Core Viewpoint - The article discusses the evolution of semiconductor manufacturing processes, highlighting the transition from traditional linear workflows to more integrated approaches like Design and Technology Co-Optimization (DTCO) and Holistic Process Optimization (HPO) [1][3][6]. Group 1: Evolution of Semiconductor Manufacturing - In the early stages of semiconductor manufacturing, the process followed a simple linear flow with limited information exchange between steps [1]. - Since around 2000, as technology nodes have shrunk and lithography techniques advanced, the complexity of chip manufacturing has increased, leading to a greater coupling between design and manufacturing processes [1]. - DTCO has emerged as a core technology in chip manufacturing, widely adopted by leading manufacturers like TSMC and Samsung to enhance process upgrades and improve product yield [1]. Group 2: HPO Concept and Implementation - The founding team of the company began collaborating with top global chip manufacturers during the early development of DFM and DTCO concepts, leading to the establishment of the HPO concept in 2014 [3]. - HPO focuses on integrating design-related information into the manufacturing process, particularly in measurement and testing, to optimize manufacturability and yield [3]. - The HPO framework aims to create a closed-loop system for data integration, enhancing the collaboration between design and manufacturing [3][4]. Group 3: HPO Product Matrix and Market Practice - The company has developed a series of HPO strategic products, including DMC, PHD, ODAS, PME, and YieldBook, to enhance manufacturability checks and yield optimization [4][5]. - These products have been successfully applied in various domestic chip manufacturers, contributing to advanced chip technology and process development [5]. Group 4: HPO 2.0 Strategic Planning - The company is initiating HPO 2.0, which aims to integrate AI capabilities into existing products and develop new applications to enhance the semiconductor design and manufacturing process [6][9]. - AI will be utilized to improve optical proximity correction models, enhance yield equipment functionalities, and create a comprehensive data platform for yield analysis [8][9]. - The transition to HPO 2.0 signifies a strategic upgrade from point tool providers to platform solution providers, aiming to overcome yield bottlenecks in advanced processes [9].