existing operators and expediting the onboarding process for new operators could be another means of reducing variability in production, thus impacting cycle time.
The implementation of an advanced AI scheduler can facilitate most of the strategies noted above, leading to an improvement in cycle time with significantly less effort demanded from a wafer fab compared to alternatives such as acquiring new tools. In the next sections we are going to see how this technology can make your existing tools move wafers faster without changing any hardware!
Applying an advanced AI scheduler to improve cycle time
In this section, we delve into how an advanced AI scheduler (AI Scheduler) can maintain factory utilization while reducing cycle time.
First let’s define what an AI Scheduler is. It is an essential fab software that has a core engine powered by AI models such as mathematical optimization. It possesses the ability to adapt to ongoing real-time changes in fab conditions, including variations in product mixes, tool downtimes, and processing times. Its output decisions can achieve superior fab objectives, such as improved cycle time, surpassing the capabilities of heuristic-based legacy scheduling systems. More aspects of an advanced AI scheduler can be found in our previous article, A is for AI. The AI Scheduler optimally schedules fab production in alignment with lean manufacturing principles. It achieves this by optimally sequencing lots and strategically batching and assigning them to tools.
Figure 5 shows an example of how an AI Scheduler can successfully shift the cycle time from the original operating curve closer to the theoretical operating curve. As a result, cycle time is now 30 days at 60% factory utilization. This can be accomplished by enhancing fab efficiency through measures such as minimizing idle times, reducing re-work, and mitigating variability in operations, among other strategies. In the next sections, we will show two examples in metrology and diffusion how cycle time is improved with optimal scheduling.
Reducing queuing times and tool utilization variability in metrology
Many wafer fabs employ a tool pull-system for dispatching. In this approach, operators typically decide which idle tool to attend to, either based on their experience or at times, randomly. Once at the tool, they then select the highest priority lots from those available for processing. A drawback of this system is that operators don't have a comprehensive view of the compatibility between the lots awaiting processing, those in transit to the rack, and the tools available. This limited perspective can lead to longer queuing times and underutilized tools, evident in Figure 6.
An AI Scheduler addresses these inefficiencies. By offering an optimized workflow, it not only shortens the total cycle time but also minimizes variability in tool utilization. This in turn indirectly improves the cycle time of the toolset and overall fab efficiency. For example, Seagate deployed an AI Scheduler to photolithography and metrology bottleneck toolsets that were impacting cycle time. The scheduler reduced queue time by 4.3% and improved throughput by 9.4% at the photolithography toolset [5]. In the metrology toolset, the AI Scheduler reduced variability in tool utilization by 75% which resulted in reduced cycle time too, see Figure 7 [6].
Improving cycle time and optimal batching in diffusion
Diffusion is a toolset that poses operational complexities due to its intricate batching options and several coupled process steps between cleaning and various furnace operations [7]. Implementing an AI Scheduler can mitigate many of these challenges, leading to reduced cycle time:
- Strategic Batching can reduce total cycle time in diffusion. To maximize the benefit of an AI Scheduler, the fab should provide good quality data.
- Automated Furnace Loading: Typically, diffusion loading is accomplished via a pull-system from the furnace. This means that operators would revisit the cleaning area to manually pick the best batches, based on upcoming furnace availability. This approach often demands substantial resources and time, thereby increasing cost or cycle time. The AI Scheduler curtails this time considerably, freeing up operators for other essential tasks, which indirectly may reduce cycle time elsewhere.
- Reduction of Timelinks Violations: A recent pilot implementation of an AI Scheduler in diffusion at a Renesas fab underscored its effectiveness. As displayed in Figure 8, timelink violations were significantly reduced. This minimizes the necessity for rework, further cutting down the cycle time, as explained earlier in the article.
Maximizing the value of the AI Scheduler by integrating with other applications
In the above examples of photo, metrology and diffusion toolsets, the AI Scheduler can support operators to achieve consistently high performance. To enhance the efficiency of the scheduling system in fabs predominantly run by operators with minimal AMHS (Automated Material Handling Systems) presence, pairing the scheduler with an operator guidance application, as detailed in one of our recent blogs on user-focused digitalisation, can be a valuable approach. This software will suggest the next task required to be executed by an operator.
