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市場調查報告書
商品編碼
2041946

生成式人工智慧硬體材料市場（2026-2036 年）

The Generative AI Hardware Materials Market 2026-2036
出版日期: | 出版商: Future Markets, Inc. | 英文 438 Pages, 145 Tables, 48 Figures | 訂單完成後即時交付

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生成式人工智慧是半導體產業最大的單一需求，而生成式人工智慧硬體材料市場則是對此需求的供應方回應。該市場涵蓋矽、記憶體、封裝、光子學、溫度控管和電源等各個層面，並嵌入到超大規模資料中心、企業和光電部署、政府主導的人工智慧專案以及新興的邊緣人工智慧領域的人工智慧基礎設施中。

這個市場最好被理解為人工智慧計算堆疊的九個同心層。最頂層是人工智慧加速器晶片，包括輝達和AMD的GPU、Google、AWS、微軟和Meta等超大規模資料中心客製化的ASIC晶片，以及Cerebras、Groq、SambaNova和中國自主研發人工智慧晶片公司的超大規模資料中心業者架構。加速器晶片下方是高頻寬記憶體（HBM）。它已成為計算晶片底層中最有價值的層，也是從HBM3E到HBM4再到HBM5藍圖的最大受益者。先進的2.5D和3D封裝技術（CoWoS、SoIC以及新興的玻璃芯基板生態系統）將運算晶片和記憶體晶片整合在一起，形成人工智慧加速器出貨的實體封裝。隨著電訊號傳輸速度在每聲道超過224 Gbps後達到極限，共封裝的光元件和矽光電正從試驗階段走向量產。隨著加速器熱設計功耗 (TDP) 超過 1500W，溫度控管正從風冷轉向晶片連接式液冷、浸沒式冷卻和封裝內微流體冷卻。電源電壓也從 12V 提升至 48V，甚至 800V 高壓直流 (HVDC) 架構，其中 GaN 和 SiC 被廣泛應用於資料中心電源。用於網路、資料中心建設供應鏈以及邊緣人工智慧的矽和光元件構成了這項技術堆疊。

目前，前沿模型的性能受限於物理限制，而這些限制只能透過材料和封裝技術的創新來克服。具體而言，這些限制包括：由於光阻面積和電晶體密度導致的計算吞吐量受限；由於HBM堆疊高度和引腳寬度導致的內存頻寬；由於銅線衰減導致的互連頻寬；由於導熱界面材料（TIM）的導電性和冷卻液流速導致的散熱受限；以及由於IR壓降和穩壓受限器效率導致的電源性能受限器。透過特定材料和封裝技術的創新，這些限制正逐步被克服，從而在整個供應鏈中產生持續且多層次的需求成長。

供應鏈基礎設施的結構性中心位於亞洲。台灣在尖端邏輯半導體和先進封裝領域處於領先地位，韓國在HBM領域主導，日本在特種材料和基板材料領域領先，而中國則在出口管制限制下同時建立自己的AI硬體體系。生成式AI供應鏈的材料和封裝層是現代經濟中產業價值鏈最集中的環節之一，其發展趨勢將決定未來十年AI運算能力擴張的速度。

《生成式人工智慧硬體材料市場（2026-2036）》是關於建構生成式人工智慧硬體所需的材料和封裝層供應端的最全面、最權威的資訊來源。本報告透過量化超大規模人工智慧資本投資如何轉化為整個供應鏈中的實體基礎設施（矽晶片、HBM堆疊、先進封裝、基板、光電、溫度控管系統和功率超大規模資料中心業者），對基礎模型、人工智慧服務和超大規模資料中心業者進行了補充。

本報告分章節說明了人工智慧硬體材料價值鏈的九個同心層級。具體而言，這些層級包括：用於人工智慧加速器的矽材料、人工智慧驅動的晶片設計（EDA）、高頻寬記憶體及超越HBM的架構、先進封裝和基板、共封裝光學元件和矽光電、溫度控管、電源及向GaN/SiC的過渡、網路和光學材料、資料中心建設供應，以及邊緣生成的人工智慧鏈層。每個章節都結合了自下而上的產量和平均售價（ASP）分析、產能和資本支出追蹤、技術藍圖繪製以及詳細的公司概況。區域分析涵蓋台灣、韓國、日本、中國大陸、東南亞和印度、美國、歐洲和以色列。重點關注供應鏈和地緣政治的章節探討了中美技術競爭、台灣市場集中化的風險、關鍵材料的供應、《晶片法案》和《歐洲晶片法案》的實施，以及中國自主人工智慧硬體堆疊的並行發展。永續性和體內碳（製造過程中的碳排放）分析涵蓋人工智慧基礎設施的營運和製造排放概況、PFAS 化學品的過渡以及碳計量的法規結構。

