Interview Questions for DFM and DFA for MEMS

In the realm of MEMS (Microelectromechanical Systems), Design for Manufacturing (DFM) and Design for Assembly (DFA) are crucial considerations for ensuring efficient and cost-effective production. While both aim to optimize the manufacturing process, they focus on different aspects.

DFM centers on making the design easily manufacturable. It considers the fabrication processes, materials, and limitations of the chosen technology to ensure high yield and low cost. Think of it as designing for the machines – ensuring the design is compatible with the equipment and techniques available.

DFA focuses on the ease of assembling the finished MEMS device. It involves considering how different components will be integrated, including handling, alignment, bonding, and packaging. It’s all about designing for the human element – how easily can technicians assemble the product?

For example, in surface micromachining, DFM might involve avoiding structures with high aspect ratios to prevent collapse during processing. DFA, on the other hand, would focus on designing features that enable easy pick-and-place assembly of individual MEMS components into a larger system.

My experience encompasses a broad range of MEMS fabrication processes, with a particular emphasis on bulk and surface micromachining. I’ve worked extensively with:

• Bulk Micromachining: This involves etching away portions of a silicon wafer to create three-dimensional structures. I’ve utilized techniques like deep reactive ion etching (DRIE) to create high-aspect-ratio features in applications such as accelerometers and pressure sensors. I’ve faced and overcome challenges associated with anisotropic etching and achieving precise dimensions.
• Surface Micromachining: This method builds structures layer by layer on a substrate. My experience includes working with polysilicon and metal deposition, lithography, and etching to fabricate complex microstructures like micro-mirrors and actuators. I’m proficient in optimizing layer thicknesses and sacrificial layer removal to ensure structural integrity.
• Other Processes: I also have familiarity with wafer bonding techniques (anodic bonding, fusion bonding) crucial for integrating different materials and functionalities into the MEMS devices.

Throughout my work, I’ve always focused on understanding the limitations and capabilities of each process to guide the design and ensure manufacturability. For instance, DRIE limitations like bowing and undercut are critical considerations I always integrate into my DFM analysis.

Incorporating DFM principles at the earliest design stages is crucial for a successful MEMS project. My approach involves a multi-step process:

1. Process Selection and Technology Review: Initially, a thorough evaluation of the available MEMS fabrication processes and their limitations is vital. This guides the initial design and limits the scope to feasible options.
2. Design Rule Check (DRC): Early design rules are created based on the chosen fabrication process capabilities. This ensures that the design respects physical limitations during fabrication. For instance, minimum feature size, aspect ratios, and layer thickness variations.
3. Design for Testability (DFT): Designing testing structures into the chip from the beginning allows for better process monitoring, failure analysis, and yield improvement.
4. Simulation and Modeling: Finite element analysis (FEA) and other simulation techniques are employed to predict the performance and structural integrity of the device under various conditions. This helps identify potential issues before fabrication.
5. Collaboration with Fabrication Team: Close interaction with the fabrication team is vital. This guarantees that the design is realistically manufacturable and that any potential issues are identified and addressed promptly.

This proactive approach minimizes costly design iterations and ensures a smoothly flowing transition from design to fabrication.

Common DFM challenges encountered during MEMS fabrication include:

• Stiction: This phenomenon occurs when microstructures adhere to the substrate or adjacent layers, typically due to surface tension or electrostatic forces. It’s particularly challenging in surface micromachining and necessitates careful design considerations regarding surface treatments and release processes.
• Stress-Induced Failures: Residual stresses from fabrication processes can lead to warping, cracking, or other structural failures. Careful material selection and process optimization are critical to minimize this.
• Aspect Ratio Limitations: Achieving high aspect ratios in microstructures can be problematic. Deep etching processes like DRIE can have limitations, leading to profile variations or bowing of the structures.
• Yield Issues: Variations in the fabrication process can result in a low yield of functional devices. This could stem from numerous sources and requires thorough root cause analysis.
• Alignment Challenges: Precise alignment of multiple layers is essential in surface micromachining. Variations in alignment can lead to structural defects or malfunctions.

Overcoming these challenges requires meticulous planning, process optimization, and a deep understanding of the underlying physics of MEMS fabrication.