The deployment of an AI Scheduler should focus on bottleneck toolsets - specifically, those that determine the fab's cycle time. Reducing the cycle time of a toolset will be inconsequential if that toolset is not a bottleneck. Consequently, fabs should consider the following two approaches:
- Ensure the deployment of the AI Scheduler on the most critical toolsets to effectively address dynamic bottlenecks. This ensures that as bottlenecks shift, the AI Scheduler can promptly reduce the cycle time of the newly identified bottlenecked toolset. By doing so, fabs can consistently maintain a low cycle time.
- The introduction of a global (or a fab wide) application layer – such as a solution that looks across all the toolsets and all lots across the whole line – can help coordinate all deployed AI Schedulers. This application should indicate which toolsets are bottlenecks and it should also adjust lot priorities or production targets per toolset to ensure a smooth flow across the line. The interaction between global applications and local scheduling applications can be seen in recent papers [9] [10].
Dealing with dynamic changes in the fab and understand trade-offs between competing objectives
Another factor to consider is that the actual operating curve of the fab is moving constantly based on changes in the operating conditions of the fab. For example, if the product mix changes substantially, this may impact the recipe distribution enabled in each tool and subsequently, the fab cycle time vs factory utilization curve would shift. The operating curve can also change if the fab layout changes, for example when new tools are added.
In Figure 9, we show an example wherein the cycle time versus factory utilization curve for product mix A shifts upward. This signifies an increased cycle time in the fab due to the recent changes in the product mix (and the factory utilization was slightly reduced under these new conditions). An autonomous AI Scheduler, as described by Sebastian Steele in a recent blog, should be able to understand the different trade-offs. For example, in Figure 10, the AI Scheduler could deal with the same utilization as before (60%) with product mix A, but the cycle time will stay at 50 days (10 days more than in the case with product mix A). Another alternative is that the user can then decide if they want to customize this trade-off so that the fab can move back to the same cycle time with this new product mix B at 40 days but staying with lower utilization at 57%.
Trade-offs between different objectives at local toolsets may impact the fab cycle time. Consider the trade-offs in terms of batching costs versus cycle time. For instance, constructing larger batches might be crucial for high-cost operational tools such as furnaces in diffusion and implant. However, this approach could lead to an extended cycle time for the specific toolset and, consequently, an overall increase in fab cycle time.
Tool availability and efficiency significantly affect cycle time, akin to the influence of product mix on operating curves. If tools experience reduced reliability over time, the operating curve may shift upward, resulting in a worse cycle time for the same utilization. While the scheduler cannot directly control tool availability, strategically scheduling maintenance and integrating it with lot scheduling can positively impact cycle time. A dedicated future article will delve into this topic in more detail.
Conclusion
The topic of the cycle time has been enriched with the introduction of an AI Scheduler, bringing a paradigm shift in how we perceive and manage the dynamics of front-end wafer fabs. As highlighted in our exploration, these schedulers do more than just automate – they optimize. By understanding and predicting the nuances of operations, from tool utilization to lot prioritization, advanced AI schedulers provide a roadmap to not just manage but optimize cycle time considering alternative trade-offs. In future articles we will talk about how scheduling maintenance and other operational aspects can be considered in a unified and autonomous AI platform that we believe would be the next revolution, after the innovations from Arsenal of Venice, Ford and Toyota.
Author: Dennis Xenos, CTO and Cofounder, Flexciton
References
- [1] James P. Ignizio, 2009, Optimizing Factory Performance: Cost-Effective Ways to Achieve Significant and Sustainable Improvement 1st Edition, McGraw-Hill, ISBN 978-0-07-163285-0
- [2] Lean Production, 2023, TPM (Total Productive Maintenance), URL.
- [3] Ondrej Burkacky, Marc de Jong, and Julia Dragon, 2022, Strategies to lead in the semiconductor world, McKinsey Article, URL.
