Achieving optimal parameter settings in epitaxial silicon carbide (Epi SiC) manufacturing is challenging due to the need to simultaneously meet conflicting multi-objective requirements, such as precise thickness control and uniform doping. This paper presents an extended Digital Twin (DT) framework that incorporates the Multi-Objective Optimization with Deep-Feature Gaussian Process (MOODFG) algorithm, explicitly extending our prior DT-in-the-loop framework (MRBORI) in [26] from single-objective tuning to spec-driven multi-objective recipe optimization. The framework enables the effective optimization of high-dimensional and interdependent process parameters, overcoming limitations of traditional methods. The key innovation of the MOODFG algorithm lies in its integration of deep feature extraction and Gaussian Process Regression, which allows dynamic refinement of feature representations and surrogate models through iterative learning. Unlike MRBORI [26] (one objective per run), MOODFG minimizes a weighted distance to a user-specified target vector (e.g., thickness and doping) and provides an uncertainty-aware target-attainment certificate with a practical stopping rule for deployment. This approach ensures robust convergence to optimal parameter values while efficiently balancing multiple objectives. Experimental validation using real-world Epi SiC manufacturing data demonstrates significant improvements in yield, parameter stability, and process adaptability, highlighting the framework’s transformative potential for semiconductor manufacturing.

High-precision wafer metrology poses significant cost and throughput challenges in modern semiconductor manufacturing, where frequent process changes and recipe variations demand highly adaptive and scalable solutions. In this paper, we present a Generative-FewShot-Active Virtual Metrology (GFA-VM) framework that unifies large-scale generative modeling, few-shot fine-tuning, and uncertainty-driven active sampling into a single, data-centric system. A foundational generative model, built on a hybrid architecture of Transformer networks and Variational Autoencoders (VAEs), learns diverse sensor characteristics in an offline stage without relying on extensive labeled data. During online inference, the model produces both wafer quality predictions and predictive uncertainties; samples exceeding a dynamic uncertainty threshold are selected for physical measurement and few-shot model recalibration. This selective sampling both reduces measurement costs and adapts rapidly to new process conditions (e.g., novel recipes or equipment upgrades), requiring only a handful of freshly labeled wafers. The paper further addresses the long-term stability of the system through a self-updating mechanism that adjusts the uncertainty threshold when distributional shifts occur. Empirical evaluations confirm that our GFA-VM approach achieves state-of-the-art accuracy while significantly reducing metrology overhead compared to conventional virtual metrology methods. Additionally, rigorous theoretical analyses—including proofs of convergence and label cost bounds—demonstrate the reliability of using a generative foundation plus meta-learning technique. By fostering on-demand adaptation within a closedloop framework, GFA-VM offers a comprehensive, scalable strategy for next-generation semiconductor process control.

Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework (iVPPF) to address this gap. iVPPF comprises a central iVPP (iVPP$_{\mathrm {C}}$) and several regional iVPPs (iVPP$_{\mathrm {n}}$), n = E, S, M, and N. These iVPPn are geographically distributed systems for intelligently managing iVPPs in four regions: east, south, middle, and north, respectively, while the iVPPC is responsible for dispatching power across iVPPn. We built iVPPC and iVPPn on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for iVPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for iVPPn. iVPPn can intelligently control DERs based on the collected data. Also, iVPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate iVPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that iVPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed iVPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. Note to Practitioners—This paper proposes an intelligent virtual power plant framework$({i} \text { VPPF})$consisting of a central coordinator$({i}\text {VPP}_{\text {C}})$and distributed regional managers (${i}\text {VPP}_{\text {n}}$for East, South, Middle, and North). Leveraging green power clouds, both${i} \text { VPP}_{\text {C}}$and${i}\text {VPP}_{\text {n}}$harness AI for intelligent management and power dispatch across regions. We detail the system architecture and showcase practical applications, including scenarios like dispatching and aggregating for demand response, using the IEEE 13-node test feeder. Additionally, we explore the design of green power clouds and cyber-physical agents (CPAs).