Addressing yield issues in MEMS manufacturing is a multifaceted problem requiring a systematic approach. My strategies include:

1. Root Cause Analysis: Identifying the root cause of yield loss is crucial. This often involves detailed failure analysis of non-functional devices using techniques like optical microscopy, scanning electron microscopy (SEM), and focused ion beam (FIB) imaging.
2. Process Optimization: Based on the root cause analysis, adjustments are made to the fabrication process. This might involve refining etching parameters, optimizing deposition conditions, or improving the cleaning procedures.
3. Design Improvements: The design itself might need modifications. This could involve changing the dimensions of specific structures, enhancing the robustness of certain features, or adding redundancy to critical components.
4. Statistical Process Control (SPC): Implementing SPC methods helps to monitor and control the variability in the fabrication process. This proactively identifies potential yield excursions.
5. Design of Experiments (DOE): DOE helps optimize process parameters efficiently. It systematically tests different parameter combinations to find optimal settings leading to high yield.

A combination of these methods, focusing on data-driven decision making, is essential to improve yield and reduce manufacturing costs.

Design for Testability (DFT) in MEMS is critically important for ensuring the functionality and reliability of the devices. It involves incorporating specific test structures and features into the design to enable efficient testing and fault diagnosis. This approach minimizes post-fabrication testing challenges and time.

Effective DFT strategies in MEMS include:

• Built-in Self-Test (BIST): Integrating circuits for on-chip testing allows for quick and automated verification of the device’s functionality before packaging.
• Test Structures: Including dedicated test structures like resistors, capacitors, and resonators provides benchmarks for verifying process parameters and detecting anomalies.
• Access Points: Designing access points for probing allows for electrical measurements and characterization of individual components or the entire device.
• Redundancy: Incorporating redundant components allows for device operation even with partial failures. This improves reliability and yield.

By proactively incorporating DFT principles, we can significantly reduce testing time and costs, improve the quality of the finished MEMS devices, and enhance their overall reliability.

My experience encompasses several MEMS packaging techniques, each chosen based on the specific application requirements and device characteristics. These techniques range from simple to complex, depending on the environmental protection and interfacing needed.

• Wire Bonding: A widely used method for electrical interconnection, involving connecting the MEMS chip to external circuitry using fine gold wires.
• Flip-Chip Bonding: Involves mounting the MEMS chip face down onto a substrate. This offers high-density interconnections and improved thermal management.
• Hermetic Sealing: For applications requiring high protection from moisture and other environmental factors, hermetic sealing (e.g., using glass or ceramic lids) is essential. This provides superior protection compared to other packaging techniques.
• Advanced Packaging: More sophisticated techniques, such as system-in-package (SiP) solutions, integrate multiple components, including MEMS devices, into a single package.

Choosing the right packaging method involves a careful assessment of the trade-offs between cost, performance, reliability, and environmental protection. I’ve worked extensively on optimizing packaging designs to ensure optimal device functionality and longevity.

Balancing performance and manufacturability in MEMS design is a delicate act of compromise. It’s like designing a high-performance sports car – you want it fast and agile (high performance), but also reliable and affordable to produce (manufacturability). We achieve this through iterative design cycles incorporating Design for Manufacturability (DFM) and Design for Assembly (DFA) principles from the outset.

• Process Capability Analysis: Early simulations and analyses using software like CoventorWare or COMSOL predict the impact of process variations on device performance. This allows us to identify potential weaknesses and adjust the design to be less sensitive to manufacturing tolerances. For example, if a gap between two MEMS structures is critical for functionality, we can design it to be wider than the minimum required to account for potential etching variations.

• Design Simplification: Reducing the number of process steps and components directly improves manufacturability and yield. This might involve choosing a simpler fabrication process, eliminating unnecessary features, or consolidating multiple parts into one. For instance, instead of bonding two separate wafers, we might try to create the entire structure on a single wafer.

• Material Selection: Selecting materials with readily available, reliable processes and robust properties reduces manufacturing challenges and failures. This minimizes risks during fabrication.

Essentially, we use a feedback loop. We design, simulate, analyze manufacturability, refine the design, and repeat the process until we reach an optimal balance between performance and manufacturability. This iterative approach is key to successfully bringing a MEMS device to market.