本報告的調查方法整合了基於自下而上分析的細分市場預測，分析內容包括單位數量、平均售價 (ASP) 和單位組件價值，並針對九個地區，提出了基準情景、樂觀情景和悲觀情景，以及截至 2036 年的市場佔有率預測。戰略展望部分提出了構成未來十年生成式人工智慧硬體的五大關鍵主題、阻礙因素瓶頸圖、策略投資框架，以及對 2030 年前併購趨勢的分析。

本報告的目標受群眾外包括亞洲晶圓代晶圓代工廠、記憶體、基板、光電、溫度控管和冷卻供應商生態系統中的買家和決策者；正在評估產能和供應商策略的超大規模資料中心業者和人工智慧晶片設計商；正在建立整個人工智慧硬體專案的機構投資者；以及正在規劃國家人工智慧基礎設施的政府人工智慧專案的政府專案。報告涵蓋2026年至2036年這十年，詳細分析了關鍵的架構轉折點、產能瓶頸、技術轉型以及將定義生成式人工智慧硬體未來十年的地緣政治格局。最終，本報告提供了一個關於構成生成式人工智慧物理基礎的單一、綜合資訊來源。

目錄包括：

  • 執行摘要- 主要發現；生成式人工智慧硬體的瓶頸；材料價值鏈概述；十年預測亮點；針對亞洲晶圓代工廠、OSAT、記憶體、基板和散熱供應商的策略洞察；主要市場參與者（NVIDIA、台積電、SK海力士、三星電子、日月光科技）
  • 支援生成式 AI 的運算堆疊 - 訓練與推理的經濟性；預訓練、後訓練和 RLHF 中的計算分配；推理代幣經濟性和服務基礎設施；測試期間的計算和推理模型需求；雲端、邊緣和主權 AI；100,000 個 GPU 規模的超大規模資料中心業者內存叢集；企業本地部署和雲端部署；的比例；CoWoS 作為限制瓶頸；超大規模資料中心業者、企業級和主權 AI 的資本支出。
  • AI加速器晶片－NVIDIA Hopper → Blackwell → Blackwell Ultra → Rubin → Rubin Ultra藍圖；NVL72機架架構及Rubin及後續晶片的擴展；AMD MI300X → MI355X → MI400部署；Intel Gaudi及後續產品；專為超大規模資料中心業者的客製化ASIC、ASIC、ASIC、ASIC、ASIC、ASIC、ASIC、ASIC、ASIC（ASIC、AUSS 這是）/ MTIA）；ASIC的非經常性工程成本經濟性及損益平衡點分析；特定領域架構（Cerebras WSE-3、Groq LPU、SambaNova RDU）；中國AI晶片生態系（華為Ascend、寒武紀、Biren、Moore Threads）；台積電孔、三星、英特節點和組合中尺寸的高數位藍圖；
  • AI主導的晶片設計（EDA）－AI硬體時代EDA的瓶頸；AI設計AI硬體的遞歸循環；現有EDA供應商的AI計劃；用於數位設計和檢驗的基於代理的AI，用於模擬和先進封裝的物理AI，用於模擬和PCB設計的AI，以及EDA相關矽領域的初創公司；地理分佈；2026-2036年市場預測工具；
  • 高頻寬記憶體 (HBM) 及未來－HBM 架構和 TSV 堆疊基礎知識；HBM3/HBM3E、HBM4/HBM4E、HBM5/HBM5E 代際藍圖；SK 海力士、三星和美光的戰略和產能展望；比特出貨量和晶能產能創新標準能；銷售構成比；記憶體計算和記憶體處理；新興記憶體；記憶體池和 CXL 結構；2030 年以後的 3D DRAM 展望。
  • 先進封裝和基板材料－2.5D/3D架構的連續發展；台積電的CoWoS-S/L/R藍圖和產能擴張；CoWoS-Photonics和CoWoP；SoIC、SoIC-X、SoIC-P混合鍵結堆疊；英特爾的EMIB和Foveros；三星的I-Cube/X-Cube/H-Cube；ABF供應的寡占；玻璃芯基板；中介層材料（矽TSV、玻璃、有機RDL）；混合鍵結設備生態系；HBM4中混合鍵結技術的應用；OSAT產能與亞洲優勢
