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市場調查報告書
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2114188

生成式人工智慧硬體材料市場（2027-2037 年）

The Generative AI Hardware Materials Market 2027-2037
出版日期: | 出版商: Future Markets, Inc. | 英文 562 Pages, 145 Tables, 48 Figures | 訂單完成後即時交付

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生成式人工智慧硬體材料市場涵蓋用於建構人工智慧訓練和推理系統的材料和組件，從基板和氣體等原料到成品伺服器機架，應有盡有。市場需求主要受超大規模資料中心業者資料中心營運商、企業和政府對人工智慧資料中心的資本投資以及邊緣人工智慧運算佔有率不斷成長的推動。

系統性能受限於一系列物理約束，而非模型設計。計算吞吐量受限於光罩面積和電晶體密度。記憶體頻寬受限於HBM堆疊高度和引腳寬度。互連頻寬受限於銅線中的訊號衰減（每通道超過約224 Gbps）。散熱受限於熱界面導熱係數和冷卻液流速。電源受限於IR壓降和穩壓器效率。針對每項約束的進展都取決於特定材料和封裝技術的開發，因此決定了該層增強人工智慧運算能力的速度。

這個市場由九個層面構成：原料、氣體和光阻劑、功率半導體和電源、導熱材料和冷卻、光電封裝、基板和中介層、先進封裝、HBM 和儲存子系統、用於 AI 加速器的矽以及 AI 伺服器和機架。與之相關的，資料中心建造供應鏈作為一個獨立的細分市場，涵蓋電力基礎設施、冷卻設備、建築、工程服務等。

各細分市場的成長率差異顯著。用於人工智慧加速器的矽晶片是最大的細分市場，但其成長速度低於其他幾個細分市場。由於堆疊高度的增加以及CoWoS級封裝產能的持續緊張，預計HBM和先進封裝在預測期內的市場佔有率將有所成長。儘管基數較小，但用於資料中心的光電封裝和功率半導體將實現最快成長。這是因為共封裝光學元件正進入量產階段，且機架功率等級將超過300千瓦。冷卻方式正從風冷轉向直接液冷和浸沒式冷卻，到2030年，這兩種冷卻方式將佔新人工智慧部署的60%以上。

對供應商和買家而言，有兩個結構性特徵至關重要。首先是地域集中度。台灣、韓國和日本佔了最先進的矽晶片、記憶體、封裝、基板和特殊材料的大部分產能，其中CoWoS級封裝製程的產能限制最為嚴格。其次是出口限制的影響。這在中國催生了一條獨立的硬體供應鏈，擁有自己的供應商和製程節點限制。兩者之間的產能差距在小規模模型推理領域縮小速度最快，而在最先進的模型訓練領域縮小速度最慢。

報告包含以下資訊：

  • 摘要整理- 主要發現、硬體瓶頸、價值鏈圖以及相關人員。
  • 運算堆疊——訓練和推理的經濟性、雲端運算、邊緣運算和自主人工智慧，以及記憶體和封裝的成本權重。
  • 用於 AI 加速器的矽晶片 - GPU、用於超大規模資料中心業者的ASIC、替代架構、中國生態系統、晶圓代工廠藍圖和晶圓級整合。
  • 人工智慧驅動的晶片設計（EDA）－現有供應商舉措、新創公司和人工智慧EDA市場預測
  • 高頻寬記憶體及未來發展方向－HBM藍圖及產能、客製化HBM、記憶體運算、CXL池化、3D DRAM
  • 先進封裝和基板——2.5D 和 3D 整合、CoWoS 生產能力、ABF 和 FC-BGA基板、中介層、混合鍵結、OSAT 生產能力
  • 共封裝光學元件和矽光電－CPO架構、光I/O晶片、光電晶圓代工廠和封裝供應鏈
  • 溫度控管－導熱界面材料、均熱板、冷板、浸沒式冷卻和微流體冷卻
  • 電源及向GaN/SiC過渡－48V和800V高壓直流架構、SiC和GaN元件及基板供電、VRM、伺服器電源、背面電源
  • 網路和光材料－用於交換器、插件收發器、DSP 和 SerDes 的矽材料、III-V 族材料、網卡、資料處理單元、連接器
  • 資料中心建設－包括電力基礎設施（SMR）、設施冷卻、模組化設計和位置。
  • Edge GenAI 硬體 - 與人工智慧驅動的智慧型手機和 PC、汽車晶片、機器人運算和邊緣加速器相關的新創公司。
  • 區域分析——台灣、韓國、日本、中國、東南亞、美國、歐洲、以色列
  • 供應鏈與地緣政治－中國國內供應鏈分析、「晶片安全與資訊安全法」實施、關鍵零件及單點故障（SPOF）
  • 永續性和植入式碳排放—營運排放、製造植入式碳排放、用水量、可再生能源採購和資訊揭露標準
  • 市場預測與策略展望－細分市場、區域市場與客戶細分市場預測、瓶頸分析、併購與投資框架
  • 附錄 -調查方法、定義、簡稱、資料資訊來源和年度預測結果

重點介紹的公司包括：1X Technologies, 3M, Acbel Polytech, Accelink Technologies, Achronix Semiconductor, Advanced Micro Devices (AMD), AGC (Asahi Glass), Agility Robotics, AheadComputing, Ajinomoto FineTechno (ABF), Akhan Semiconductor, Alibaba THead (PingTouGe), Alpha Assembly Solutions (MacDermid Alpha), Alphabet Inc. (Google), Amazon Web Services (AWS), Ambarella, Amber Semiconductor (AmberSemi), AMD, Amkor Technology, Amphenol Corporation, Anduril Industries, Apple Inc., Applied Materials, Apptronik, Arago, ASE Group, ASE Technology Holding (incl. SPIL), Asetek, Asia Vital Components (AVC), ASMPT, Asperitas, Astera Labs, Astrus, AT&S (Austria Technologie & Systemtechnik), Auras Technology, Avalanche Technology, Axelera AI, Axera Technology, AXT Inc., Ayar Labs, BE Semiconductor Industries (BESI), Biren Technology, Black Sesame Technologies, Blaize, Broadcom Inc., Cambricon Technologies, Cambridge GaN Devices (CGD), Carbice Corporation, Celero Communications, Cerebras Systems, Chemours Company, ChipAgents, Chipmind, ChipMOS Technologies, Chiral, Ciena, Cisco Systems, Claros, Coherent Corp., ColorChip, Cooler Master Co., CoolIT Systems, CoreWeave Inc., Corintis, Corning Incorporated, Crossbar Inc., Crusoe Energy Systems, CXMT (ChangXin Memory Technologies), DEEPX, Delta Electronics, d-Matrix, DOW Inc., Dust Photonics, Eaton Corporation, EdgeCortix, EFFECT Photonics, Efficient Computer, Efficient Power Conversion (EPC), Element Six (e6), Eliyan, Empower Semiconductor, Engineered Fluids, Eoptolink Technology, Eridu, Etched.ai, Ethernovia, EuQlid, EV Group (EVG), Everspin Technologies, Fabric8Labs, Fabrinet, Femtum, Ferroelectric Memory Company (FMC), Figure AI, Fourier Intelligence, Foxconn Industrial Internet (FII), Foxconn Interconnect Technology (FIT), Frore Systems, FSP Group, Fujipoly, Furiosa AI, G42, Gaianixx, Galatek, Gigalight, Google, Great Sky, Green Revolution Cooling (GRC), GreenWaves Technologies, Groq Inc., GS Microelectronics (GSME), Hailo Technologies, Henkel AG, Heraeus, Hesheng Silicon Industry, Hisense Broadband, HiSilicon (Huawei), Hitachi Energy, Hon Hai (Foxconn), Honeywell International, Horizon Robotics, Hua Tian Technology (HT-Tech), Huawei Technologies, Huawei Technologies (HiSilicon), Hummink, Ibiden Co. Ltd., Iceotope Technologies, Iluvatar CoreX, Indium Corporation, Infineon Technologies AG, Innolight Technology, Innoscience Technology, Intel, Intel Corporation, Intel Foundry, IQE plc, JCET Group, JetCool Technologies, Kandou AI, Kaneka Corporation, Kinsus Interconnect Technology, Kioxia Holdings, Kneron, Kulicke & Soffa Industries (K&S), Kyocera Corporation等。

