Abstract
Businesses that rely solely on semiconductors face severe challenges due to various factors degrading semiconductor quality. Primary issues include inconsistent synthesis conditions, material impurities, surface defects, passivation problems, inaccurate characterization methods, batch-to-batch production inconsistencies, and degradation over time. To overcome these issues, improved characterization methods for semiconductor quantum dots are introduced by integrating a fuzzy logic–based controller. The approach begins by identifying and characterizing the root causes of poor semiconductor Quantum dot performance. A SIMULINK model is designed for the characterization process, alongside another model for implementing the fuzzy logic controller. An algorithm is developed to execute these processes and enforce the controller’s rule set, which aims to minimize defects and inconsistencies in the quantum dots. The system is then validated by comparing the performance of the conventional process with that achieved using the fuzzy logic controller. Results indicate that inconsistent synthesis conditions, originally contributing 30% to poor characterization, were reduced to 27.05% with the fuzzy controller, representing an enhancement of 2.95%. Similarly, the issue of inadequate control of quantum dot size, responsible for a 15% performance deterioration, improved to 13.53% upon fuzzy controller integration. Additionally, inconsistent batch-to-batch production, which accounted for 5% of the issues, was lowered to 4.508%. Overall, the fuzzy logic–based controller yielded an improvement of 0.492% in the characterization methods. This study demonstrates that incorporating fuzzy logic can effectively mitigate several critical causes of semiconductor quantum dot degradation, thereby enhancing performance and reliability in semiconductor-dependent operations.