- [4] Philipp Neuner, Stefan Haeussler, Julian Fodor, and Gregor Blossey, 2023, Putting a Price Tag on Hot Lots and Expediting in Semiconductor Manufacturing. In Proceedings of the Winter Simulation Conference (WSC '22). IEEE Press, 3338–3348.
- [5] Robert Moss, Dennis Xenos, Tina O’Donnell, 2023, Deployment of an Advanced Photolithography Scheduler at Seagate Technology, IFORS News, Volume 18, Issue 1, ISSN 2223-4373, pp. 8–10, URL.
- [6] Robert Moss, 2022, Ever-decreasing circles: how iterative modelling led to better performance at Seagate Technologies. Euro 2022 Conference, Finland, URL
- [7] Thomas Beeg, 2023, Impact of “time links” or controlled queue times, Factory Physics and Automation, URL.
- [8] Jamie Potter, 2023, Fab scheduling is now so complex that it needs next-generation intelligent software, Silicon Semiconductor Magazine, Volume 44, Issue 2, pp. 26-29, URL.
- [9] I. Konstantelos et al., 2022, "Fab-Wide Scheduling of Semiconductor Plants: A Large-Scale Industrial Deployment Case Study," 2022 Winter Simulation Conference (WSC), Singapore, pp. 3297-3308, doi: 10.1109/WSC57314.2022.10015364.
- [10] Félicien Barhebwa-Mushamuka. 2020, Novel optimization approaches for global fab scheduling in semiconductor manufacturing. Other. Université de Lyon. English. ⟨NNT : 2020LYSEM020⟩. ⟨tel-03358300⟩
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Staying ahead in smart manufacturing technologies has become paramount for global competitiveness. This topic was the focal point of the recent panel discussion webinar hosted by Flexciton.
The semiconductor industry's journey toward fully autonomous manufacturing is underway, driven by advanced technologies and strategic investment. Staying ahead in smart manufacturing technologies has become paramount for global competitiveness. This topic was the focal point of the recent panel discussion webinar, hosted by Jamie Potter, Flexction CEO & Cofounder. The panel featured industry leaders representing fabs and suppliers: Matthew Johnson, VP of Wafer Fab Operations at Seagate; Patrick Sorenson, Industrial Engineer at Microchip Technology; Francisco Lobo, CEO of Critical Manufacturing; and Madhav Kidambi, Technical Marketing Director at Applied Materials.
Survey Insights: Where Are We Now?
The panel discussion was initiated with a presentation of the findings from Flexciton's inaugural Front End Manufacturing Insights survey, conducted among fabs in the US, Europe, and Asia. Key takeaways included:
- A majority of respondents see autonomous manufacturing as achievable within the next decade.
- Data standardization and integration remain major barriers, delaying scalable solutions.
- Cloud computing, IoT and Mathematical Optimization stand as the top three advanced technologies that fabs have adopted so far.
These insights laid a strong foundation for a lively discussion, highlighting the shared vision while addressing divergent strategies and challenges.
Insights from Industry Experts
Pragmatism Over Perfection in Data Models
Francisco Lobo emphasized the importance of starting with what’s available when building scalable solutions.
“Instead of building a complete model from scratch, leverage existing standards and your MES infrastructure. Begin with a pragmatic approach and evolve as you learn.”
This iterative strategy ensures companies can start deriving value early, without waiting years for a perfect model to be developed.
Strategic Investments In Downturns
While many fabs postpone investments during downcycles, Matthew Johnson emphasizes that smart manufacturing investments should be continuous rather than cyclical. He highlighted the strategic advantage of such approach:
“In down cycles, you often need these solutions the most. For example, using smart manufacturing to scale metrology tools through sampling can significantly stretch your existing resources without capital-heavy investments.”
His insight underscores how downturns provide a window to refine processes for long-term gains.
Getting Leadership Buy-in
Securing leadership support for smart manufacturing investments remains challenging when benefits aren't immediately apparent. Patrick Sorenson shares that the ROI justification was easier during the recent upcycle:
"If we just get a few more lots out of the fab when we have more demand than capacity, that will pay for itself."