第1章：摘要整理

第2章：生成過程背後的計算棧

  • 訓練經濟學與推論經濟學的比較
  • 雲端運算、邊緣運算和自主人工智慧
  • 為什麼記憶體頻寬和封裝會影響成本
  • 材料和零件成為新的瓶頸。
  • 超大規模資料中心業者企業 vs. 企業 vs. 主權資本支出
  • 43 家公司簡介（9 家公司簡介）

第3章：人工智慧加速矽

  • GPU
  • 客製化超大規模資料中心業者積體電路
  • 領域特定架構與挑戰者架構
  • 中國的AI晶片生態系統
  • 製程節點和晶圓代工廠藍圖
  • 晶圓級整合與光罩拼接
  • 70 份公司簡介（53 家公司的簡介）

第4章：基於人工智慧的晶片設計（EDA）

  • 人工智慧硬體時代的EDA瓶頸
  • 遞歸循環：為不同的AI系統設計AI硬體
  • 現有EDA供應商的AI舉措
  • 創業孵化器：四種不同的方法
  • 地理分佈
  • 市場預測：AI-EDA 工具 2026-2037 年
  • 戰略意義
  • 129 家公司簡介（6 家公司的簡介）

第5章：高頻寬記憶體及其他

  • HBM架構和TSV堆疊的基本原理
  • HBM 代際藍圖
  • 記憶體製造商和產能預測
  • 客製化HBM（cHBM）和基板創新
  • 大規模記憶體內運算和記憶體內處理
  • 面向人工智慧資料中心的下一代記憶體
  • 內存池和 CXL 織物
  • 3D DRAM－2030 年及以後的展望
  • 153 份公司簡介（23 家公司的簡介）

第6章：先進封裝與基板

  • 2.5D/3D建築連續體
  • 台積電CoWoS及產能限制
  • 英特爾和三星的先進封裝技術
  • 基板技術（ABF、FC-BGA）
  • 中介層材料（矽TSV、玻璃、有機RDL）
  • 混合鍵結和銅-銅互連
  • OSAT 的能力和亞洲的優勢
  • 先進包裝材料供應商
  • 188 家公司簡介（56 家公司簡介）

第7章：用於人工智慧的光學元件和矽光電的共封裝

  • 光連接模組的必要性
  • CPO架構和兩個網路層
  • 台積電小轎車、CoWoS- 光電、iOIS
  • ASE VIPack 和 Merchant Photonics 封裝層
  • 光學 I/O 晶片：AyarLabs、Lightmatter、Celestial AI
  • 用於開關和整合光引擎的矽
  • 矽光電晶圓代工廠
  • 光電封裝材料和供應鏈
  • 光電市場規模預測（2026-2037）
  • 253 份公司簡介（共 28 家公司簡介）

第8章：人工智慧資料中心的溫度控管

  • 熱危機：封裝級功率密度
  • 導熱界面材料（TIM）
  • 散熱器、蒸氣室和熱管
  • 晶片的冷板和直連式液冷
  • 浸沒式冷卻
  • 微流體冷卻和封裝內冷卻
  • 市場預測：人工智慧驅動的溫度控管2024-2037 年
  • 292 家公司簡介（40 家公司的簡介）

第9章 電源和GAN/SIC轉換

  • 電力危機：從12V到48V，再到800V高壓直流輸電
  • 電源層級：系統 → 電路板 → 封裝 → 晶片
  • SiC裝置和基板供應
  • 氮化鎵裝置：水平型、垂直型和級聯型
  • 電壓調節模組和多相負載點
  • 伺服器電源單元和機架整流器機架
  • 背面電源（BSPDN）
  • 市場預測：人工智慧資料中心功率半導體市場（2024-2037 年）
  • 343 份公司簡介（42 家公司的簡介）

第10章：網路與光學材料

  • 人工智慧資料中心的三層網路
  • 切換矽藍圖
  • 插入式光收發器
  • 用於光收發器的DSP和SerDes
  • III-V族材料層：InP、GaAs、GaN光電
  • 網路卡、DPU 和智慧網卡
  • 電纜、連接器和直連銅​​線
  • 市場預測：人工智慧整合網路和光纖通訊2024-2037
  • 394 家公司簡介（36 家公司簡介）

第11章：資料中心建置與永續性

  • 建構人工智慧資料中心：規模與範圍
  • 電力基礎設施：電網、現場發電、小型模組化反應器（SMR）
  • 設施級冷卻架構
  • 建築供應鍊和模組化資料中心架構
  • 地理集中度和位置選擇
  • PUE、WUE 和永續性指標
  • 法規結構
  • 市場預測：人工智慧資料中心建置供應鏈（2024-2037）

第12章 邊緣產生人工智慧硬體

  • 邊緣人工智慧分類系統
  • 人工智慧智慧型手機
  • AI PC
  • NVIDIA Jetson 與嵌入式 AI 平台
  • 汽車人工智慧矽
  • 人形機器人：人工智慧運算的前沿
  • 邊緣人工智慧加速器新創公司
  • 邊緣AI記憶體：LPDDR5X、片上SRAM、eMRAM
  • 市場預測：邊緣人工智慧晶片 2024-2037
  • 450 份公司簡介（51 家公司的簡介）

第13章 區域分析：產生式人工智慧硬體供應鏈的區域分佈

  • 亞洲集中度
  • 台灣
  • 韓國
  • 日本
  • 中國
  • 東南亞和印度
  • 美國
  • 歐洲和以色列
  • 其他地區：小眾技能與國家抱負
  • 區域總收入：情境分析（2026-2037 年）

第14章：供應鏈與地緣政治

  • 關鍵張力
  • 中國的戰略：主權堆疊與國內替代
  • 美國「晶片製造和生產法案」（CHIPS Act）的實施以及恢復國內生產
  • 歐洲小費法和戰略自主權
  • 重要材料層
  • 單點失效分析
  • 供應鏈韌性情景
  • 利用自主人工智慧作為策略需求促進因素。

第15章永續性與內生碳

  • 關於永續性的利益衝突
  • 運行排放：訓練、推理和冷卻能源稅
  • 半導體製造過程中的碳排放
  • 水、化學品和資源強度
  • 超大規模資料中心業者可再生能源採購
  • 熱回收、循環經濟和報廢產品
  • 碳計量標準與企業資料
  • 主要供應商的環保生產實踐
  • 市場與監管展望（2026-2037 年）

第16章 市場預測：生成式人工智慧與硬體（2026-2037 年）

  • 預測性調查方法與框架
  • GenAI硬體市場概況 - 基準情境預測
  • 整體層面的多頭市場/底部/熊市情景
  • 對人工智慧加速器矽子領域的預測
  • HBM 和記憶子細分市場預測
  • 先進包裝細分市場預測
  • 光電封裝細分市場預測
  • 溫度控管子領域的預測
  • 電源供應子領域預測
  • 網路和光纖通訊細分市場預測
  • 資料中心建置供應鏈子領域預測
  • 邊緣人工智慧矽細分市場預測
  • 區域擷取預測
  • 客戶細分預測
  • 關鍵預測風險和敏感度

第17章 戰略展望

  • GenAI硬體十年來的五大主題
  • 咽喉要道圖
  • 戰略投資框架
  • 併購和策略整合的現狀
  • 敏感度分析
  • 相關人員的策略影響
  • 可能改變這項預測的因素

第18章附錄

The generative AI hardware materials market covers the materials and components used to build AI training and inference systems, from raw substrates and gases through to finished server racks. Demand is driven by hyperscaler, enterprise and sovereign capital expenditure on AI datacentres, and by the growing share of AI compute performed at the edge.