My proficiency in MEMS DFM/DFA analysis software includes CoventorWare, COMSOL Multiphysics, and ANSYS. CoventorWare is invaluable for MEMS-specific simulations, including process simulations and stress analysis. COMSOL allows for broader multiphysics simulations, incorporating aspects like fluid dynamics and electrostatics that are often crucial in MEMS designs. ANSYS offers robust structural analysis capabilities vital for assessing the mechanical robustness of the device. Beyond these, I also utilize CAD software like AutoCAD and SolidWorks for detailed geometric modeling and design verification.

During the development of a microfluidic chip, we encountered unexpected high failure rates during the packaging stage. Initially, we suspected issues with the chip itself. However, through rigorous root cause analysis, which involved examining failed devices under a microscope, analyzing process logs, and running control experiments, we discovered that the epoxy used for sealing the chip was reacting with a specific metal layer in the chip, causing delamination and leakage. The solution was to replace the epoxy with a chemically compatible alternative and to incorporate a more robust design for packaging. The failure rate dropped dramatically once the compatible material was implemented. This highlighted the importance of considering material compatibility at every stage of the design and fabrication process.

Ensuring robustness and reliability in MEMS design begins with a thorough understanding of the operating environment and potential failure mechanisms. This involves:

• Finite Element Analysis (FEA): FEA helps predict stress, strain, and fatigue under various conditions, enabling the identification and mitigation of weak points. For instance, we might discover a potential stress concentration point in a cantilever beam and reinforce it through design changes.

• Reliability Testing: Accelerated life testing, such as temperature cycling and shock testing, simulates real-world conditions and helps uncover latent weaknesses. This is crucial to identify potential failure modes and improve the overall reliability of the MEMS devices before mass production.

• Material Selection and Characterization: Choosing materials with high strength-to-weight ratios and resistance to environmental factors (such as moisture, temperature, and chemicals) is fundamental. Thorough material characterization ensures that the selected materials meet the performance requirements and operational lifespan expectations.

• Tolerance Analysis: A critical step is to analyze how process variations and tolerances affect device performance and reliability. This guides design choices to minimize the impact of manufacturing variations.

By incorporating these analyses and tests early in the design process, we can proactively address potential reliability issues, leading to a more robust and reliable final product.

Material selection for MEMS is driven by a complex interplay of factors:

• Mechanical Properties: Strength, stiffness, elasticity, and fatigue resistance are crucial, depending on the device’s function. Silicon is a popular choice due to its well-established processing techniques and relatively good mechanical properties.

• Chemical Stability: The material must withstand the processing chemicals and the operating environment. For instance, in biomedical applications, biocompatibility is paramount.

• Electrical Properties: Conductivity, resistivity, and dielectric properties are vital for devices with electrical functionalities.

• Etch Characteristics: The material needs to be readily etchable using standard microfabrication processes. Different etching techniques have varying impacts on material properties, requiring careful selection for the intended device design.

Often, we need to balance competing requirements. For example, a material with high strength might be difficult to etch, or a biocompatible material might lack the required electrical properties. Careful consideration and potentially a trade-off are necessary to arrive at the most suitable material combination.

Statistical Process Control (SPC) in MEMS manufacturing is essential for maintaining consistent product quality and identifying potential problems early. It involves monitoring key process parameters (KPIs) using statistical methods to detect deviations from the target values. This allows for timely intervention to prevent defects and maintain high yield.

In MEMS, SPC is used to monitor parameters such as:

• Etch rates and uniformity: Ensuring consistent etching across the wafer.

• Layer thicknesses: Maintaining precise layer dimensions.

• Gap dimensions: Controlling the critical gaps between MEMS structures.

• Device performance metrics: Measuring parameters like resonance frequency or sensitivity.

Control charts, such as X-bar and R charts or Cpk calculations, are commonly used to visually monitor process capability and detect shifts or trends that signal potential problems. SPC allows for proactive adjustments to the manufacturing process to prevent defects, leading to higher yields and more consistent product quality.

Incorporating DFA into MEMS design focuses on simplifying assembly to reduce costs, improve yield, and ensure reliability. This involves designing the MEMS device to minimize the number of parts and assembly steps.

• Self-Assembly Techniques: Exploring self-assembly techniques where possible can drastically reduce manual assembly steps. This might involve designing features that allow for spontaneous alignment or bonding of components.

• Modular Design: Breaking down the device into functional modules that can be assembled independently simplifies the process. This enables easier testing and replacement of individual components if needed.