In other scenarios, focus on demonstrating benefits through yield improvements, capital avoidance, or labor efficiency.
Industry Alignment on the Vision
Madhav Kidambi observed a growing consensus around the end goal of autonomous manufacturing, even as companies differ in their pathways:
“The vision of Lights Out manufacturing is clear, but strategies are evolving as companies learn how to justify and sequence investments to sustain the journey.”
Ecosystem Collaboration and The Path Towards Autonomy
A key theme emerging from the discussion is the importance of collaboration between suppliers and fabs. This includes:
- Open platforms and integration capabilities
- Standardized data protocols
- Partner ecosystems for specialized solutions
- Shared innovation initiatives
As the industry progresses toward autonomous manufacturing, success will depend on:
- Maintaining continuous investment in smart technologies
- Taking pragmatic approaches to data integration
- Developing clear ROI frameworks
- Fostering collaboration across the ecosystem
- Building upon existing systems and standards
As Matt from Seagate concludes,
"Fab operation is really a journey of continuous improvement, and the pursuit of smart technologies is a fundamental tenet of our strategy to ensure that we meet the objectives as an organization."
Watch the Full Webinar
The conversation is packed with actionable insights on overcoming barriers, achieving quick wins, and navigating the complexities of smart manufacturing adoption. Don’t miss out—click here to watch the full discussion recording.
Innovate UK, part of UK Research and Innovation, has invested in Flexciton and Seagate Technology's production planning project to help improve UK semiconductor manufacturing.
London, UK – 1 Oct – Flexciton, a UK-based software company at the forefront of autonomous semiconductor manufacturing solutions, is excited to announce investment from Innovate UK in a strategic collaboration with Seagate Technology’s Northern Ireland facility. Innovate UK, the UK’s innovation agency, drives productivity and economic growth by supporting businesses to develop and realize the potential of new ideas. As part of their £11.5 million investment across 16 pioneering projects, this collaboration will help develop and demonstrate cutting-edge technology to boost semiconductor manufacturing efficiency and enhance the UK’s role in the global semiconductor supply chain.
Jamie Potter, CEO and Cofounder of Flexciton, commented:
"We are thrilled to partner with Seagate Technology to bring yet another Flexciton innovation to market. By combining our autonomous scheduling system with Flex Planner, we are enhancing productivity in semiconductor wafer facilities and driving greater adoption of autonomous manufacturing."
The partnership aligns directly with the UK government’s National Semiconductor Strategy, which seeks to secure the UK’s position as a key player in the global semiconductor industry. Flexciton’s contribution to this strategy is not just a testament to its cutting-edge technology but also highlights the company’s role in reinforcing supply chain resilience and scaling up manufacturing capabilities within the UK.
Flex Planner: A breakthrough solution for chip manufacturing
At the heart of this project is Flex Planner, the first closed-loop production planning solution for semiconductor manufacturing with the ability to control the flow of WIP in a fab over the next 2-4 weeks, autonomously avoiding dynamic bottlenecks, reducing cycle times, and improving on-time delivery performance.
Supporting the UK's semiconductor growth
The UK government’s investment in semiconductor innovation underlines its commitment to fostering cutting-edge solutions that bolster the sector’s growth. The semiconductor industry is projected to grow from £10 billion to £17 billion by 2030, with initiatives like this collaboration driving the innovation necessary to achieve these goals.
Flexciton’s partnership with Seagate exemplifies how collaboration between technology innovators and manufacturers can lead to transformative advances in the industry. The funding from Innovate UK enables both companies to develop and test solutions that not only enhance productivity but also position the UK as a critical link in the global semiconductor ecosystem.
About Flexciton
Flexciton is pioneering autonomous technology for production scheduling and planning in semiconductor manufacturing. Leveraging advanced AI and optimization technology, we tackle the increasing complexity of chipmaking processes. By simplifying and streamlining wafer fabrication with our next-generation solutions, we enable semiconductor fabs to significantly enhance efficiency, boost productivity, and reduce costs. Empowering manufacturers with unmatched precision and agility, Flexciton is revolutionizing wafer fabrication to meet the demands of modern semiconductor production.