System performance is limited by a set of physical constraints rather than by model design. Compute throughput is limited by reticle area and transistor density. Memory bandwidth is limited by HBM stack height and pin width. Interconnect bandwidth is limited by signal attenuation in copper traces above roughly 224 Gbps per lane. Heat removal is limited by thermal interface conductivity and coolant flow rate. Power delivery is limited by IR drop and voltage regulator efficiency. Progress against each constraint depends on a specific materials or packaging development, which is why this layer determines how quickly AI compute capacity can be added.

The market is structured in nine layers: raw materials, gases and photoresists; power semiconductors and delivery; thermal materials and cooling; photonics packaging; substrates and interposers; advanced packaging; HBM and the memory subsystem; AI accelerator silicon; and AI servers and racks. A related but separate segment is the datacentre construction supply chain, covering power infrastructure, cooling plant, buildings and engineering services.

Growth rates differ substantially by layer. AI accelerator silicon is the largest segment but grows more slowly than several of the layers below it. HBM and advanced packaging gain share over the forecast period, as stack heights increase and CoWoS-class packaging capacity remains tight. Photonics packaging and datacentre power semiconductors grow fastest from a smaller base, as co-packaged optics moves into volume production and rack power levels rise above 300 kW. Cooling shifts away from air: direct liquid cooling and immersion account for more than 60% of new AI deployments by 2030.

Two structural characteristics are relevant to suppliers and buyers. The first is geographic concentration. Taiwan, South Korea and Japan account for most leading-edge silicon, memory, packaging, substrate and specialty materials capacity, and CoWoS-class packaging is the most constrained single step. The second is the effect of export controls, which have led to a separate hardware supply chain in China with its own suppliers and process node limits. The capability gap between the two is narrowing fastest in small-model inference and slowest in frontier model training.

Report contents include:

  • Executive summary - headline findings, the hardware bottleneck, value-chain map and implications by stakeholder
  • The compute stack - training and inference economics, cloud, edge and sovereign AI, and the cost weighting of memory and packaging
  • AI accelerator silicon - GPUs, hyperscaler ASICs, alternative architectures, the Chinese ecosystem, foundry roadmaps and wafer-level integration
  • AI-driven chip design (EDA) - incumbent vendor initiatives, the startup cohort and AI-EDA market forecasts
  • High bandwidth memory and beyond - HBM roadmap and capacity, custom HBM, compute-in-memory, CXL pooling and 3D DRAM
  • Advanced packaging and substrates - 2.5D and 3D integration, CoWoS capacity, ABF and FC-BGA substrates, interposers, hybrid bonding and OSAT capacity
  • Co-packaged optics and silicon photonics - CPO architecture, optical I/O chiplets, photonics foundries and packaging supply chain
  • Thermal management - thermal interface materials, vapour chambers, cold plates, immersion and microfluidic cooling
  • Power delivery and the GaN/SiC transition - 48V and 800V HVDC architectures, SiC and GaN device and substrate supply, VRMs, server PSUs and backside power delivery
  • Networking and optical materials - switch silicon, pluggable transceivers, DSP and SerDes, III-V materials, NICs, DPUs and connectors
  • Datacentre construction - power infrastructure including SMRs, facility cooling, modular designs and site selection
  • Edge GenAI hardware - AI smartphones and PCs, automotive silicon, robotics compute and edge accelerator start-ups
  • Regional analysis - Taiwan, South Korea, Japan, China, Southeast Asia, the United States, Europe and Israel
  • Supply chain and geopolitics - China's domestic supply chain, CHIPS Act implementation, critical materials and single-point-of-failure analysis
  • Sustainability and embodied carbon - operational emissions, fab embodied carbon, water use, renewable procurement and disclosure standards
  • Market forecasts and strategic outlook - segment, regional and customer-tier forecasts, choke-point analysis, M&A and investment framework
  • Appendix - methodology, definitions, abbreviations, sources and year-by-year forecast outputs

Companies profiled include 1X Technologies, 3M, Acbel Polytech, Accelink Technologies, Achronix Semiconductor, Advanced Micro Devices (AMD), AGC (Asahi Glass), Agility Robotics, AheadComputing, Ajinomoto FineTechno (ABF), Akhan Semiconductor, Alibaba THead (PingTouGe), Alpha Assembly Solutions (MacDermid Alpha), Alphabet Inc. (Google), Amazon Web Services (AWS), Ambarella, Amber Semiconductor (AmberSemi), AMD, Amkor Technology, Amphenol Corporation, Anduril Industries, Apple Inc., Applied Materials, Apptronik, Arago, ASE Group, ASE Technology Holding (incl. SPIL), Asetek, Asia Vital Components (AVC), ASMPT, Asperitas, Astera Labs, Astrus, AT&S (Austria Technologie & Systemtechnik), Auras Technology, Avalanche Technology, Axelera AI, Axera Technology, AXT Inc., Ayar Labs, BE Semiconductor Industries (BESI), Biren Technology, Black Sesame Technologies, Blaize, Broadcom Inc., Cambricon Technologies, Cambridge GaN Devices (CGD), Carbice Corporation, Celero Communications, Cerebras Systems, Chemours Company, ChipAgents, Chipmind, ChipMOS Technologies, Chiral, Ciena, Cisco Systems, Claros, Coherent Corp., ColorChip, Cooler Master Co., CoolIT Systems, CoreWeave Inc., Corintis, Corning Incorporated, Crossbar Inc., Crusoe Energy Systems, CXMT (ChangXin Memory Technologies), DEEPX, Delta Electronics, d-Matrix, DOW Inc., Dust Photonics, Eaton Corporation, EdgeCortix, EFFECT Photonics, Efficient Computer, Efficient Power Conversion (EPC), Element Six (e6), Eliyan, Empower Semiconductor, Engineered Fluids, Eoptolink Technology, Eridu, Etched.ai, Ethernovia, EuQlid, EV Group (EVG), Everspin Technologies, Fabric8Labs, Fabrinet, Femtum, Ferroelectric Memory Company (FMC), Figure AI, Fourier Intelligence, Foxconn Industrial Internet (FII), Foxconn Interconnect Technology (FIT), Frore Systems, FSP Group, Fujipoly, Furiosa AI, G42, Gaianixx, Galatek, Gigalight, Google, Great Sky, Green Revolution Cooling (GRC), GreenWaves Technologies, Groq Inc., GS Microelectronics (GSME), Hailo Technologies, Henkel AG, Heraeus, Hesheng Silicon Industry, Hisense Broadband, HiSilicon (Huawei), Hitachi Energy, Hon Hai (Foxconn), Honeywell International, Horizon Robotics, Hua Tian Technology (HT-Tech), Huawei Technologies, Huawei Technologies (HiSilicon), Hummink, Ibiden Co. Ltd., Iceotope Technologies, Iluvatar CoreX, Indium Corporation, Infineon Technologies AG, Innolight Technology, Innoscience Technology, Intel, Intel Corporation, Intel Foundry, IQE plc, JCET Group, JetCool Technologies, Kandou AI, Kaneka Corporation, Kinsus Interconnect Technology, Kioxia Holdings, Kneron, Kulicke & Soffa Industries (K&S), Kyocera Corporation and more.....