  • 共封裝光學元件和矽光電在人工智慧中的應用－光連接模組的需求；CPO架構和雙層網路；台積電COUPE、CoWoS-Photonics、iOIS；向CoWoP和NVIDIA Rubin的過渡； AI/Marvell）；交換器矽晶和共封裝光引擎；矽光電晶圓代工廠；光電封裝材料供應鏈；2026-2036年市場規模
  • 人工智慧資料中心的溫度控管－封裝級熱危機；導熱界面材料（液態金屬導熱界面材料、焊料導熱界面材料、鑽石基導熱界面材料）；散熱片、均熱板、熱管；冷板和晶片級液冷；冷板供應鏈瓶頸；單微流體和兩相浸沒式冷卻；PFASASCD；
  • 電源及GaN/SiC過渡－從12V到48V和800V高壓直流輸電的電力危機；48V托盤架構和過流保護（OCP）標準；機架級800V高壓直流輸電和Rubin過渡；SiC元件和基板供應；GaN元件（橫向、縱向、級聯）；GaN在AI伺服器電源應用中的應用；2027年後縱向GaN展望；電壓調節模組（VRM）和多相負載點；單晶片電源系統在AI VRM中的優勢；封裝整合VRM；伺服器電源與機架整流器架；背面電源（Intel PowerVia、台積電A16、三星BSPDN）；2024-2036年市場預測
  • 網路和光材料－人工智慧資料中心的三層網路；交換器晶片藍圖（博通 Tomahawk 6 Davisson、NVIDIA Spectrum-X/Quantum-X）；超乙太網路聯盟；可插拔光收發器；量產收發器供應商；光電器組裝（Fabrinet、捷串解動器、精密處理器）；業務；線性可插拔光元件 (LPO)；III-V 族材料（磷化銦、砷化鎵、氮化鎵光電）；網卡 (NIC)、資料處理單元 (DPU) 和智慧網路卡 (SmartNIC)；線纜、連接器和數位類比轉換器 (DAC)；2024-2036 年市場預測預測
  • 資料中心建置與永續性－電力基礎設施（輸電網、現場發電、小型模組化反應器）；表後天然氣；核能發電廠運作與小型模組化反應器採購；超大規模資料中心業者大規模可再生能源採購；開關設備和變壓器；設施級冷卻架構；建設供應鏈和模組化資料中心架構；地理集中度和位置；電源使用效率 (PUEUE)、永續性排放效率 (PUEUE) 和法規結構碳化；
  • 邊緣人工智慧硬體－人工智慧智慧型手機和蘋果神經網路引擎的演進；人工智慧PC（NVIDIA、驍龍X Elite）；NVIDIA Jetson和嵌入式人工智慧；Jetson AGX Thor和人形機器人；汽車人工智慧晶片（NVIDIA DRIVE Thor、特斯拉FSD）；人形機器人銷售與晶片收入預測；邊緣人工智慧新創趨勢；邊緣人工智慧記憶體（LPDDR5X、片上SRAM、eMRAM）；2024-2036年市場預測
  • 區域分析－台灣、韓國、日本、中國、東南亞和印度、美國、歐洲和以色列；2026年至2036年區域市佔率取得情境分析
  • 供應鏈與地緣政治－中國戰略與主權堆棧；中芯國際及其最尖端科技之路；長江中芯國際和長江中芯國際的HBM量產擴張；美國《晶片法案》的實施（台積電亞利桑那工廠、三星泰勒工廠、英特爾晶圓晶圓代工廠、美光科技）；歐洲《晶片法案》；關鍵材料（稀土元素、鎵和鍺、氖和特種氣體、特種石英和基板）；單點故障分析；供應鏈韌性情境；主權人工智慧作為戰略需求的驅動力
  • 永續性和體內碳排放－營運排放；冷凍能源稅；半導體製造中的體內碳排放；全氟化合物和製程氣體氟烷基物質的化學轉型；可再生能源採購；核能發電運作和小型模組化反應器；熱回收和區域供熱；循環經濟；碳核算標準（範圍 1/2/3、歐盟製造企業）指令；
  • 2026-2036年市場預測 - 整體市場規模基本案例；看漲/中性/看跌情境；人工智慧加速器矽、HBM、先進封裝、光電封裝、溫度控管、電源、網路、資料中心建設和邊緣人工智慧等細分市場預測；區域佔有率預測；客戶細分市場預測；關鍵預測風險和敏感度分析分析；
  • 策略展望－五大主題；瓶頸圖；策略投資架構；2030 年前的併購趨勢與策略整合；敏感度分析；相關人員。

