Semiconductor-Glorysoft (Shanghai) Co.,Ltd.

Semiconductor

Substrate Materials

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: Substrate material processes differ significantly with diverse specifications, making CIM system construction highly challenging. Silicon-based and third-generation semiconductor substrates have marked differences in process requirements and size specifications, imposing extremely high demands on manufacturing system compatibility and flexibility.

Pain Point 2: During substrate manufacturing, most processes require metrology. Especially for high-capacity and large-size products, data volumes surge rapidly, requiring high system data processing efficiency and stability to avoid lag or latency.

Pain Point 3: Ingot growth parameter adjustments have long relied on operator experience; achieving data-driven automatic parameter adjustment and optimization remains a critical technical challenge to overcome.

Product Empowerment Value

Value 1: A unified platform architecture covers all process steps, supporting lot-level management for small-size products and piece-level (per wafer) management for large-size products, while adapting to fully automated operational scenarios.

Value 2: Comprehensive collection of processing lot, equipment status, and process parameter data across all process steps for real-time analysis and processing; the big data platform supports custom data stress testing to guarantee processing efficiency and stability under massive data scenarios.

Value 3: Supporting non-standard both and standardized communication protocols for data acquisition from Ingot pullers, with capability to perform equipment modifications to enable real-time monitoring, adjustment, and early warning for key parameters such as temperature, effectively reducing reliance on operator experience.

Wafer

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: The core characteristics of wafer manufacturing are process complexity, co-existence of process rigidity and customization, and high yield sensitivity, which directly make CIM systems difficult to achieve "universal adaptation" and result in massive customization development work.

Pain Point 2: Wafer fabs are typical data-intensive production lines with massive daily data volumes. Data types encompass real-time equipment data, structured process data, and unstructured inspection data (wafer defect maps, overlay maps, etc.). Data governance is the core challenge in CIM construction and the foundation for subsequent data value extraction.

Pain Point 3: Wafer fab CIM construction is not merely a "software project", but a system engineering effort integrating semiconductor processes, industrial software, automation, big data, and project management. The overall project implementation cycle is long and capital-intensive, requiring exceptionally high capabilities from subsequent O&M teams.

Product Empowerment Value

Value 1: A unified platform architecture, along with complete solutions honed across multiple wafer fab CIM implementations, effectively lowers implementation difficulty.

Value 2: The big data processing platform collaborates uniformly with the proprietary AI large model platform GloryIGI, integrating wafer lot, equipment, and process parameter data across all line steps to provide decision support for yield optimization and process adjustments.

Value 3: Possessing multiple successful CIM implementation experiences and comprehensive after-sales service, reducing client difficulty and risk in subsequent O&M.

300mm

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: The MES market has long been dominated by a few major players, creating a concentration risk that prompts fab operators to explore viable alternatives. While large‑scale 300mm fully‑automated production references from emerging vendors are still limited.

Pain Point 2: Currently, instances in the industry of fully implementing a project from scratch to genuine AUTO3 acceptance are rare. Achieving the three-stage leap from manual to semi-automated, and ultimately to fully automated operations involves extreme complexity, demanding extraordinarily high product maturity, team experience, and project management capabilities from vendors.

Pain Point 3: Fab systems such as MES, EAP, RTD, APC/FDC, and Scheduling come from different vendors with complex interfaces and non-uniform data standards, creating "data silos" that severely hinder end-to-end automated collaboration and data-driven decision-making, becoming the primary bottleneck in building AUTO3 smart fabs.

Pain Point 4: In complex production scenarios such as Non-Product Wafer (NPW) management, Pilot Run, and Sampling, there is high reliance on manual experience and repetitive operations, which is inefficient and error-prone, making dynamic optimization and precise control based on real-time data difficult to achieve.

Product Empowerment Value

Value 1: Delivering proven 300mm fully-automated MES solutions with verified production references, helping to mitigate client project risks. Glorysoft's AUTO3-level MES system, built on proprietary intellectual property, ensures operational continuity and security in manufacturing environments.

Value 2: Providing proven, mature products and efficient implementation capabilities to shorten production ramp-up cycles. The team once helped a client achieve AUTO3 and pass final acceptance within 15 months.

Value 3: Unifying manufacturing processes through a single platform and data model, providing integrated support for clients upgrading automation from AUTO1 to AUTO3 levels.

Value 4: Built-in intelligent algorithm models (such as smart NPW management and dispatching, APC-integrated Pilot Run control, and dynamic sampling rules) replace manual judgment, achieving refined and automated management of production processes, significantly improving overall equipment effectiveness (OEE), shortening Cycle Time, and reducing reliance on highly skilled operators.

Packaging & Testing

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: Traditional systems can only handle lot-level management, failing to satisfy advanced packaging demands for precise single-die traceability. Meanwhile, yield data is scattered and hard to decompose in real time and precisely down to step and equipment levels, leading to lagged anomaly detection and an inability to achieve proactive early warning and closed-loop management.

Pain Point 2: The packaging and testing process chain is lengthy, encompassing hundreds of steps including wafer dicing, die bonding (DB), wire bonding (WB), molding, and multiple testing rounds. The adoption of advanced packaging technologies (such as CoWoS, FO-WLP, 2.5D/3D IC, and Chiplet) introduces substantial branching, rework, and engineering changes, making traditional linear management processes inadequate and demanding extreme flexibility from manufacturing systems.

Pain Point 3: Operational areas such as production, equipment, quality, and warehousing often use independent systems (e.g., ERP, MES, EAP), preventing seamless data integration. This creates information blind spots, making end-to-end "one-stop" visual control from wafer receipt to finished product shipment impossible, severely impacting operational efficiency and decision-making speed.