1 EXECUTIVE SUMMARY

  • 1.1 Key Findings
  • 1.2 The Generative AI Hardware Bottleneck
  • 1.3 Materials Value Chain at a Glance
  • 1.4 Eleven-Year Forecast Highlights
  • 1.5 Strategic Implications for Asian Foundries, OSAT, Memory, Substrate, and Cooling Vendors
  • 1.6 Differentiation vs. Adjacent Coverage
  • 1.7 Major Market Players

2 THE COMPUTE STACK BEHING GENERATIVE

  • 2.1 Training vs. Inference Economics
    • 2.1.1 Pre-training, post-training, RLHF compute splits
    • 2.1.2 Inference token economics and serving infrastructure
    • 2.1.3 Test-time compute and reasoning-model demand
  • 2.2 Cloud, Edge, and Sovereign AI
    • 2.2.1 Hyperscaler clusters at 100,000-GPU scale
    • 2.2.2 Enterprise on-prem and neocloud deployments
    • 2.2.3 Sovereign AI build-outs
    • 2.2.4 Edge inference cross-reference
  • 2.3 Why Memory Bandwidth and Packaging Dominate Cost
    • 2.3.1 The memory wall in LLM serving
    • 2.3.2 HBM ASP as percentage of AI accelerator BOM
    • 2.3.3 CoWoS as the constraining bottleneck
  • 2.4 Materials and Components as the New Bottleneck
  • 2.5 Hyperscaler vs. Enterprise vs. Sovereign Capex
  • 2.6 Company Profiles 43 (9 company profiles)

3 AI ACCELERTOR SILICON

  • 3.1 GPUs
    • 3.1.1 NVIDIA roadmap: Hopper → Blackwell → Blackwell Ultra → Rubin → Rubin Ultra
    • 3.1.2 NVL72 rack architecture and post-Rubin scale-up
    • 3.1.3 AMD MI300X → MI355X → MI400 trajectory
    • 3.1.4 Intel Gaudi and the post-Gaudi roadmap
  • 3.2 Custom Hyperscaler ASICs
    • 3.2.1 Google TPU v5/v6/v7 and ML supercomputer architecture
    • 3.2.2 AWS Trainium 2/3 and Inferentia
    • 3.2.3 Microsoft Maia and Cobalt
    • 3.2.4 Meta MTIA generations
    • 3.2.5 ASIC NRE economics and break-even analysis
  • 3.3 Domain-Specific and Challenger Architectures
    • 3.3.1 Cerebras WSE-3 wafer-scale
    • 3.3.2 Groq LPU deterministic inference
    • 3.3.3 SambaNova RDU and dataflow
    • 3.3.4 Tenstorrent, d-Matrix, Etched, Rivos, Lightmatter
  • 3.4 Chinese AI Chip Ecosystem
    • 3.4.1 Huawei Ascend 910C / 910D / 950
    • 3.4.2 Cambricon, Biren, Moore Threads, Iluvatar CoreX
    • 3.4.3 Alibaba T-Head Hanguang and PingTouGe
    • 3.4.4 Domestic substitution timeline to gen-on-gen parity
  • 3.5 Process Nodes and Foundry Roadmaps
    • 3.5.1 TSMC: N3 → N3P → N2 → N2P → A16 → A14
    • 3.5.2 Samsung Foundry: 3GAP → 2GAP → SF1.4
    • 3.5.3 Intel Foundry: 18A → 14A and external customer pipeline
    • 3.5.4 SMIC: N+1 / N+2 and the EUV-free 5nm question
    • 3.5.5 EUV and High-NA EUV adoption curves
  • 3.6 Wafer-Level Integration and Reticle Stitching
  • 3.7 Company Profiles 70 (53 company profiles)

4 AI-DRIVEN CHIP DESIGN (EDA)

  • 4.1 The EDA Bottleneck in the AI Hardware Era
  • 4.2 The Recursive Loop: AI Designing AI Hardware
  • 4.3 The Incumbent EDA Vendors' AI Initiatives
  • 4.4 The Startup Cohort: Four Distinct Approaches
    • 4.4.1 Agentic AI for digital design and verification
    • 4.4.2 Physics-AI for simulation and advanced packaging
    • 4.4.3 AI for analog and PCB design
    • 4.4.4 EDA-adjacent silicon and applied AI
  • 4.5 Geographic Distribution
  • 4.6 Market Forecast: AI-EDA Tools 2026-2037
  • 4.7 Strategic Implications
  • 4.8 Company profiles 129 (6 company profiles)

5 HIGH BANDWIDTH MEMORY AND BEYOND

  • 5.1 HBM Architecture and TSV Stacking Fundamentals
  • 5.2 HBM Generation Roadmap
    • 5.2.1 HBM3 / HBM3E specifications and deployment
    • 5.2.2 HBM4 / HBM4E: pin width doubling and base-die logic
    • 5.2.3 HBM5 / HBM5E: 2031-2037 architecture directions
  • 5.3 Memory Makers and Capacity Outlook
    • 5.3.1 SK hynix strategy, products, capex through 2030
    • 5.3.2 Samsung HBM3E re-qualification and HBM4 catch-up
    • 5.3.3 Micron HBM3E entry and AI customer share gains
    • 5.3.4 HBM bit-shipment and wafer-capacity forecasts
  • 5.4 Custom HBM (cHBM) and Base-Die Innovation
    • 5.4.1 Customer-specific HBM with NVIDIA, Broadcom, Google
    • 5.4.2 Standard vs custom HBM revenue split through 2030
  • 5.5 Compute-in-Memory and Processing-in-Memory at Scale
  • 5.6 Emerging Memory for AI Datacenters
    • 5.6.1 Storage-class memory after 3D XPoint
  • 5.7 Memory Pooling and CXL Fabrics
  • 5.8 3D DRAM - The Post-2030 Path
  • 5.9 Company Profiles 153 (23 company profiles)

6 ADVANCED PACKAGING AND SUBSTRATE MATERIALS

  • 6.1 The 2.5D / 3D Architecture Continuum
  • 6.2 TSMC CoWoS and the Capacity Constraint
    • 6.2.1 CoWoS-S, CoWoS-L, CoWoS-R roadmap
    • 6.2.2 CoWoS-Photonics and CoWoP
    • 6.2.3 CoWoS capacity expansion: 2024 vs. 2026 vs. 2028 vs. 2030
    • 6.2.4 SoIC, SoIC-X, SoIC-P: Hybrid-Bonded Stacks
  • 6.3 Intel and Samsung Advanced Packaging
    • 6.3.1 Intel: EMIB, EMIB-T, Foveros, Foveros Direct, Foveros Omni
    • 6.3.2 Samsung: I-Cube, X-Cube, H-Cube
  • 6.4 Substrate Technologies (ABF, FC-BGA)
    • 6.4.1 ABF supply oligopoly
    • 6.4.2 Glass core substrate (Intel, ASE, SCHOTT)
  • 6.5 Interposer Materials (Silicon TSV, Glass, Organic RDL)
  • 6.6 Hybrid Bonding and Copper-to-Copper Interconnect
    • 6.6.1 Hybrid bonding equipment ecosystem
    • 6.6.2 HBM4 adoption of hybrid bonding
  • 6.7 OSAT Capacity and Asian Dominance
  • 6.8 Advanced Packaging Materials Suppliers
  • 6.9 Company Profiles 188 (56 company profiles)

7 CO-PACKAGED OPTICS AND SILICON PHOTONICS FOR AI

  • 7.1 The Optical Interconnect Imperative
  • 7.2 CPO Architecture and the Two Network Layers
  • 7.3 TSMC COUPE, CoWoS-Photonics, iOIS
    • 7.3.1 TSMC photonics design ecosystem
    • 7.3.2 CoWoP and the NVIDIA Rubin transition
  • 7.4 ASE VIPack and the Merchant Photonics Packaging Layer
  • 7.5 Optical I/O Chiplets: AyarLabs, Lightmatter, Celestial AI
    • 7.5.1 AyarLabs TeraPHY
    • 7.5.2 Lightmatter Passage
    • 7.5.3 Celestial AI Photonic Fabric and the Marvell acquisition
  • 7.6 Switch Silicon and Co-Packaged Optical Engines
  • 7.7 Silicon Photonics Foundries
  • 7.8 Photonics Packaging Materials and Supply Chain
  • 7.9 Market Sizing for Photonics Packaging 2026-2037
  • 7.10 Company Profiles 253 (28 company profiles)