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 T-Head（PingTouGe）、Alpha Assembly Solutions（MacDermid Alpha）、Alphabet Inc.（Google）、Amazon Web Services（AWS）、Ambarella、Amber Semiconductor（AmberSemi）、Amkor Technology、Amphenol Corporation、Anduril Industries、Apple Inc.、Applied Materials、Apptronik、Arago、ASE Technology Holding（SPILを含む）、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)、d-Matrix、DEEPX、Delta Electronics、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、Great Sky、Green Revolution Cooling（GRC）、GreenWaves Technologies、Groq Inc.、GS Microelectronics（GSME）、Hailo Technologies、Henkel AG、Heraeus、Hesheng Silicon Industry、Hisense Broadband、Hitachi Energy、Hon Hai（Foxconn）、Honeywell International、Horizon Robotics、Hua Tian Technology (HT-Tech)、Huawei Technologies (HiSilicon)、Hummink、Ibiden Co. Ltd.、Iceotope Technologies、Iluvatar CoreX、Indium Corporation、Infineon Technologies AG、Innolight Technology、Innoscience Technology、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、Lace Lithography、Lam Research、Lambda Inc.、LG Innotek、Lightmatter、Liquid Wire Inc.、LiquidStack, LiteOn Technology、LOTES Co.、Lumentum Holdings、Lumotive、Luxshare Precision、M&I Materials、Macronix International、Maieutic Semiconductor、Majestic Labs、Marvell Technology、MatX, MediaTek、Mesh Optical Technologies、Meta Platforms、Microchip Technology、Micron Technology Inc.、Microsoft Corporation、Mitsubishi Electric、Mobileye Global、Monolithic Power Systems (MPS)、Montage Technology、Moore Threads Technology、Morphing Machines、Movandi, Multibeam Corporation、Murata Manufacturing、Mythic、Nan Ya PCB、Nanya Technology、Navitas Semiconductor、NcodiN、Neo Semiconductor、NeoGraf Solutions、NeoLogic、Netrasemi、NEURA Robotics、Neurophos、Normal Computing、NVIDIA Corporation、NXP Semiconductors、Olix、Omni Design Technologies、onsemi (ON Semiconductor)、OpenLight、Optalysys、Opticore、Oracle Corporation（Oracle Cloud Infrastructure）、Oxmiq Labs、Panasonic、Parker Chomerics、Patentix、Positron AI、Power Integrations、Powerchip Semiconductor（PSMC）、PowerLattice、Powertech Technology、Primemas

目錄

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
  • 公司簡介
    • Alphabet Inc. (Google)
    • Amazon Web Services (AWS)
    • CoreWeave Inc.
    • Crusoe Energy Systems
    • G42
    • Lambda Inc.
    • Meta Platforms
    • Microsoft Corporation
    • Oracle Corporation (Oracle Cloud Infrastructure)

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
  • 公司簡介（53 家公司簡介）

第4章：基於人工智慧的晶片設計（EDA）

  • 人工智慧硬體時代下的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 2026-2036 年
  • 戰略意義
  • 公司簡介（6 家公司簡介）

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
  • 公司簡介（23 家公司簡介）

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
  • 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（2026-2036）
  • 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（2024-2036 年）
  • 公司簡介（40家公司簡介）

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裝置和基板的供應
  • 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（2024-2036 年）
  • 公司簡介（42家公司簡介）

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2024-2036 年
  • 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 2024–2036

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 2024-2036
  • 公司簡介（51家公司簡介）

第13章 區域分析：吉奈五金供應鏈的區域

  • 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（2026-2036 年）

第14章：供應鏈與地緣政治

  • 關鍵張力
  • 中國戰略：主權堆疊與國內替代
  • 美國「兒童移民和難民保護法」的執行和國內遣返
  • 歐洲晶片法和戰略自主權
  • 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
  • 半導體製造產生的碳排放
  • 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
  • 2026-2036年市場與監管展望

第16章 市場預測：通用人工智慧硬體 2026–2036

  • 預測性調查方法與框架
  • 生成式人工智慧硬體市場—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

  • 過去十年生成式人工智慧硬體的五大主題
  • 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
  • 利害關係人的相關利益者影響
  • 哪些因素可能會改變這個預測？

第18章附錄

Generative AI has become the largest single demand driver in the semiconductor industry, and the Generative AI Hardware Materials market is the supply-side response to that demand. It spans the silicon, memory, packaging, photonics, thermal, and power-delivery layers that go into AI infrastructure across hyperscale data centres, enterprise and neocloud deployments, sovereign-AI programs, and the emerging edge AI tier.