• Pick-and-Place Compatibility: Designing parts with features that are easily gripped and placed by automated pick-and-place machines reduces assembly time and human error.

• Alignment Features: Incorporating alignment features, such as alignment marks, aids in automated assembly and precise placement of components.

For example, instead of assembling multiple individual components into a complex MEMS structure, we might design the entire structure to be fabricated monolithically on a single wafer, significantly simplifying the assembly process. DFA is a crucial step in ensuring cost-effectiveness and high reliability during manufacturing.

Design for Assembly (DFA) challenges in MEMS are often amplified by the miniature scale and complex geometries involved. One major hurdle is the handling and placement of delicate MEMS components. Their small size makes them susceptible to damage during assembly, requiring specialized tools and techniques like robotic manipulation and micro-handling systems. Another significant challenge is achieving precise alignment and bonding. Micrometer-level accuracy is often necessary, making processes like die bonding, wire bonding, and wafer-level packaging exceptionally demanding. Furthermore, the integration of multiple components into a single package presents difficulties. This can involve managing different materials with potentially incompatible thermal expansion coefficients, leading to stress and failure. Finally, contamination control is paramount. Even microscopic dust particles can severely compromise the functionality of a MEMS device, demanding stringent cleanroom environments and rigorous processes throughout assembly.

• Example: Aligning a micro-mirror with its supporting actuators within a few microns requires sophisticated alignment systems and often iterative adjustments.
• Example: Anodic bonding, a common technique in MEMS packaging, can be challenging to control, leading to inconsistencies in bond strength and potential leakage.

Evaluating the cost-effectiveness of MEMS manufacturing processes requires a holistic approach, considering factors beyond just raw material costs. We need to analyze the yield, meaning the percentage of functional devices produced, which directly impacts the cost per unit. High defect rates significantly increase the cost. Throughput, or the number of devices produced per unit time, is another key factor; higher throughput generally reduces per-unit costs. The capital expenditure (CAPEX) for equipment like cleanrooms, lithography tools, and assembly robots significantly influences the overall cost structure. Operating expenses (OPEX), including labor, maintenance, and consumables, also play a vital role. Finally, the packaging cost can be substantial, varying greatly depending on the complexity of the device and packaging requirements.

A common approach is to develop a cost model that integrates all these elements. This can be done using spreadsheet software or dedicated manufacturing cost estimation tools. By comparing different process options through this model, we can identify the most economically viable solution, balancing the initial investment with production efficiency and yield.

Example: Comparing bulk micromachining with surface micromachining for a specific sensor application might reveal that bulk micromachining is cost-effective for high-volume production due to better yield, while surface micromachining may be preferable for prototyping due to lower initial equipment costs.

Tolerance analysis is critical in MEMS design due to the tiny dimensions and tight specifications involved. Even minute variations in dimensions or material properties can drastically affect device performance. My experience involves utilizing both statistical tolerance analysis and Monte Carlo simulations. Statistical tolerance analysis uses mathematical formulas to quantify the propagation of tolerances through the design. Monte Carlo simulation, on the other hand, uses random sampling to determine the probability distribution of device performance parameters. These simulations allow us to identify critical design parameters that are most sensitive to variations and guide design optimization for improved robustness.

Example: In the design of a micro-cantilever sensor, we might use tolerance analysis to determine the impact of variations in cantilever thickness and length on the sensor’s resonant frequency. This would allow us to identify acceptable tolerances for manufacturing.

Software: I have experience using software packages like ANSYS and MATLAB for tolerance analysis and Monte Carlo simulation in MEMS design.

Handling design changes during MEMS fabrication necessitates careful planning and coordination. The specific approach depends heavily on the stage of fabrication at which the change is required. Early design changes, before fabrication begins, are relatively straightforward, involving adjustments to the mask design and software used for fabrication. Later changes, however, require more complex strategies and can be extremely expensive. They often involve re-spinning wafers or even completely restarting the fabrication process. To mitigate the impact of design changes, we use design reviews and thorough simulations to ensure the design’s robustness and minimize the likelihood of late-stage modifications.

In case a design change becomes necessary during fabrication, we assess the impact on the existing partially fabricated wafers and decide whether to rework them or to discard them and start anew. We need to weigh the cost of rework against the cost of producing a completely new batch of wafers. This decision is heavily influenced by the nature of the change and the amount of effort already invested in the current fabrication run.