8 THERMAL MANAGEMENT FOR AI DATA CENTERS

  • 8.1 The Thermal Crisis: Power Density at the Package Level
  • 8.2 Thermal Interface Materials (TIMs)
    • 8.2.1 Liquid metal TIM and the gallium corrosion problem
    • 8.2.2 Solder TIM (indium and SnAg)
    • 8.2.3 Diamond-based TIMs and emerging materials
  • 8.3 Heat Spreaders, Vapor Chambers, and Heat Pipes
  • 8.4 Cold Plates and Direct-to-Chip Liquid Cooling
    • 8.4.1 Cold plate design and microchannel geometry
    • 8.4.2 The cold plate supply chain bottleneck
  • 8.5 Immersion Cooling
    • 8.5.1 Single-phase immersion: mineral oil and synthetic dielectrics
    • 8.5.2 Two-phase immersion: fluorocarbons and the PFAS challenge
  • 8.6 Microfluidic and In-Package Cooling
    • 8.6.1 Microfluidic ecosystem and the first commercial applications
    • 8.6.2 Coolant Distribution Units, Manifolds, and Facility Plumbing
  • 8.7 Market Forecast: AI-Tied Thermal Management 2024-2037
  • 8.8 Company Profiles 292 (40 company profiles)

9 POWER DELIVERY AND GAN/SIC TRANSITION

  • 9.1 The Power Crisis: From 12V to 48V to 800V HVDC
  • 9.2 The Power Hierarchy: System → Board → Package → Die
    • 9.2.1 48V tray architecture and the OCP standard
    • 9.2.2 800V HVDC at the rack and the Rubin transition
  • 9.3 SiC Devices and Substrate Supply
    • 9.3.1 SiC substrate supply: the bottleneck
  • 9.4 GaN Devices: Lateral, Vertical, Cascode
    • 9.4.1 GaN switching speed and AI server PSU applications
    • 9.4.2 Vertical GaN: the post-2027 trajectory
  • 9.5 Voltage Regulator Modules and Multi-Phase Point-of-Load
    • 9.5.1 The Monolithic Power Systems advantage in AI VRMs
    • 9.5.2 Vertical power delivery and the package-integrated VRM
  • 9.6 Server Power Supply Units and Rack Rectifier Shelves
  • 9.7 Backside Power Delivery (BSPDN)
    • 9.7.1 Intel PowerVia (18A)
    • 9.7.2 TSMC backside power (A16)
    • 9.7.3 Samsung BSPDN
  • 9.8 Market Forecast: AI Datacenter Power Semiconductors 2024-2037
  • 9.9 Company Profiles 343 (42 company profiles)

10 NETWORKING AND OPTICAL MATERIALS

  • 10.1 The Three Network Layers in an AI Datacenter
  • 10.2 Switch Silicon Roadmap
    • 10.2.1 Tomahawk 6 Davisson and the CPO inflection
    • 10.2.2 NVIDIA Spectrum-X and Quantum-X
    • 10.2.3 Ultra Ethernet Consortium (UEC)
  • 10.3 Pluggable Optical Transceivers
    • 10.3.1 Volume optical transceiver suppliers
    • 10.3.2 Optical transceiver assembly: Fabrinet, Jabil, Luxshare
  • 10.4 DSP and SerDes for Optical Transceivers
    • 10.4.1 Marvell's DSP business and the AI optical transceiver
    • 10.4.2 Linear Pluggable Optics (LPO) and the DSP-less transceiver
  • 10.5 III-V Materials Layer: InP, GaAs, GaN-Photonics
  • 10.6 NICs, DPUs, and SmartNICs
  • 10.7 Cables, Connectors, and Direct Attach Copper
  • 10.8 Market Forecast: AI-Tied Networking and Optical 2024-2037
  • 10.9 Company Profiles 394 (36 company profiles)

11 DATA CENTER CONSTRUCTION AND SUSTAINABILITY

  • 11.1 The AI Datacenter Buildout: Scale and Scope
  • 11.2 Power Infrastructure: Grid, On-Site Generation, and SMRs
    • 11.2.1 Behind-the-meter natural-gas generation
    • 11.2.2 Nuclear restart and Small Modular Reactor procurement
    • 11.2.3 Renewable energy procurement at hyperscaler scale
    • 11.2.4 Switchgear and transformers: the silent bottleneck
  • 11.3 Facility-Level Cooling Architecture
  • 11.4 Construction Supply Chain and Modular Datacenter Architecture
  • 11.5 Geographic Concentration and Site Selection
    • 11.5.1 The Top 12 AI Datacenter Regions (2026)
    • 11.5.2 Climate as a constraint
  • 11.6 PUE, WUE, and Sustainability Metrics
    • 11.6.1 Carbon-Free Energy (CFE) accounting
    • 11.6.2 Embodied carbon and circular economy
  • 11.7 Regulatory Framework
    • 11.7.1 Permit and interconnection timelines
  • 11.8 Market Forecast: AI Datacenter Construction Supply Chain 2024-2037

12 EDGE GENAI HARDWARE

  • 12.1 The Edge AI Taxonomy
  • 12.2 AI Smartphones
    • 12.2.1 Apple Neural Engine evolution
  • 12.3 AI PCs
    • 12.3.1 NVIDIA's AI PC entry
    • 12.3.2 Snapdragon X Elite and Qualcomm's PC push
  • 12.4 NVIDIA Jetson and the Embedded AI Platform
    • 12.4.1 Jetson AGX Thor and humanoid robotics
  • 12.5 Automotive AI Silicon
    • 12.5.1 NVIDIA DRIVE Thor and the L4 autonomous driving platform
    • 12.5.2 Tesla FSD and the captive silicon path
  • 12.6 Humanoid Robotics: The Emerging Edge AI Compute Frontier
    • 12.6.1 Humanoid robot unit volumes and silicon revenue forecast
  • 12.7 Edge AI Accelerator Start-ups
  • 12.8 Edge AI Memory: LPDDR5X, On-Chip SRAM, eMRAM
  • 12.9 Market Forecast: Edge AI Silicon 2024-2037
  • 12.10 Company Profiles 450 (51 company profiles)

13 REGIONAL ANALYSIS: GEOGRAPHY OF THE GENAI HARDWARE SUPPLY CHAIN

  • 13.1 The Asian Concentration
  • 13.2 Taiwan
    • 13.2.1 The TSMC scale
    • 13.2.2 The Taiwan supply chain depth
    • 13.2.3 Taiwan's geographic concentration risk
  • 13.3 South Korea
    • 13.3.1 SK hynix as the strategic anchor
    • 13.3.2 Samsung: vertical integration across the stack
    • 13.3.3 Korean specialty positions
  • 13.4 Japan
    • 13.4.1 Kumamoto and the broader Japanese fab expansion
  • 13.5 China
    • 13.5.1 Chinese domestic AI silicon volume and trajectory
    • 13.5.2 The SMIC constraint
    • 13.5.3 China's strength layers
  • 13.6 Southeast Asia and India
    • 13.6.1 Malaysian AI infrastructure
    • 13.6.2 India's emerging fab and OSAT capacity
    • 13.6.3 ASEAN AI cloud and sovereign-AI initiatives
  • 13.7 The United States
    • 13.7.1 The CHIPS Act build-out
    • 13.7.2 The US labour and supply chain constraints
  • 13.8 Europe and Israel
    • 13.8.1 ASML
    • 13.8.2 European Chips Act and the limits of European industrial policy
    • 13.8.3 Israel's specialty position
  • 13.9 The Rest of World: Niche Capabilities and Sovereign Ambitions
  • 13.10 Aggregate Regional Capture: Scenario Analysis 2026-2037