The market is best understood as nine concentric layers of the AI compute stack. AI accelerator silicon sits at the top - GPUs from NVIDIA and AMD, custom hyperscaler ASICs from Google, AWS, Microsoft, and Meta, and challenger architectures from Cerebras, Groq, SambaNova, and the Chinese sovereign-AI silicon cohort. Beneath the accelerator die sits high-bandwidth memory, which has emerged as the most valuable layer below the compute silicon and the principal beneficiary of the HBM3E-to-HBM4-to-HBM5 roadmap. Advanced 2.5D and 3D packaging - CoWoS, SoIC, and the emerging glass-core substrate ecosystem - integrates compute and memory dies into the physical packages that AI accelerators ship in. Co-packaged optics and silicon photonics are moving from pilot to volume as electrical signalling reaches its limit above 224 Gbps per lane. Thermal management is shifting from air cooling to direct-to-chip liquid cooling, immersion, and in-package microfluidic cooling as accelerator TDPs scale past 1500 W. Power delivery is transitioning from 12V to 48V to 800V HVDC architectures, pulling GaN and SiC into data centre PSU applications. Networking silicon and optical components, the data centre construction supply chain, and the edge AI silicon tier round out the stack.

Frontier-model performance is now bounded by physical limits that yield only to materials and packaging innovation - compute throughput by reticle area and transistor density, memory bandwidth by HBM stack height and pin width, interconnect bandwidth by copper trace attenuation, thermal dissipation by TIM conductivity and coolant flow rate, and power delivery by IR drop and voltage-regulator efficiency. Each of these walls is being attacked by a specific materials or packaging innovation, creating a sustained, multi-layer demand expansion across the supply chain.

The supply base is structurally Asia-centric. Taiwan dominates leading-edge logic and advanced packaging, Korea dominates HBM, Japan dominates specialty materials and substrate inputs, and China is building a parallel sovereign-AI hardware stack under export-control constraints. The materials and packaging layer of the GenAI supply chain is one of the most concentrated industrial value chains in the modern economy, and its trajectory will define the cadence at which AI compute scales over the next decade.

The Generative AI Hardware Materials Market 2026–2036 is the most comprehensive single source on the materials- and packaging-layer supply side of the generative AI hardware build-out. It complements demand-side coverage of foundation models, AI services, and hyperscaler capex by quantifying the physical infrastructure - silicon dies, HBM stacks, advanced packages, substrates, photonics, thermal systems, and power semiconductors - that hyperscaler AI capex commitments translate into across the supply chain.

The report covers nine concentric layers of the AI hardware materials value chain in dedicated chapters: AI accelerator silicon, AI-driven chip design (EDA), high-bandwidth memory and beyond-HBM architectures, advanced packaging and substrates, co-packaged optics and silicon photonics, thermal management, power delivery and the GaN/SiC transition, networking and optical materials, the data centre construction supply chain, and the edge GenAI hardware tier. Each chapter combines bottom-up unit-volume and ASP analysis, capacity and capex tracking, technology-roadmap mapping, and detailed company profiles. Regional analysis covers Taiwan, South Korea, Japan, China, Southeast Asia and India, the United States, Europe, and Israel. A dedicated supply-chain and geopolitics chapter covers the US-China technology competition, Taiwan concentration risk, critical-materials supply, CHIPS Act and European Chips Act implementation, and the parallel China sovereign-AI hardware stack. Sustainability and embodied-carbon analysis covers the operational and embodied emissions profile of AI infrastructure, the PFAS chemistry transition, and the carbon-accounting regulatory framework.

The methodology aggregates segment-level forecasts built from bottom-up unit volumes, ASPs, and content-per-unit analysis, with Base, Bull, and Bear scenarios through 2036 and regional capture forecasts for nine geographies. The strategic outlook frames five defining themes of the GenAI hardware decade, a choke-point map of binding constraints, a strategic investment framework, and an M&A landscape analysis through 2030.

The report is designed for buyers and decision-makers in the Asian foundry, OSAT, memory, substrate, photonics, thermal, and cooling vendor ecosystem; for hyperscalers and AI silicon designers evaluating capacity and supplier strategy; for institutional investors building positions across the AI hardware value chain; and for sovereign-AI program managers planning national AI infrastructure. Coverage spans the full decade from 2026 through 2036 with dedicated treatment of the major architectural inflections, capacity bottlenecks, technology transitions, and geopolitical scenarios that will define the GenAI hardware decade. The result is a single integrated source on the hardware that makes generative AI physically possible.

