An In-Depth Review of Models Used to Optimize Electron Beam Lithography Processes
Keywords:
Electron, Beam, Lithography, Currents, Flashes, Exposure, EBLOM, Accuracy, Throughput, Critical Dimensions, Cost, ComplexityAbstract
Electron beam (EB) lithography represents a fundamental technique in the semiconductor industry, involving the precise focus of electron beams onto silicon wafers to fabricate integrated circuits (ICs). This process leverages a suite of essential components, including an electron cannon, blanking electrode, deflection electrode, multiple electron lenses, and dedicated control circuits for each of these constituent parts. However, the challenge arises from the occurrence of crucial dimension overshoots during the lithography process, leading to a degradation in the quality of the resulting ICs. This deterioration stems from reduced re-exposure of chip regions, heightened beam currents, and an increased frequency of electron flashes. To address these critical issues, researchers have introduced a diverse array of optimization models, each possessing distinct qualitative and quantitative performance characteristics. The task of selecting the most suitable model for a specific application can be daunting, given the inherent variability in the operational properties of these models. To alleviate this uncertainty, the present work delves into the limitations specific to deployment, highlights functional advantages, elucidates application-specific intricacies, and outlines future research directions within the context of EB lithography optimizations. This comprehensive exploration has revealed the superiority of bioinspired models over traditional linear modeling techniques, particularly in the realm of real-time deployments. Unlike their predecessors, these bioinspired models target stochastic optimality in electron beam design, thereby concurrently enhancing both the quality and speed of wafer imprinting sets. To facilitate the decision-making process, this article undertakes a comparative analysis of various models, considering criteria such as crucial dimensions, throughput, accuracy, computational complexity, and deployment costs. In light of this discourse, researchers are empowered to make informed choices regarding the selection of models that align with the specific performance requirements of their applications. Furthermore, this paper advocates for the adoption of a novel metric, termed EB Lithography Optimization Metric (EBLOM), which amalgamates multiple performance metrics to evaluate the real-time performance of models in a holistic manner. The incorporation of EBLOM allows researchers to identify models that excel in diverse usage scenarios, offering enhanced efficiency and performance within the constraints of performance-specific limitations.
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