For media inquiries, please contact: media@flexciton.com
The semiconductor industry is set to receive $1tn in investment over the next six years, driven by AI and advanced technologies, with over 100 new wafer fabs expected. However, labor shortages continue to pose a challenge, pushing the need for autonomous wafer fabs to ensure continued growth.
Over the next 6 years, the semiconductor industry is set to receive around $1tn in investment. The opportunities for growth – driven by the rapid rise of AI, autonomous and electric vehicles, and high-performance computing – are enormous. To support this anticipated growth, over 100 new wafer fabs are expected to emerge worldwide in the coming years (Ajit Manocha, SEMI 2024).
However, a significant challenge looms: labor. In the US, one-third of semiconductor workers are now aged 55 or older. Younger generations are increasingly drawn to giants like Google, Apple and Meta for their exciting technological innovation and brand prestige, making it difficult for semiconductor employers to compete. In recent years, the likelihood of employees leaving their jobs in the semiconductor sector has risen by 13% (McKinsey, 2024).
To operate these new fabs effectively, the industry must find a solution. The Autonomous Wafer Fab, a self-optimizing facility with minimal human intervention and seamless production, is looking increasingly likely to be the solution chipmakers need. This vision, long held by the industry, now needs to be accelerated due to current labor pressures.
Thankfully, rapid advancements in artificial intelligence (AI) and Internet of Things (IoT) mean that the Autonomous Wafer Fab is no longer a distant dream but an attainable goal. In this blog, we will explore what an Autonomous Wafer Fab will look like, how we can achieve this milestone, the expected outcomes, and the timeline for reaching this transformative state.
What will an Autonomous Wafer Fab look like?
Imagine a wafer fab where the entire production process is seamlessly interconnected and self-regulating, free to make decisions on its own. In this autonomous environment, advanced algorithms, IoT, AI and optimization technologies work in harmony to optimize every aspect of the manufacturing process. From daily manufacturing decisions to product quality control and fault prediction, every step is meticulously coordinated without the need for human intervention.
Key features of an Autonomous Wafer Fab:
Intelligent Scheduling and Planning: The heart of the autonomous fab lies in its scheduling and planning capabilities. By leveraging advancements such as Autonomous Scheduling Technology (AST), the fab has the power to exhaustively evaluate billions of potential scenarios and guarantee the optimal course for production. This ensures that all constraints and variables are considered, leading to superior outcomes in terms of throughput, cycle time, and on-time delivery.
Real-Time Adaptability: An autonomous fab is equipped with sensors and IoT devices that continuously monitor the production environment. These devices can feed real-time data into the scheduling system, allowing it to dynamically adjust schedules and production plans in response to any changes or disruptions.
Digital Twin: Digital Twin technology mirrors real-time operations through storing masses of data from sensors and IoT devices. This standardized data schema allows for rapid introduction of new technologies and better scalability. Moreover, by simulating production processes, it helps to model possible scenarios – such as KPI adjustments – within the specific constraints of the fab.
Predictive maintenance: Predictive maintenance systems will anticipate equipment failures before they occur, reducing downtime and extending the lifespan of critical machinery. This proactive approach ensures that the fab operates at peak efficiency with minimal interruptions. Robotics will carry out the physical maintenance tasks identified by these systems, and when human intervention is necessary, remote maintenance capabilities will allow technicians to diagnose and address issues without being on-site.
The Control Room: In an autonomous fab, decision-making is driven by data and algorithms. The interconnected system can balance trade-offs between competing objectives, such as maximizing throughput while minimizing cycle time, with unparalleled precision. That said, critical decisions such as overall fab objectives may still be left to humans in the “control room”, who could be on the fab site or 9000 km away…
How can we get there?