14 SUPPLY CHAIN AND GEOPOLITICS

  • 14.1 The Defining Tensions
  • 14.2 The China Strategy: Sovereign Stack and Domestic Substitution
    • 14.2.1 SMIC's role and the EUV-free leading-edge path
    • 14.2.2 The CXMT and JHICC HBM ramp
    • 14.2.3 China's wafer-fab equipment indigenisation
  • 14.3 US CHIPS Act Implementation and Domestic Reshoring
    • 14.3.1 TSMC Arizona
    • 14.3.2 Samsung Taylor
    • 14.3.3 Intel Foundry
    • 14.3.4 Micron's CHIPS-supported expansion
    • 14.3.5 The labour and ecosystem constraints
  • 14.4 European Chips Act and Strategic Autonomy
    • 14.4.1 The European specialty position
  • 14.5 The Critical Materials Layer
    • 14.5.1 Rare earths
    • 14.5.2 Gallium and germanium
    • 14.5.3 Neon and specialty gases
    • 14.5.4 Specialty quartz, silicon, and substrates
  • 14.6 Single-Point-of-Failure Analysis
  • 14.7 Scenarios for Supply Chain Resilience
    • 14.7.1 The "successful diversification" scenario (Bull case for resilience)
    • 14.7.2 The "concentrated capacity" scenario (Base case)
    • 14.7.3 The "geopolitical disruption" scenario (Bear case for resilience)
  • 14.8 Sovereign AI as a Strategic Demand Driver

15 SUSTAINABILITY AND EMBODIED CARBON

  • 15.1 The Sustainability Stakes
  • 15.2 Operational Emissions: Training, Inference, and the Cooling Energy Tax
    • 15.2.1 Training versus inference: the dominant share
  • 15.3 Embodied Carbon in Semiconductor Manufacturing
    • 15.3.1 The PFC and process-gas problem
    • 15.3.2 Embodied carbon at the device level
    • 15.3.3 Server-level and facility-level embodied carbon
  • 15.4 Water, Chemicals, and Resource Intensity
    • 15.4.1 PFAS chemistry and the transition
  • 15.5 Renewable Energy Procurement at Hyperscaler Scale
    • 15.5.1 Nuclear restart and SMR as carbon-free baseload
    • 15.5.2 On-site natural gas: the carbon offset
  • 15.6 Heat Recovery, Circular Economy, and End-of-Life
    • 15.6.1 Heat recovery and district heating
    • 15.6.2 Circular economy and component reuse
  • 15.7 Carbon Accounting Standards and Corporate Disclosure
    • 15.7.1 Scope 1, 2, 3 framework
    • 15.7.2 EU Corporate Sustainability Reporting Directive
    • 15.7.3 SEC climate disclosure rules
    • 15.7.4 Carbon pricing and offsets
  • 15.8 Green Manufacturing Practices at Major Suppliers
    • 15.8.1 Process gas abatement
    • 15.8.2 Water recycling and reuse
  • 15.9 Market and Regulatory Outlook 2026-2037
    • 15.9.1 Carbon-related regulatory tightening
    • 15.9.2 Embodied-carbon-conscious procurement
    • 15.9.3 The carbon-aware AI compute frontier

16 MARKET FORECASTS: GENAI HARDWARE 2026-2037

  • 16.1 Forecast Methodology and Framework
  • 16.2 Total GenAI Hardware Market - Base Case Forecast
  • 16.3 Bull/Base/Bear Scenarios at Aggregate Level
  • 16.4 AI Accelerator Silicon Sub-Segment Forecast
    • 16.4.1 Merchant vs. captive ASIC share trajectory
    • 16.4.2 China sovereign-stack AI silicon trajectory
  • 16.5 HBM and Memory Sub-Segment Forecast
  • 16.6 Advanced Packaging Sub-Segment Forecast
  • 16.7 Photonics Packaging Sub-Segment Forecast
  • 16.8 Thermal Management Sub-Segment Forecast
  • 16.9 Power Delivery Sub-Segment Forecast
  • 16.10 Networking and Optical Sub-Segment Forecast
  • 16.11 Datacenter Construction Supply Chain Sub-Segment Forecast
  • 16.12 Edge AI Silicon Sub-Segment Forecast
  • 16.13 Regional Capture Forecast
  • 16.14 Customer Tier Forecast
  • 16.15 Key Forecast Risks and Sensitivities
    • 16.15.1 The CapEx normalisation risk
    • 16.15.2 The Taiwan concentration risk
    • 16.15.3 Model training economics
    • 16.15.4 Chinese sovereign-stack acceleration
    • 16.15.5 Power infrastructure constraints

17 STRATEGIC OUTLOOK

  • 17.1 The Five Defining Themes of the GenAI Hardware Decade
  • 17.2 The Choke-Point Map
  • 17.3 The Strategic Investment Framework
  • 17.4 M&A Landscape and Strategic Consolidation
    • 17.4.1 Photonics consolidation
    • 17.4.2 Memory and HBM consolidation
    • 17.4.3 Equipment and tools consolidation
    • 17.4.4 AI silicon start-up consolidation
    • 17.4.5 Forward M&A trajectory through 2030
  • 17.5 Sensitivity Analysis
  • 17.6 Strategic Implications by Stakeholder
    • 17.6.1 For AI accelerator silicon designers
    • 17.6.2 For hyperscalers and AI cloud operators
    • 17.6.3 For memory manufacturers
    • 17.6.4 For foundries
    • 17.6.5 For OSATs and substrate suppliers
    • 17.6.6 For thermal and power infrastructure suppliers
    • 17.6.7 For photonics packaging participants
    • 17.6.8 For governments and policymakers
  • 17.7 What Could Change This Forecast
    • 17.7.1 Upside surprises
    • 17.7.2 Downside surprises
    • 17.7.3 Structural rather than cyclical risk

18 APPENDIX

  • 18.1 Forecast Methodology
    • 18.1.1 Unit volume forecast construction
    • 18.1.2 ASP and content-per-unit forecast construction
    • 18.1.3 Scenario construction
    • 18.1.4 Cross-validation
  • 18.2 Definitions and Terminology
    • 18.2.1 AI accelerator silicon categories
    • 18.2.2 Memory technology categories
    • 18.2.3 Packaging terminology
    • 18.2.4 Photonics terminology
    • 18.2.5 Thermal terminology
    • 18.2.6 Power terminology
    • 18.2.7 Networking terminology
    • 18.2.8 Geographic and customer terminology
  • 18.3 Abbreviations
  • 18.4 Sources and References
    • 18.4.1 Primary research
    • 18.4.2 Company financial disclosures
    • 18.4.3 Industry-association and government statistics
    • 18.4.4 Cross-reference industry reports
    • 18.4.5 Technical and scientific literature
  • 18.5 Forecast Scope, Limitations, and Disclaimers
    • 18.5.1 Forecast scope
    • 18.5.2 Forecast limitations
    • 18.5.3 Disclaimers
  • 18.6 Detailed Year-by-Year Forecast Outputs