Product Empowerment Value

Value 1: Implementing full lifecycle precise die-level traceability to meet high-end product traceability requirements for advanced packaging, elevating product quality and market competitiveness.

Value 2: Managing the release and issuance of rework and modification work orders via the system, making rework processes controllable and costs transparent.

Value 3: Eliminating information silos across major operating systems to share and integrate data, unifying data sources, and enhancing operational decision-making.

Display Panel

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: The display panel industry is characterized by strong cyclicity, heavy assets, and thin profit margins; enterprises urgently need to transform from scale manufacturing to high-end, intelligent manufacturing to navigate industry cycles and enhance competitiveness.

Pain Point 2: Smart fabs involve numerous systems such as MES, EAP, WMS, QMS, YMS, and DMS; compatibility and integration among these systems are highly challenging, resulting in persistently high system construction and maintenance costs.

Pain Point 3: Data concerning yield, defects, quality, process parameters, and equipment status is scattered across different systems with inconsistent data dimensions. Engineers must switch between multiple systems, resulting in insufficient analysis efficiency and consistency.

Pain Point 4: Traditional monitoring remains at the post-event reporting stage, lacking automatic identification and priority analysis of anomaly patterns. Especially in high-precision processes such as Micro LED, yield risks caused by process fluctuations are difficult to effectively prevent and control.

Product Empowerment Value

CIM Overall Solution

Value 1: Achieving fab-wide smart production to manage personnel, materials, processes, and equipment, optimizing manufacturing workflows, reducing resource waste, elevating equipment utilization and production efficiency, and cutting production costs by at least 15%.

Value 2: Delivering fully autonomous systems with high cross-system compatibility, high integration, and low cost.

Big Data & AI Solutions

Value 1: AI fuses yield, defect, parameter, and equipment data to automatically identify anomaly patterns and key impact factors, assisting YMS and EDA in cross-stage causal chain analysis, significantly reducing reliance on manual experience in complex processes.

Value 2: Leveraging BDP's unified data governance capability, AI automatically correlates and extracts features from YMS, DMS, QMS, and FDC data, helping engineers conduct comprehensive yield, quality, and process analysis under a unified perspective.

Value 3: AI assists FDC in anomaly trend identification and alarm prioritization; combines with VM for predictive compensation of key metrology metrics; and works with EHM for risk assessment of equipment health status, interlocking with QMS to form a closed loop of anomaly handling and improvement verification.

PCB

High-End Printed Circuit Board (PCB) Industry

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: Production flows are long and highly varied, with opaque processes making precise delivery scheduling difficult.

Pain Point 2: Process execution relies heavily on operator experience, yet high staff turnover hinders knowledge retention. The lack of a standardized system leaves product quality and stability susceptible to human factors, making it hard to meet high-end clients' stringent compliance and traceability demands.

Pain Point 3: Heterogeneous equipment interfaces result in low automation and data integration levels. Numerous machines require manual operation and logging, causing poor data timeliness, an inability to monitor process flows in real time, and a weak big data foundation that constrains intelligent upgrades.

Product Empowerment Value

Value 1: Utilizing APS + MES to achieve precise scheduling and real-time monitoring, providing complete visibility into order progress, equipment status, and WIP distribution to support accurate decision-making and efficiency gains.

Value 2: Transforming operator experience into enterprise assets through RMS, MES, and QMS, establishing a comprehensive traceability chain to satisfy client fab audit requirements.

Value 3: Achieving automated interfacing and data collection for key equipment via EAP to minimize manual intervention; combined with the big data platform, this provides a data foundation for AI innovation applications, supporting process optimization and efficiency improvement.

Photovoltaics

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: Process iteration is rapid, production is highly scaled, and equipment is dense with diverse brands and models, making traceability accuracy and stability difficult to guarantee.

Pain Point 2: Fierce industry competition drives stringent demands for conversion efficiency and yield.

Pain Point 3: Synchronization among process parameters, quality inspection standards, and production plans is poor, and exploration of intelligent manufacturing remains immature.

Product Empowerment Value

Value 1: Utilizing a low-code process configuration platform and hierarchical control system to achieve rapid introduction of new processes and precise management of multi-process line sharing; combining physical and virtual dual-code error proofing with AGV data supplementation.

Value 2: Connecting end-to-end data links across production, equipment, and quality to achieve process-equipment-yield interlocked analysis, rapidly pinpointing yield root causes and reducing unplanned downtime and quality losses.

Value 3: Integrating LOFA and the big data intelligent engine to construct automated linkage among process, quality inspection, and production, transferring mature semiconductor technologies down to photovoltaic lines to support process optimization, intelligent judgment, and predictive scheduling.

Intelligent Equipment Software

Industry Pain Points

Product Empowerment Value

Industry Pain Points

Pain Point 1: High costs, long delivery times, and delayed after-sales response. Core components depend on imports, creating supply chain uncertainties.

Pain Point 2: Manual inspection is inefficient, costly, and inconsistent. High miss rates, manual paper based logs for traceability.

Pain Point 3: Existing equipment lacks the functionality to cover the entire inspection workflow. It cannot simultaneously perform fastscanning, microscopic review, and compositional analysis. Heterogeneous interfaces across multi-brand equipment furthercomplicate automation integration.

Product Empowerment Value

Value 1: Shortened delivery cycle; 24-hour localized after-sales service; reduced reliance on imported supply chains.

Value 2: Macro inspection speed ≤300 mm/s; elevated data traceability efficiency; LOFA enables multi-machine management per operator or fully unmanned operation; defect positioning accuracy ≤±0.8 μm.

Value 3: AI-OM integrates LIBS composition analysis, FlashSight achieves integrated "macro + micro" inspection, and LOFA is compatible with mainstream machine brands for rapid adaptation without hardware modification.

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