Contents include:

  • Executive Summary - Key findings; the GenAI hardware bottleneck; materials value chain at a glance; ten-year forecast highlights; strategic implications for Asian foundries, OSAT, memory, substrate, and cooling vendors; major market players (NVIDIA, TSMC, SK hynix, Samsung Electronics, ASE Technology)
  • The Compute Stack Behind Generative AI - Training vs. inference economics; pre-training, post-training, RLHF compute splits; inference token economics and serving infrastructure; test-time compute and reasoning-model demand; cloud, edge, and sovereign AI; hyperscaler clusters at 100,000-GPU scale; enterprise on-prem and neocloud deployments; sovereign AI build-outs; the memory wall in LLM serving; HBM ASP as percentage of AI accelerator BOM; CoWoS as the constraining bottleneck; hyperscaler vs. enterprise vs. sovereign capex
  • AI Accelerator Silicon - NVIDIA Hopper → Blackwell → Blackwell Ultra → Rubin → Rubin Ultra roadmap; NVL72 rack architecture and post-Rubin scale-up; AMD MI300X → MI355X → MI400 trajectory; Intel Gaudi and post-Gaudi; custom hyperscaler ASICs (Google TPU, AWS Trainium/Inferentia, Microsoft Maia/Cobalt, Meta MTIA); ASIC NRE economics and break-even analysis; domain-specific architectures (Cerebras WSE-3, Groq LPU, SambaNova RDU); Chinese AI chip ecosystem (Huawei Ascend, Cambricon, Biren, Moore Threads); TSMC, Samsung, Intel, and SMIC process-node roadmaps; EUV and High-NA EUV adoption; wafer-level integration and reticle stitching
  • AI-Driven Chip Design (EDA) - The EDA bottleneck in the AI hardware era; the recursive loop of AI designing AI hardware; incumbent EDA vendors' AI initiatives; the startup cohort across agentic AI for digital design and verification, physics-AI for simulation and advanced packaging, AI for analog and PCB design, and EDA-adjacent silicon; geographic distribution; AI-EDA tools market forecast 2026–2036
  • High Bandwidth Memory and Beyond - HBM architecture and TSV stacking fundamentals; HBM3/HBM3E, HBM4/HBM4E, HBM5/HBM5E generation roadmap; SK hynix, Samsung, and Micron strategy and capacity outlook; bit-shipment and wafer-capacity forecasts; custom HBM (cHBM) and base-die innovation; standard vs custom HBM revenue split; compute-in-memory and processing-in-memory; emerging memory; memory pooling and CXL fabrics; 3D DRAM post-2030 path
  • Advanced Packaging and Substrate Materials - The 2.5D/3D architecture continuum; TSMC CoWoS-S/L/R roadmap and capacity expansion; CoWoS-Photonics and CoWoP; SoIC, SoIC-X, SoIC-P hybrid-bonded stacks; Intel EMIB and Foveros; Samsung I-Cube/X-Cube/H-Cube; ABF supply oligopoly; glass-core substrates; interposer materials (silicon TSV, glass, organic RDL); hybrid bonding equipment ecosystem; HBM4 hybrid bonding adoption; OSAT capacity and Asian dominance
  • Co-Packaged Optics and Silicon Photonics for AI - The optical interconnect imperative; CPO architecture and two network layers; TSMC COUPE, CoWoS-Photonics, iOIS; CoWoP and the NVIDIA Rubin transition; ASE VIPack and the merchant photonics packaging layer; optical I/O chiplets (AyarLabs TeraPHY, Lightmatter Passage, Celestial AI / Marvell); switch silicon and co-packaged optical engines; silicon photonics foundries; photonics packaging materials supply chain; market sizing 2026–2036
  • Thermal Management for AI Data Centers - The thermal crisis at the package level; thermal interface materials (liquid metal TIM, solder TIM, diamond-based TIMs); heat spreaders, vapor chambers, heat pipes; cold plates and direct-to-chip liquid cooling; the cold plate supply chain bottleneck; single-phase and two-phase immersion cooling; PFAS challenge; microfluidic and in-package cooling; coolant distribution units, manifolds, and facility plumbing; market forecast 2024–2036
  • Power Delivery and GaN/SiC Transition - The power crisis from 12V to 48V to 800V HVDC; 48V tray architecture and OCP standard; 800V HVDC at the rack and the Rubin transition; SiC devices and substrate supply; GaN devices (lateral, vertical, cascode); GaN in AI server PSU applications; vertical GaN post-2027 trajectory; voltage regulator modules and multi-phase point-of-load; Monolithic Power Systems advantage in AI VRMs; package-integrated VRM; server PSUs and rack rectifier shelves; backside power delivery (Intel PowerVia, TSMC A16, Samsung BSPDN); market forecast 2024–2036
  • Networking and Optical Materials - The three network layers in an AI datacenter; switch silicon roadmap (Broadcom Tomahawk 6 Davisson, NVIDIA Spectrum-X/Quantum-X); Ultra Ethernet Consortium; pluggable optical transceivers; volume transceiver suppliers; optical transceiver assembly (Fabrinet, Jabil, Luxshare); DSP and SerDes; Marvell's DSP business; Linear Pluggable Optics (LPO); III-V materials (InP, GaAs, GaN-Photonics); NICs, DPUs, and SmartNICs; cables, connectors, and DAC; market forecast 2024–2036
  • Data Center Construction and Sustainability - Power infrastructure (grid, on-site generation, SMRs); behind-the-meter natural-gas; nuclear restart and SMR procurement; renewable energy procurement at hyperscaler scale; switchgear and transformers; facility-level cooling architecture; construction supply chain and modular datacenter architecture; geographic concentration and site selection; PUE, WUE, and sustainability metrics; carbon-free energy accounting; embodied carbon; regulatory framework
  • Edge GenAI Hardware - AI smartphones and Apple Neural Engine evolution; AI PCs (NVIDIA, Snapdragon X Elite); NVIDIA Jetson and embedded AI; Jetson AGX Thor and humanoid robotics; automotive AI silicon (NVIDIA DRIVE Thor, Tesla FSD); humanoid robotics unit volumes and silicon revenue forecast; edge AI startup cohort; edge AI memory (LPDDR5X, on-chip SRAM, eMRAM); market forecast 2024–2036
  • Regional Analysis - Taiwan, South Korea, Japan, China, Southeast Asia and India, the United States, Europe and Israel; aggregate regional capture scenario analysis 2026–2036
  • Supply Chain and Geopolitics - The China strategy and sovereign stack; SMIC and the EUV-free leading-edge path; CXMT and JHICC HBM ramp; US CHIPS Act implementation (TSMC Arizona, Samsung Taylor, Intel Foundry, Micron); European Chips Act; critical materials (rare earths, gallium and germanium, neon and specialty gases, specialty quartz and substrates); single-point-of-failure analysis; supply-chain resilience scenarios; sovereign AI as a strategic demand driver
  • Sustainability and Embodied Carbon - Operational emissions; cooling energy tax; embodied carbon in semiconductor manufacturing; PFC and process-gas problem; PFAS chemistry transition; renewable energy procurement; nuclear restart and SMR; heat recovery and district heating; circular economy; carbon accounting standards (Scope 1/2/3, EU CSRD, SEC); green manufacturing practices
  • Market Forecasts 2026–2036 - Total market Base case; Bull/Base/Bear scenarios; AI accelerator silicon, HBM, advanced packaging, photonics packaging, thermal, power, networking, datacenter construction, and edge AI sub-segment forecasts; regional capture forecast; customer-tier forecast; key forecast risks and sensitivities
  • Strategic Outlook - Five defining themes; choke-point map; strategic investment framework; M&A landscape and strategic consolidation through 2030; sensitivity analysis; strategic implications by stakeholder