Industry 4.0 is accelerating, with manufacturing enterprises embracing digital transformation and intelligent manufacturing (iM) to enhance competitiveness. Manufacturing enterprises must also improve their environmental performance to meet the goal of net zero by 2050 and to avoid border carbon tax imposed by large economies. Thus, green intelligent manufacturing (GiM), i.e., conducting iM while pursuing to maximize energy conservation and carbon reduction, has become the key development trend and a fundamental challenge for the modern manufacturing industry. To address the challenges of green intelligent manufacturing (GiM), this paper proposes a novel framework called Industry 4.2 for GiM (I4.2-GiM). This framework builds on the Intelligent Factory Automation ($i$FA) platform, which the authors developed to achieve zero-defect manufacturing (i.e., Industry 4.1). I4.2-GiM uses various IoT devices, called Cyber-Physical Agents (CPAs), to collect and integrate large amounts of data. It also includes two interrelated systems, an intelligent carbon emission management system ($i$CMS) and an intelligent energy management system ($i$EMS), simultaneously tackling carbon reduction and energy saving. Existing factory EMSs typically save less than 10% of energy, but I4.2-GiM has been shown to conserve 10.9% of energy and reduce carbon emissions by 12.55% while conducting iM in daily production. I4.2-GiM is a promising new framework that can help manufacturing enterprises approach net zero intelligently.Note to Practitioners—This paper proposes a new green intelligent manufacturing (GiM) framework called I4.2-GiM (Industry 4.2 for GiM). I4.2-GiM builds on the Intelligent Factory Automation ($i$FA) platform, which the authors developed to achieve zero-defect manufacturing (i.e., Industry 4.1). I4.2-GiM also uses various IoT devices called Cyber-Physical Agents (CPAs) to collect data from multiple sources. It also includes two interrelated systems, an intelligent carbon emission management system (iCMS) and an intelligent energy management system (iEMS), to address carbon reduction and energy saving simultaneously. Moreover, this paper provides a systematic implementation procedure and several practical examples to help practitioners adopt the designs and niches of I4.2-GiM and build their desired GiM systems for reaching the goal of net zero.

Many core technologies of Industry 4.0 have gained substantial advancement in recent years. Digital Twin (DT) has become the key technology and tool for manufacturing industries to realize intelligent cyber-physical integration and digital transformation by leveraging these technologies. Although there have been many DT-related works, there is no standard definition, unified framework, and implementation approach of DT until now. Widely developing DTs for the manufacturing industry is still challenging. Thus, this paper proposes a novel implementation framework of digital twins for intelligent manufacturing, denoted as IF-DTiM, which possesses several distinct merits to distinguish itself from previous works. First, IF-DTiM fully utilizes new-generation container technology so that DT-related applications and services can be packaged in a self-contained way, rapidly deployed, and robustly operated with the capabilities of failover, autoscaling, and load balancing. Second, it leverages existing intelligent cloud manufacturing services to realize the intelligence for DT externally in a scalable and plug-and-play manner instead of using traditional approaches to embed intelligence in DT. Third, IF-DTiM contains Product DT for products, Equipment DT (i.e., EQ DT) for equipment, and Process DT for production lines, which can generically fulfill the demands and scenarios to achieve intelligent manufacturing for various manufacturing industries. Testing results show that IF-DTiM can achieve remarkable performance in rapid deployment and real-time data exchanges of DT-related applications. Finally, we develop an example DTiM system for CNC machining based on IF-DTiM to demonstrate its efficacy and applicability in facilitating the manufacturing industry to build their DT systems.Note to Practitioners—Developing Digital Twin (DT) systems to realize intelligent manufacturing is challenging. The proposed IF-DTiM (Implementation Framework of Digital Twins for Intelligent Manufacturing) provides a novel container-technology and cloud-manufacturing-service-based systematic methodology for building DTiM. In this paper, we present the system architecture and several operational scenarios (e.g., how to create and use DTs) of IF-DTiM, together with the design of its core functional mechanisms (e.g., rapid deployment scheme for DT, real-time data exchange for DT, DT interface pattern, and general workflow architecture for DT). Also, an example DTiM system for CNC machining based on IF-DTiM is presented to facilitate the practitioners to adopt the designs and niches in IF-DTiM to build their desired DTiM systems.