The close relationship between yield and reliability of semiconductor ICs has been well studied and documented. The data in Figure 1 demonstrates this relationship. Similar results are available at the batch, wafer and chip levels. In short, the yield is high and the reliability is good. The correlation between yield and reliability is completely unexpected, because the type of defect that causes chip failure is the same as the type of defect that causes early reliability problems. The differences between defects affecting yield and reliability are mainly in their size and their position on the chip pattern.
Figure 1 The close relationship between IC component reliability and yield.
Therefore, reducing the number of defects affecting yield in the IC manufacturing process will increase the benchmark yield and improve the reliability of components in actual use. Recognizing this fact, the foundries serving the automotive market face two key factors. The first problem is economic: In order to improve reliability, it takes time, money and resources to increase yield, and what is the appropriate level of investment? The second question is technical: In order to raise the benchmark yield to the necessary level, what? Is the best way to reduce defects?
For OEMs that manufacture consumer electronics (mobile phones, tablets, etc.), "mature yield" is defined as a turning point in further investment in time and resources that does not necessarily increase yield. As the product matures, yield Stabilizing, usually reaching a high value but still well below 100%. Consumer product foundries will redistribute resources to the process and equipment for developing the next design node, or reduce costs to increase the profitability of their mature nodes. Ability, not to pursue higher yields, because doing so is more economical.
For automotive foundries, the economic decision to increase investment in order to increase yields has exceeded the typical marginal benefit decision. When reliability issues arise, automotive IC manufacturers may have to bear expensive and time-consuming failure analysis. And assume the economic responsibility for failure and product recovery during the warranty period of the product, as well as potential legal liability. Considering that the reliability requirements for automotive ICs are two to three orders of magnitude higher than consumer ICs, automotive foundries must achieve even more High benchmark yield level. This requires rethinking the meaning of "mature yield."
Figure 2 highlights the difference between the mature yield of consumer products and automotive OEMs. Any type of fab will increase the yield curve, so almost all of the systemic impact yields have been resolved. The yield loss is mainly caused by random defects in the process equipment or the environment. At this time, the consumer product foundry may consider the yield and reliability to be "good enough" and take the appropriate approach. However, in the automotive industry, generation The factory uses a continuous improvement strategy to push up the yield curve. By reducing the incidence of defects affecting yield, automotive foundries can also reduce potential reliability defects, thereby optimizing their profits and reducing risk.
The automotive supply chain (from OEMs to Tier 1 suppliers to IC manufacturers) is forming a mindset that “every defect is important” and a strategy of pursuing zero defects. They recognize that when potential defects leave After the foundry, it finds and solves the cost of each step forward in the supply chain by 10 times. Therefore, the current method of over-reliance on electrical testing needs to be replaced by the lowest cost strategy, that is, the potential failure in the foundry Stop. Only a methodical implementation of the plan to reduce defects, the foundry can achieve zero defect targets, and can be strictly audited by car manufacturers.
In addition to robust online defect control capabilities, some of the ways that car purchasing managers want to see to reduce defects include:
Continuous Improvement Program (CIP) to reduce baseline defects
. Best equipment workflow
Bad Equipment Improvement Program
Continue to reduce baseline defects
The line defect strategy is the basis for any strict reduction of the baseline defect plan. To successfully detect yield and reliability defects affecting its design rules and component types, the foundry line defect strategy must include appropriate process control equipment and appropriate Inspection sampling plan. The defect detection system used must have the required defect sensitivity, be well maintained and up to specification, and use carefully adjusted inspection procedures. The inspection sampling must be sufficient for the process steps to quickly detect the process or equipment. In addition, there should be sufficient detection capacity to support accelerated anomaly detection, root cause differentiation and risk WIP tracking control plans. With these elements, automotive foundries should be able to achieve a successful baseline defect reduction program. The plan can demonstrate an improvement in yield trends over time, providing further improvement goals and equating industry best practices.
One of the biggest challenges in a baseline defect reduction plan is to answer: Where does the defect come from? The answer is often not so simple. Sometimes, defects are detected after multiple process steps. Sometimes, only after the wafer passes through the other After the process and "decoration" of the defect, it becomes apparent, which means that the defect is more obvious in the detection system. The device monitoring strategy helps solve the problem of the origin of the defect.
In equipment monitoring/device certification (TMTQ) applications, a wafer of wafers is first tested to run in a designated process equipment (or reaction chamber) and then retested (Figure 3). Any new defects must be due to the specified process equipment. The results are clear; there is no doubt about the root cause of the defect. The car foundry pursuing zero defect standards recognizes the benefits of the equipment monitoring strategy: through sensitive testing procedures , Appropriate Control Limits and Out of Control Action Plan (OCAP), can reveal random yield losses from each process equipment and resolve them.
Figure 3 After the “pre-check” detects the reference data of the wafer, the wafer can be used to cycle some or all of the process equipment steps. The “post-test” reveals the defects added to the process equipment.
In addition, as shown in Figure 4, the newly added defects of the process equipment are plotted over time, which provides a record of sustainable improvement that can be audited and used to set future defect reduction targets. The foundry can Classification of defects that occur on each device, and generates a database that can be used as a reference for failure analysis of field failures. This method requires very frequent device certification (at least once a day), usually with the best device workflow discussed below or Bad equipment improvement plan is used together.
Figure 4 Continuously improve the cleanliness of the equipment over time. The root cause of the problem is clear, and the defect reduction target can be set objectively quarterly or monthly. In addition, comparing the defects of the two process equipment can show which machine Cleaner. This helps guide equipment maintenance activities and locks the cause of discrepancies between devices.
AWF/bad equipment improvement plans have their own advantages
The best equipment workflow is another strategy used by foundries to meet the zero defect standards required by the automotive industry. With the best equipment workflow or automotive workflow (AWF), wafers for automotive ICs are only at the fab. Running in the best process equipment. This requires the fab to know the best machine for any custom process. To reliably determine which machine is the best, the foundry uses the online and equipment to monitor the detected data and then only those Machines are used in automotive workflows. Limiting automotive wafers to a single device at each process step can result in longer cycle times. However, compared to process flows with higher defect rates that can lead to reliability issues This approach is still preferred for automotive wafers. Coupled with a methodical continuous improvement program, most foundries can usually achieve multiple AWF-compliant equipment in each step by setting a quarterly defect reduction goal.
Because this method is difficult to scale, the best equipment workflow is best suited for small-scale WIP-based OEMs. For foundries that produce automotive products in large quantities, priority should be given to more streamlined continuous improvement programs, as shown below. The method of improving bad equipment.
The Bad Equipment Improvement Program is the opposite of the best equipment workflow because it can proactively address the worst process equipment in any given process step. The foundries that have the greatest success in reducing baseline defects often use poor equipment to improve their plans. They first down the worst device in each process step and adjust the device until it exceeds the average of the rest of the devices in the same group. They repeat the process over and over until all devices in the same group match Minimum Standard. An effective bad equipment improvement program requires the factory to have a well-organized equipment monitoring strategy to certify each process equipment at each step. At least one certification process is required to complete each day on each equipment. Be sure to collect enough data to have ANOVA or Kruskal-Wallis analysis determine the best and worst equipment in each group. A bad equipment improvement program will schedule downtime for process equipment and is known to upgrade the entire fab to the car. One of the fastest ways to standardize. By improving yield and reliability, the strategy finally mentions Effective productivity and profitability of automotive foundries.