Example: Discovering a critical flaw in the layout of interconnects during the lithography stage might necessitate re-spinning the wafers, incurring significant extra cost and time delays. To minimize such scenarios, thorough design verification and reviews are crucial before moving to each fabrication stage.

Minimizing the environmental impact of MEMS manufacturing requires a multi-pronged approach focusing on resource consumption, waste reduction, and energy efficiency. One key strategy is to adopt green chemistry principles, employing environmentally benign materials and processes. This includes selecting materials with low toxicity and recyclability, and replacing hazardous chemicals with safer alternatives. Implementing waste reduction techniques is critical. This might involve optimizing processes to reduce material waste, recycling solvents and chemicals, and properly disposing of hazardous waste in accordance with environmental regulations.

Furthermore, efforts should be made to improve energy efficiency across the entire manufacturing process. This involves optimizing equipment usage, improving the efficiency of energy-intensive steps like lithography, and utilizing renewable energy sources wherever possible. Finally, considering the end-of-life management of MEMS devices is essential. This means designing devices for easy disassembly and recycling to minimize environmental impact once their useful life is over. The focus should be on designing for recyclability from the outset, making the recovery of valuable materials easier and more cost-effective.

Example: Using water-based resists instead of solvent-based resists in photolithography reduces the use of volatile organic compounds and minimizes air pollution.

Finite Element Analysis (FEA) is indispensable in MEMS design for predicting the mechanical behavior of devices under various conditions. My experience includes utilizing FEA to simulate stress, strain, and displacement in MEMS structures. This helps identify potential failure points, optimize designs for strength and reliability, and validate design choices before fabrication. I am proficient in using commercial FEA software packages like ANSYS and COMSOL Multiphysics to model complex MEMS structures, incorporating various material properties and boundary conditions.

Example: Using FEA to analyze the stress distribution in a micro-cantilever beam subjected to applied force helps determine the maximum allowable force before failure, ensuring the structural integrity of the device. Similar analyses can be used for predicting the resonant frequency of vibrating MEMS structures and ensuring its functionality within the desired range.

Applications: FEA is commonly used to analyze resonant frequencies, stress concentrations, deflection, thermal effects, and fluid flow in MEMS devices.

My experience encompasses a range of MEMS sensors and actuators. In sensors, I’ve worked extensively with accelerometers, using capacitive sensing techniques to measure acceleration. These are widely used in smartphones, gaming consoles, and automotive applications. I’ve also been involved in the design and analysis of gyroscopes based on Coriolis force, vital for motion tracking and inertial navigation systems. Further experience includes pressure sensors, leveraging piezoresistive or capacitive effects for measurement. I have also explored optical MEMS sensors, such as micro-mirrors for optical switching and beam steering in various applications including optical communication and medical imaging.

In actuators, my focus has been on electrostatic actuators, which are commonly used in micro-mirrors and optical switches. These actuators provide high precision and fast response times. I’ve also worked with thermal actuators, relying on the expansion and contraction of materials due to temperature changes, offering larger displacement compared to electrostatic actuators but slower response times. Finally, I’ve investigated piezoelectric actuators, utilizing the piezoelectric effect for high-force, high-precision actuation, particularly beneficial in applications demanding precise positioning.

Example: A project involved designing a micro-mirror array for a laser scanning application, requiring a deep understanding of both electrostatic actuation and optical design principles.

Ensuring precise alignment and bonding in MEMS assembly is crucial for device functionality. It’s like assembling a complex clock – if the gears aren’t perfectly aligned, it won’t work. We employ several techniques to achieve this. Precision alignment often relies on techniques like optical alignment systems using microscopes and lasers to position components with sub-micron accuracy. This is particularly important for wafer-level bonding where multiple dies need to be perfectly aligned before bonding. Bonding techniques are equally critical. We use methods like anodic bonding, thermocompression bonding, and adhesive bonding, each selected based on material compatibility and desired bond strength. For instance, anodic bonding is excellent for glass-silicon bonding due to its high strength and hermetic seal, while adhesive bonding offers flexibility for different materials. Process monitoring and control are key. In-situ monitoring during bonding, using techniques like infrared thermography to track temperature uniformity, ensures a consistent and reliable bond. Finally, post-bond inspection with techniques like optical microscopy and scanning electron microscopy (SEM) verifies the quality and completeness of the bond. Any misalignment or voids are identified and analyzed to improve the process for future runs.