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 T-Head (PingTouGe), Alpha Assembly Solutions (MacDermid Alpha), Alphabet Inc. (Google), Amazon Web Services (AWS), Ambarella, Amber Semiconductor (AmberSemi), Amkor Technology, Amphenol Corporation, Anduril Industries, Apple Inc., Applied Materials, Apptronik, Arago, 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), d-Matrix, DEEPX, Delta Electronics, 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, Great Sky, Green Revolution Cooling (GRC), GreenWaves Technologies, Groq Inc., GS Microelectronics (GSME), Hailo Technologies, Henkel AG, Heraeus, Hesheng Silicon Industry, Hisense Broadband, Hitachi Energy, Hon Hai (Foxconn), Honeywell International, Horizon Robotics, Hua Tian Technology (HT-Tech), Huawei Technologies (HiSilicon), Hummink, Ibiden Co. Ltd., Iceotope Technologies, Iluvatar CoreX, Indium Corporation, Infineon Technologies AG, Innolight Technology, Innoscience Technology, 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, Lace Lithography, Lam Research, Lambda Inc., LG Innotek, Lightmatter, Liquid Wire Inc., LiquidStack, LiteOn Technology, LOTES Co., Lumentum Holdings, Lumotive, Luxshare Precision, M&I Materials, Macronix International, Maieutic Semiconductor, Majestic Labs, Marvell Technology, MatX, MediaTek, Mesh Optical Technologies, Meta Platforms, Microchip Technology, Micron Technology Inc., Microsoft Corporation, Mitsubishi Electric, Mobileye Global, Monolithic Power Systems (MPS), Montage Technology, Moore Threads Technology, Morphing Machines, Movandi, Multibeam Corporation, Murata Manufacturing, Mythic, Nan Ya PCB, Nanya Technology, Navitas Semiconductor, NcodiN, Neo Semiconductor, NeoGraf Solutions, NeoLogic, Netrasemi, NEURA Robotics, Neurophos, Normal Computing, NVIDIA Corporation, NXP Semiconductors, Olix, Omni Design Technologies, onsemi (ON Semiconductor), OpenLight, Optalysys, Opticore, Oracle Corporation (Oracle Cloud Infrastructure), Oxmiq Labs, Panasonic, Parker Chomerics, Patentix, Positron AI, Power Integrations, Powerchip Semiconductor (PSMC), PowerLattice, Powertech Technology, Primemas and more......