List of Tables

  • Table 1. Headline Findings Summary (Base Case)
  • Table 2. Eleven-Year Forecast Summary: GenAI Hardware Materials Market 2026–2037 (US $B, Base Case)
  • Table 3. Top Ten Strategic Conclusions Mapped to Stakeholder Type
  • Table 4. Training vs. Inference Hardware Mix Comparison
  • Table 5. Silicon Content per 100 MW AI Training Facility (Reference BoM)
  • Table 6. Cost-per-Token by Model Size and Hardware Configuration 2024–2040 (USD per million output tokens)
  • Table 7. Sovereign AI Build-Outs by Country 2025–2030
  • Table 8. AI Accelerator Memory Requirements 2024–2030F
  • Table 9. US and Chinese Hyperscaler Capex Summary 2021–2026 (US $B)
  • Table 10. GPU Specifications: NVIDIA Blackwell, Rubin; AMD MI350X, MI450 (2024–2026)
  • Table 11. Rack-Scale GPU Platform Comparison
  • Table 12. AI ASIC Specifications: Google, AWS, Microsoft, Meta (2024–2026)
  • Table 13. AI ASIC Technology Specification Database (All Major Vendors)
  • Table 14. Chinese Data Center Processor Manufacturer Overview
  • Table 15. China AI Chip Capability Gap Assessment by Workload Type
  • Table 16. Semiconductor Process Node Roadmap 2024–2030
  • Table 17. TSMC Node Roadmap: N3, N2, A16, A14 Specs and Timeline
  • Table 18. Wafer-Scale Accelerator Yield Economics: Cerebras WSE-3 and Tesla Dojo
  • Table 19. Incumbent EDA Vendor AI Initiatives vs. Startup Cohort
  • Table 20. AI-EDA Approaches by Design-Flow Stage
  • Table 21. AI-EDA Market Forecast 2026–2037
  • Table 22. HBM Generation Technical Specifications HBM2E to HBM5
  • Table 23. HBM Bonding Integration Roadmap and Vendor Mapping
  • Table 24. HBM Market Share by Supplier 2022–2028F (%)
  • Table 25. HBM Customer Demand Breakdown: NVIDIA, Google, AMD, Hyperscalers 2024–2028F
  • Table 26. Custom HBM Players, Products, Design Roadmaps
  • Table 27. Standard vs. Custom HBM Revenue Forecast 2024–2030F (US $M)
  • Table 28. Near-Memory and In-Memory Computing Landscape
  • Table 29. Resistive Non-Volatile Memory Technologies
  • Table 30. Storage-Class Memory Technology Comparison
  • Table 31. CXL Switch Silicon Vendors and Capability Matrix
  • Table 32. 3D DRAM Technology Readiness Assessment by Player 2026
  • Table 33. Advanced Packaging Technology Comparison: 2.5D and 3D Options
  • Table 34. CoWoS Capacity Forecast by Sub-Variant 2024–2037 (k wafers/month equivalent)
  • Table 35. TSMC SoIC Variants: Specifications and AI Customer Adoption
  • Table 36. Comparative Advanced Packaging Roadmap: TSMC vs. Intel vs. Samsung
  • Table 37. Substrate Suppliers for AI Accelerator Packages
  • Table 38. Substrate Demand Forecast for AI Packages 2024–2037 (k units/month)
  • Table 39. Interposer Material Comparison: Silicon TSV vs. Glass vs. Organic RDL
  • Table 40. Hybrid Bonding Adoption Roadmap for DRAM Applications 2023–2030
  • Table 41. OSAT Capacity and Revenue Concentration 2024–2030
  • Table 42. Advanced Packaging Materials Suppliers
  • Table 43. Migration Trajectory from Copper to Optical Across the Two Network Layers
  • Table 44. Key Technology Building Blocks for Co-Packaged Optics
  • Table 45. TSMC Photonics Packaging Capabilities
  • Table 46. Merchant Photonics Packaging Platform Comparison
  • Table 47. Optical I/O Chiplet Vendor Comparison
  • Table 48. AI-Switch Silicon Roadmap with CPO Integration
  • Table 49. Silicon Photonics Foundry Capability Matrix
  • Table 50. CPO Supply Chain Critical Materials and Suppliers
  • Table 51. Photonics Packaging Revenue Forecast for AI Applications 2024–2037 (US $B)
  • Table 52. Cooling Technologies for High-Performance AI Processors
  • Table 53. Thermal Interface Material Categories and Suppliers
  • Table 54. TIM Properties for AI Accelerator Applications
  • Table 55. TIM Revenue Forecast for AI Datacenter Applications 2024–2037 (US $M)
  • Table 56. Heat Spreader and Vapor Chamber Suppliers
  • Table 57. Heat Spreader and Heat Sink Revenue Forecast 2024–2037 (US $M)
  • Table 58. Cold Plate Suppliers for AI Servers
  • Table 59. Liquid Cooling Adoption Share in New AI Datacenter Deployments
  • Table 60. Immersion Cooling Fluid Categories and Suppliers
  • Table 61. Immersion Cooling System Suppliers
  • Table 62. Microfluidic Cooling Technology Comparison
  • Table 63. Facility Liquid Cooling Infrastructure Suppliers
  • Table 64. AI-Tied Thermal Management Revenue Forecast 2024–2037 (US $B)
  • Table 65. Power Delivery Hierarchy in AI Servers
  • Table 66. Comparison of 48V and 800V HVDC Rack Architectures
  • Table 67. SiC vs. GaN vs. Silicon Power Device Comparison
  • Table 68. SiC Substrate and Device Suppliers
  • Table 69. GaN Device Manufacturers and Application Focus
  • Table 70. AI VRM Controller and Power Stage Suppliers
  • Table 71. Server Power Supply Unit Suppliers
  • Table 72. Backside Power Delivery Adoption Roadmap
  • Table 73.AI Datacenter Power Semiconductor Revenue Forecast 2024–2037 (US $B)
  • Table 74. The Three Networking Layers in an AI Datacenter
  • Table 75. AI Switch Silicon Roadmap
  • Table 76. Optical Transceiver Form Factor and Data Rate Roadmap
  • Table 77. Optical Transceiver Module Suppliers for AI Datacenters
  • Table 78. Optical DSP Suppliers and Application Mapping
  • Table 79. III-V Substrate Materials Suppliers for AI Optical Transceivers
  • Table 80. NIC, DPU, and SmartNIC Suppliers
  • Table 81. Cable, Connector, and Fiber Suppliers for AI Datacenters
  • Table 82. AI-Tied Networking and Optical Revenue Forecast 2024–2037 (US $B)
  • Table 83. AI Datacenter CAPEX Breakdown (100 MW Training Facility, 2026 Reference)
  • Table 84. Hyperscaler Power Procurement Strategies (2025 Snapshot)
  • Table 85. Major Switchgear, Transformer, and Power Infrastructure Suppliers
  • Table 86. Facility Cooling Infrastructure Suppliers
  • Table 87. Major AI Datacenter Construction Companies and Operators
  • Table 88. Construction Engineering and EPC Firms with Major AI Datacenter Practice
  • Table 89. PUE Targets and Achievement at Major Hyperscalers (2025)
  • Table 90. AI-Tied Datacenter Construction Supply Chain Revenue Forecast 2024–2037 (US $B)
  • Table 91. Edge AI NPU Performance by Application Segment
  • Table 92. Flagship Smartphone AI Processor Comparison (2026)
  • Table 93. Evolution of Apple Neural Engine AI Performance (2017–2026)
  • Table 94. AI PC Silicon Platform Comparison (2026)
  • Table 95. AI PC On-Device LLM Inference Capability (2026)
  • Table 96. NVIDIA Jetson Product Line (2026)
  • Table 97. Automotive AI Silicon Platforms (2026)
  • Table 98. Humanoid Robot Compute Platforms (2026)
  • Table 99. Edge AI Start-up Landscape
  • Table 100. Edge AI Memory Suppliers and Categories
  • Table 101. Edge AI Silicon Revenue Forecast 2024–2037 (US $B)
  • Table 102. Regional Capture of GenAI Hardware Bill of Materials, 2026 Base Case
  • Table 103. Taiwan AI Hardware Supply Chain by Capability Layer
  • Table 104. Korea AI Hardware Supply Chain by Capability Layer
  • Table 105. Japan AI Hardware Supply Chain by Capability Layer
  • Table 106. China AI Hardware Supply Chain by Capability Layer
  • Table 107. Southeast Asia and India AI Hardware Supply Chain
  • Table 108. United States AI Hardware Supply Chain by Capability Layer
  • Table 109. Europe and Israel AI Hardware Supply Chain
  • Table 110. Regional GenAI Hardware BoM Capture by Scenario (% of Global BoM Value)
  • Table 111. Major US Export Control Actions Affecting AI Hardware (2019–2026)
  • Table 112. Chinese Wafer-Fab Equipment Companies and Capability Status
  • Table 113. Major CHIPS Act-Funded Semiconductor Projects
  • Table 114. Critical Materials Supply Chain Concentration for AI Hardware
  • Table 115. Top Single-Point-of-Failure Risks in the GenAI Hardware Supply Chain
  • Table 116. Supply Chain Diversification Scenario Outcomes 2030
  • Table 117. Lifecycle Carbon Footprint by AI Chip Type
  • Table 118. AI Carbon Footprint Examples and Mitigation Strategies
  • Table 119. Estimated Embodied Carbon Across the AI Hardware Hierarchy
  • Table 120. Water Consumption Profile for AI Hardware Manufacturing and Operations
  • Table 121. Hyperscaler Renewable Energy and Nuclear Procurement (2025 Snapshot)
  • Table 122. Lifecycle and End-of-Life Treatment for AI Hardware
  • Table 123. Major Corporate Carbon Commitments Affecting AI Hardware Procurement
  • Table 124. Green Manufacturing Initiatives by Major Semiconductor Suppliers
  • Table 125. Forecast Methodology and Key Assumptions
  • Table 126. Total GenAI Hardware Market by Major Segment, Base Case (US $B)
  • Table 127. GenAI Hardware Aggregate Market Across Three Scenarios, 2026–2037 (US $B, excl. construction supply chain)
  • Table 128. AI Accelerator Silicon Sub-Segment Forecast 2024–2037 (US $B)
  • Table 129. HBM and AI-Tied Memory Sub-Segment Forecast 2024–2037 (US $B)
  • Table 130. Advanced Packaging Sub-Segment Forecast 2024–2037 (US $B, AI-tied)
  • Table 131. Photonics Packaging Sub-Segment Forecast 2024–2037 (US $B)
  • Table 132. Thermal Management Sub-Segment Forecast 2024–2037 (US $B, AI-tied)
  • Table 133. Power Delivery (AI Datacenter Tied) Sub-Segment Forecast 2024–2037 (US $B)
  • Table 134. Networking and Optical (AI-Tied) Sub-Segment Forecast 2024–2037 (US $B)
  • Table 135. Datacenter Construction Supply Chain Sub-Segment Forecast 2024–2037 (US $B)
  • Table 136. Edge AI Silicon Sub-Segment Forecast 2024–2037 (US $B)
  • Table 137. Regional GenAI Hardware BoM Capture Forecast, 2026–2037, Base Case (%)
  • Table 138. Total GenAI Hardware Demand by Customer Tier, Base Case 2026–2037 (US $B, excl. construction supply chain)
  • Table 139. The Five Defining Themes: Strategic Implications by Layer
  • Table 140. The Top 15 Strategic Choke Points in the GenAI Hardware Supply Chain
  • Table 141. Strategic Tier Classification of GenAI Hardware Sub-Segments
  • Table 142. Notable GenAI Hardware M&A and Strategic Investments 2020–2026
  • Table 143. Sensitivity of Base Case 2030 Forecast to Key Assumptions
  • Table 144. Detailed Year-by-Year Total Forecast, Base Case (US $B, excl. DC construction supply chain)
  • Table 145. Detailed Year-by-Year Total Forecast Across All Three Scenarios (US $B, excl. DC construction supply chain)