MEMS devices, being miniature marvels of engineering, are prone to several failure modes. These can broadly be categorized into mechanical failures, electrical failures, and environmental failures. Mechanical failures often involve stiction (adhesion of moving parts), fracture due to stress concentration (think of a tiny bridge collapsing), or fatigue from repeated cycles of operation. For example, a micro-mirror in an optical switch might fail due to stiction if the surface tension forces are too strong. Electrical failures can arise from short circuits, open circuits, or dielectric breakdown. Think of a tiny wire breaking within the device. Environmental failures might involve corrosion, contamination, or damage due to temperature cycling or humidity. For example, moisture penetrating a MEMS sensor package can lead to corrosion and malfunction. Understanding these failure modes is critical for designing robust and reliable devices.

Root cause analysis for MEMS failures is a systematic process, akin to detective work. We typically employ a structured approach like the ‘5 Whys’ technique to drill down to the root cause. We start with the observed failure and repeatedly ask ‘why’ until we identify the underlying issue. For example, if a MEMS accelerometer fails to provide accurate readings, we might ask: ‘Why is the reading inaccurate?’ (Maybe due to offset drift). ‘Why is there offset drift?’ (Maybe due to temperature sensitivity). ‘Why is it temperature sensitive?’ (Maybe due to a specific material used). Along with the ‘5 Whys’, we use advanced analytical techniques such as: Failure analysis using microscopy techniques (SEM, TEM) to visually inspect the device for defects; Electrical characterization to measure device parameters and identify anomalies; and Finite Element Analysis (FEA) to simulate stress, strain, and other parameters within the device, helping pinpoint regions prone to failure. By combining these methods, we can effectively determine the underlying cause and implement corrective measures.

My experience with process monitoring and control in MEMS fabrication is extensive. It’s crucial for consistent and high-yield production. We employ a multi-pronged strategy. First, we use in-line monitoring techniques throughout the fabrication process. This includes real-time measurements of parameters like temperature, pressure, and flow rates during deposition, etching, and other processes. These are often integrated with automated control systems to maintain tight process tolerances. Secondly, in-situ metrology is vital for tracking critical dimensions and properties of the MEMS structure during fabrication. Techniques like ellipsometry, optical profilometry, and scanning probe microscopy allow for precise measurements of layer thickness, surface roughness, and other critical parameters. Finally, rigorous statistical process control (SPC) techniques are applied to analyze the data collected from in-line monitoring and metrology. Control charts help to identify trends and variations that could lead to defects or failures, enabling proactive adjustments to the process parameters and prevention of problems. For example, if we observe an upward trend in the defect rate on a control chart, we investigate and adjust the process parameters to bring the defect rate back within acceptable limits.

Quantifying the impact of process variations on MEMS device performance is crucial for ensuring product reliability. We use statistical modeling and design of experiments (DOE) techniques to achieve this. DOE helps us systematically vary key process parameters and measure the resulting changes in device performance. Techniques like Taguchi methods or full factorial designs are employed to determine the sensitivity of the device to various process parameters. Monte Carlo simulation is often used to incorporate the inherent randomness of process variations into the model and predict the distribution of device performance metrics. This allows us to estimate yield, identify critical parameters that need tighter control, and determine the impact of tolerances on device functionality. For example, by running Monte Carlo simulations, we can predict the probability of a particular MEMS resonator falling outside its specified frequency range due to variations in its dimensions or material properties.

The future of MEMS DFM/DFA will likely be shaped by several key trends. Increased integration will lead to more complex devices that require more sophisticated DFM/DFA strategies. The development of advanced materials with improved properties will influence design and fabrication processes. Artificial intelligence (AI) and machine learning (ML) will play a larger role in process optimization, predictive modeling, and root cause analysis of failures. AI-powered tools will analyze vast datasets from fabrication and testing to identify subtle correlations between process parameters and device performance, leading to improved designs and more efficient processes. Finally, the integration of multi-physics simulation tools will allow for a more holistic approach to design and analysis, enabling the prediction of complex interactions within the device. This will help in designing more reliable and robust MEMS devices while minimizing development time and costs.