The shrinking of semiconductor devices and the application of new technologies have become a major trend in the modern technology field, but they are also accompanied by high requirements for wafer-level reliability testing and modeling. To meet this need, reliability test methodologies are evolving and evolving to better understand and address the performance and reliability challenges of semiconductor devices. In this article, we will explore some of the new reliability test methods, such as charge pump technology and I-V characterization test methods, and their applications in semiconductor device research.
With the shrinking of semiconductor devices, traditional methods of measuring the interfacial state are no longer suitable for studying the hot carrier effect. This effect is becoming more pronounced in modern semiconductor devices, so new test methods are needed to better understand it. Charge pump technology is an emerging approach that uses the charge pump effect of semiconductor materials to study the performance of devices. This method is based on the I-V characteristic test method, and the performance of the device is observed through stress experiments. The combination of these two methods provides more possibilities for studying the hot carrier effect.
The hot carrier effect is an important problem in semiconductor devices, which can lead to device degradation and performance degradation. Therefore, understanding and characterizing this effect is critical to ensure the reliability of the device. Using charge pump technology and I-V characterization test methods, researchers can gain a more complete understanding of the nature and effects of the hot carrier effect, allowing them to take appropriate steps to improve the reliability of their devices.
Theoretical knowledge of artificial neural networks (ANNs) also plays a key role in research. An artificial neural network is a computational model that mimics a biological neural network, which can be used to analyze and model complex data. A multilayer perceptron (MLP) is a common artificial neural network structure that mimics the process of information transfer between neurons. Such networks can be used to model and predict the performance and reliability of semiconductor devices.
In addition to traditional artificial neural networks, knowledge-based neural networks (KBNNs) have also been introduced into research. KBNN combines the knowledge of domain experts and incorporates it into neural networks to improve the performance and accuracy of the network. The basic structure of KBNN includes a knowledge layer, a boundary layer, a region layer, and a standardized region layer, each with different functions to better deal with complex problems.
In the application of neural networks, the method of selecting and generating test data is also crucial. Appropriate test data can improve the training effect and prediction accuracy of the network. In addition, the independence of the test data and the evaluation criteria are also the core factors that affect the performance of neural networks. Neural networks can only be used to their best if they have the right data and evaluation methods.
In conclusion, the research content emphasizes the importance in the establishment of degradation models of semiconductor devices. New reliability test methods, such as charge pump technology and I-V characterization test methods, as well as the application of artificial neural networks, provide us with additional tools and methods to better understand and address the performance and reliability challenges of semiconductor devices. The continuous development and innovation in this field will help drive the advancement of semiconductor technology and bring more possibilities to our technological world.