List of Figures

  • Figure 1. Five Compute-Scaling Walls and Their Material Solutions
  • Figure 2. Generative AI Hardware Materials Value-Chain Layer Map
  • Figure 3. Base-Case Forecast Stacked-Area Visualisation 2026–2037
  • Figure 4. Bull, Base, and Bear Scenario Comparison 2026–2037
  • Figure 5. Asia-Pacific Capture Rate of GenAI Hardware Value 2026–2037
  • Figure 6. AI Data Centre Silicon Content Map
  • Figure 7. Inference Token Economics by Model Size
  • Figure 8. Sovereign AI Capex Pipeline 2024–2030 by Geography
  • Figure 9. Generative AI Compute Demand Scaling vs. Electrical Interconnect Capacity
  • Figure 10. AI Accelerator BoM Decomposition: Where the Dollars Go
  • Figure 11. Annual GenAI-Driven AI Hardware Demand Pool 2024–2030
  • Figure 12. NVIDIA GPU Architecture Evolution: Volta to Post-Blackwell Timeline
  • Figure 13. Rack-Scale GPU Architecture: NVL72 and Next-Generation Platforms
  • Figure 14. Hyperscaler ASIC Roadmap Comparison
  • Figure 15. Hyperscaler ASIC vs. Merchant GPU Share of Datacenter AI Compute 2024–2037
  • Figure 16. AI ASIC Start-Up Landscape by Funding Stage
  • Figure 17. GPU vs. AI ASIC Performance per Watt Comparison 2022–2026
  • Figure 18. China Semiconductor Capability Map: Node vs. Supply-Chain Layer
  • Figure 19. China AI Chip Roadmap vs. NVIDIA / AMD: Parity Distance by Generation
  • Figure 20. Leading-Edge Foundry Roadmap Comparison 2023–2037 (Gantt)
  • Figure 21. HBM Architecture: Die-Stack Cross-Section
  • Figure 22. HBM Bandwidth Evolution HBM1 to HBM5
  • Figure 23. HBM4 Die-to-Wafer Bonding Integration Scheme
  • Figure 24. HBM Market Share by Supplier 2022–2028F
  • Figure 25. SK hynix HBM Strategy and Roadmap
  • Figure 26. Samsung HBM Strategy and Roadmap
  • Figure 27. Micron HBM Strategy and Roadmap
  • Figure 28. HBM Customer Demand Breakdown by AI Accelerator
  • Figure 29. Custom HBM Architecture: Co-Design Concept
  • Figure 30. Custom HBM Share of Total HBM Bit Demand 2026–2037
  • Figure 31. Near-Memory vs. PIM Architecture Comparison
  • Figure 32. CXL Memory Pooling Architecture and Vendor Map
  • Figure 33. 3D DRAM Concept Architectures
  • Figure 34. Monolithic Die vs. Chiplet Architecture: Yield and Cost
  • Figure 35. Chiplet Interconnect Technology Spectrum
  • Figure 36. CoWoS Integration: GPU + HBM on Silicon Interposer
  • Figure 37. CoWoS Capacity Expansion Roadmap
  • Figure 38. OSAT Revenue Concentration by Geography 2024–2037
  • Figure 39. Compute Demand vs. Interconnect Bandwidth Gap
  • Figure 40. Photonics Packaging Revenue Forecast for AI Applications 2024–2037
  • Figure 41. AI Accelerator TDP and Cooling Architecture Trajectory 2022–2037
  • Figure 42. Liquid Cooling Adoption Trajectory in AI Datacenter Deployments
  • Figure 43. Power Density at AI Server Rack: From 30 kW to 600 kW per Rack
  • Figure 44. Wide-Bandgap Power Semiconductor Material Properties Comparison
  • Figure 45. Edge AI Performance and Power Envelope Map
  • Figure 46. Total GenAI Hardware Market 2024–2037 by Segment, Base Case
  • Figure 47. GenAI Hardware Market Bull/Base/Bear Scenarios 2024–2037
  • Figure 48. Sensitivity of 2030 Forecast to Key Variables