Achieving the vision of an Autonomous Wafer Fab requires a multi-faceted approach that integrates technological innovation, strategic investments, and a cultural shift towards embracing automation. Here are the key steps to pave the way:
A Robust Roadmap: All fabs within an organization need to have a common vision. Key milestones need to be laid out to help navigate each fab through the transition with clear actions at each stage. SEMI’s smart manufacturing roadmap offers an insight into what this could look like.
Investing in Novel Technologies: The pivotal step towards autonomy is investing in the latest technologies, including AI, machine learning, AST, and IoT. These technologies form the backbone of the autonomous fab, enabling intelligent planning and scheduling, real-time monitoring, and adaptive control.
Data Integration and Analytics: A crucial aspect of autonomy is the seamless integration of data from various sources within the fab. By harnessing big data analytics, fabs can not only gain deep insights into their operations, but they will have the correct data in place to support autonomous systems further down the line.
Developing Skilled Workforce: While the goal is to minimize human intervention, the semiconductor industry will still require skilled professionals who can manage and maintain advanced systems. Investing in workforce training and development to fill the current void is essential to ensure a smooth transition.
Collaborative Ecosystem: Even the biggest of chipmakers is unlikely to reach the autonomous fab all on their own. Collaboration with technology providers, research institutions, and industry partners will be key. Sharing knowledge and best practices can accelerate the development and deployment of autonomous solutions.
Pilot Programs and Gradual Implementation: Transitioning to an autonomous fab should be approached incrementally. Starting with pilot programs to test and refine technologies in a controlled environment will help identify challenges and demonstrate the benefits. Gradual implementation allows for continuous improvement and adaptation.
How will fabs benefit?
The transition to an Autonomous Wafer Fab promises a multitude of benefits that will revolutionize semiconductor manufacturing:
Enhanced Efficiency: By optimizing production schedules and processes, autonomous fabs will achieve higher throughput and better resource utilization. This translates to increased production capacity and reduced operational costs.
Better Quality: Advanced process control and real-time adaptability ensure consistent product quality, minimizing defects and rework. This leads to higher yields and greater customer satisfaction.
Reduced Downtime: Predictive maintenance and automated decision-making reduce equipment failures and production interruptions. This results in higher uptime and more reliable operations.
Improved Flexibility: Autonomous fabs can quickly adapt to changing market demands and production requirements. This flexibility enables manufacturers to respond rapidly to customer needs and stay competitive in a dynamic industry.
Cost Savings: The efficiencies gained from autonomous operations lead to significant cost savings. Reduced labor intensity, lower material waste, and optimized energy consumption contribute to a more cost-effective production process.
Sounds great, but when will it become a reality?
The journey towards an Autonomous Wafer Fab is well underway, but the timeline for full realization varies depending on several factors, including technological advancements, industry adoption, and investment levels. However, significant progress is expected within the next decade.
Short-Term (1-3 Years):
- Implementation of pilot programs and continual adoption of AI, IoT, AST and other advanced technologies.
- Incremental improvements in scheduling, process control, and maintenance practices.
Medium-Term (3-7 Years):
- Broader adoption of autonomous solutions across the industry.
- Enhanced data integration and analytics capabilities.
- Development of a skilled workforce to support autonomous operations.
Long-Term (7-10 Years and Beyond):
- Full realization of the Autonomous Wafer Fab with minimal human intervention.
- Industry-wide standards and best practices for autonomous manufacturing.
- Continuous innovation and refinement of autonomous technologies.
Conclusion
The pathway to the Autonomous Wafer Fab is a transformative journey that holds immense potential for the semiconductor industry. By embracing advanced technologies, fostering collaboration, and investing in the future workforce, fabs can unlock unprecedented levels of efficiency, quality, and flexibility. Autonomous Scheduling Technology, as a key pillar, will play a crucial role in this evolution, driving the industry towards a future where production is seamless, self-optimizing, and truly autonomous. The vision of an Autonomous Wafer Fab is not just a distant possibility but an imminent reality, poised to redefine the landscape of semiconductor manufacturing.
Now available to download: our new Autonomous Scheduling Technology White Paper
We have just released a new White Paper on Autonomous Scheduling Technology (AST) with insights into the latest advancements and benefits.
Click here to read it.