Three-dimensional integrated circuit (3D IC) pack-aging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as thermal hot spots, warpage-induced stresses, and interconnect aging demands monitoring and control capabilities that surpass traditional offline metrology. Although Digital Twin (DT) technology provides a principled route to real-time reliability management, the existing literature remains fragmented and frequently blurs the distinction between static multiphysics simulation workflows and truly dynamic, closed-loop twins. This critical review distinguishes itself by addressing these deficiencies through three specific contributions. First, we clarify the Digital Twin hierarchy to resolve terminological ambiguity between digital models, shadows, and twins. Second, we synthesize three foundational enabling technologies: (1) physics-based modeling, emphasizing the shift from computationally intensive finite-element analysis (FEA) to real-time surrogate models; (2) data-driven paradigms, highlighting virtual metrology (VM) for inferring latent metrics; and (3) in-situ sensing, the nervous system coupling the physical stack to its virtual counterpart. Third, beyond a descriptive survey, we propose a unified hybrid DT architecture that leverages physics-informed machine learning (e.g., PINNs) to reconcile data scarcity with latency constraints. Finally, we outline a standards-aligned roadmap incorporating IEEE 1451 and UCIe protocols to accelerate the transition from passive digital shadows to autonomous, self-optimizing Digital Twins for 3D IC manufacturing and field operation.

翻译：三维集成电路（3D IC）封装与异质集成已成为当代半导体微缩化的核心支柱。然而，堆叠架构固有的多物理场耦合——表现为热热点、翘曲诱导应力及互连老化——要求超越传统离线计量学的监测与控制能力。尽管数字孪生（DT）技术为实时可靠性管理提供了原理性路径，现有文献仍显零散，且常混淆静态多物理场仿真流程与真正动态、闭环的孪生体之间的界限。本批判性综述通过三项具体贡献来弥补这些不足：首先，我们厘清数字孪生层级，以消解数字模型、数字影子和数字孪生间的术语模糊性。其次，我们综合三项基础使能技术：（1）基于物理的建模，强调从计算密集型有限元分析（FEA）向实时代理模型的转变；（2）数据驱动范式，着重于推断潜在指标的虚拟计量学（VM）；（3）原位传感，作为连接物理堆叠与其虚拟对应体的“神经系统”。第三，超越描述性综述，我们提出一种统一的混合数字孪生架构，利用物理信息机器学习（如PINNs）在数据稀缺与延迟约束间取得平衡。最后，我们勾勒出一条符合IEEE 1451与UCIe协议标准的路线图，以加速从被动数字影子向自主、自优化的数字孪生体过渡，服务于3D IC制造与现场运维。