Table of Contents

1 EXECUTIVE SUMMARY

  • 1.1 Key Findings
  • 1.2 The Generative AI Hardware Bottleneck
  • 1.3 Materials Value Chain at a Glance
  • 1.4 Ten-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
    • 2.6.1 Alphabet Inc. (Google)
    • 2.6.2 Amazon Web Services (AWS)
    • 2.6.3 CoreWeave Inc.
    • 2.6.4 Crusoe Energy Systems
    • 2.6.5 G42
    • 2.6.6 Lambda Inc.
    • 2.6.7 Meta Platforms
    • 2.6.8 Microsoft Corporation
    • 2.6.9 Oracle Corporation (Oracle Cloud Infrastructure)

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 (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 2026–2036
  • 4.7 Strategic Implications
  • 4.8 Company profiles (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: 2031–2036 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 (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 (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 2026–2036
  • 7.10 Company Profiles (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 2024–2036
  • 8.8 Company Profiles (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 2024–2036
  • 9.9 Company Profiles (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 2024–2036
  • 10.9 Company Profiles (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 2024–2036

12 EDGE GEN AI 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 2024–2036
  • 12.10 Company Profiles (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 2026–2036

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 2026–2036
    • 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: GEN AI HARDWARE 2026-2036

  • 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. Ten-Year Forecast Summary: GenAI Hardware Materials Market 2026–2036 (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 2026–2036
  • 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 2024–2036 (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 2024–2036 (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 2024–2036 (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 2024–2036 (US $M)
  • Table 56. Heat Spreader and Vapor Chamber Suppliers
  • Table 57. Heat Spreader and Heat Sink Revenue Forecast 2024–2036 (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 2024–2036 (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 2024–2036 (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 2024–2036 (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 2024–2036 (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 2024–2036 (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, 2026–2036 (US $B, excl. construction supply chain)
  • Table 128. AI Accelerator Silicon Sub-Segment Forecast 2024–2036 (US $B)
  • Table 129. HBM and AI-Tied Memory Sub-Segment Forecast 2024–2036 (US $B)
  • Table 130. Advanced Packaging Sub-Segment Forecast 2024–2036 (US $B, AI-tied)
  • Table 131. Photonics Packaging Sub-Segment Forecast 2024–2036 (US $B)
  • Table 132. Thermal Management Sub-Segment Forecast 2024–2036 (US $B, AI-tied)
  • Table 133. Power Delivery (AI Datacenter Tied) Sub-Segment Forecast 2024–2036 (US $B)
  • Table 134. Networking and Optical (AI-Tied) Sub-Segment Forecast 2024–2036 (US $B)
  • Table 135. Datacenter Construction Supply Chain Sub-Segment Forecast 2024–2036 (US $B)
  • Table 136. Edge AI Silicon Sub-Segment Forecast 2024–2036 (US $B)
  • Table 137. Regional GenAI Hardware BoM Capture Forecast, 2026–2036, Base Case (%)
  • Table 138. Total GenAI Hardware Demand by Customer Tier, Base Case 2026–2036 (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 2026–2036
  • Figure 4. Bull, Base, and Bear Scenario Comparison 2026–2036
  • Figure 5. Asia-Pacific Capture Rate of GenAI Hardware Value 2026–2036
  • 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 2024–2036
  • 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 2023–2036 (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 2026–2036
  • 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 2024–2036
  • Figure 39. Compute Demand vs. Interconnect Bandwidth Gap
  • Figure 40. Photonics Packaging Revenue Forecast for AI Applications 2024–2036
  • Figure 41. AI Accelerator TDP and Cooling Architecture Trajectory 2022–2036
  • 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 2024–2036 by Segment, Base Case
  • Figure 47. GenAI Hardware Market Bull/Base/Bear Scenarios 2024–2036
  • Figure 48. Sensitivity of 2030 Forecast to Key Variables