用於人工智慧（AI）半導體的高頻寬記憶體（HBM）和先進封裝材料：2027-2037 年

高頻寬記憶體 (HBM) 和先進封裝材料構成了人工智慧 (AI) 所用半導體的供應基礎。 AI 加速器封裝由堆疊式 DRAM、邏輯晶片、矽或玻璃中介層、多層有機基板和整合式散熱解決方案組成。記憶體和封裝的總成本佔封裝總成本的比例遠高於邏輯晶片本身的成本。

此供應鏈由五層組成。 HBM裝置佔最大的價值佔有率。第二層是先進封裝服務，包括台積電的CoWoS和SoIC平台、英特爾的EMIB和Foveros、三星的I-Cube和X-Cube，以及來自OSAT的同等技術。第三層是基板和中介層。第四層是封裝和儲存材料，例如介電薄膜、覆銅層壓板、玻璃布、光阻劑、電鍍化學品、底部填充劑和模塑化合物。第五層是散熱材料和封裝內冷卻硬體。

需求受加速器出貨量、每個封裝的HBM堆疊層數以及封裝面積的驅動。這三者均呈上升趨勢。堆疊層數正從8層和12層向16層及更高層發展，堆疊層數也在增加。此外，隨著光阻放大倍率的提高，封裝面積也在擴大。

目前，多項技術變革正在進行中。在HBM堆疊結構中，混合銅-銅鍵結可望取代微凸塊互連，從而無需在堆疊結構內部填充底部材料和焊料，同時引入鍵合介質及相關耗材。玻璃芯基板和玻璃中介層目前正在進行認證，以取代有機芯和矽中介層。面板封裝正在開發中，以取代晶圓級組裝。封裝的熱設計功率不斷提高，溫度控管也正從傳統的蓋板和導熱界面材料轉向微通道蓋板和矽直接微流體冷卻。

材料產業的特點是供應商集中度高。對於疊層介電薄膜、低模量玻璃纖維布和一些組裝耗材而言，認證供應商數量有限。與產品迭代周期相比，這些材料的認證週期較長，限制了替代供應商的引進速度。

在客製化HBM（高密度記憶體）領域，加速器供應商指定基於邏輯的晶片，價值正從記憶體製造商轉移到邏輯晶圓代工廠。生產集中在韓國、台灣和日本。韓國和台灣在整個價值鏈中佔了大部分價值。美國正透過投資國內記憶體和封裝技術來擴大其市場佔有率。中國則在出口限制下建構國內供應鏈。

「面向人工智慧半導體的高頻寬記憶體 (HBM) 和先進封裝材料 - 2027–2037」報告對用於高頻寬記憶體和人工智慧半導體製造的先進封裝材料進行了為期 11 年的市場分析和預測。本報告涵蓋五個價值層：HBM 裝置、先進封裝服務、基板和中介層、封裝和儲存材料，以及散熱材料和封裝內冷卻。

本報告以2026年固定美元價格為基準，對2027年至2037年每年的需求進行了預測，並按層數、材料類別、應用領域和地區進行了細分，同時列出了基準情景、悲觀情景和樂觀情景。材料需求模型基於堆疊高度、堆疊層數、黏合界面面積和封裝體面積等物理因素，並以貨幣和數量兩種形式呈現。

本報告包含一個針對HBM的逐層材料消耗模型，穿透矽通孔（TSV）和電鍍化學品、CMP耗材、鍵合介質、底部填充材料和非導電薄膜、微凸塊焊料以及減薄和處理材料。對於每類材料，均使用基於AI相關等級計算的CR1、CR3和Herfindahl-Hirschman指數來評估供應商集中度。

本研究針對材料供應商、基板和封裝公司、記憶體製造商、半導體製造設備供應商、加速器供應商以及需要細分市場預測和供應鏈風險評估的投資者。

目錄包括：

• 摘要整理、主要發現和市場概覽表
• 明確研究範圍、調查方法、模型結構和情境。
• AI需求基礎：加速器出貨量、每個包裝的HBM堆疊數量、包裝材料清單（BOM）和包裝面積藍圖
• HBM技術：HBM2-HBM6規格、客製化HBM、晶片基礎價值趨勢、鍵結藍圖、堆疊式溫度控管、3D DRAM
• HBM市場預測：出貨量、平均售價、收入、世代分佈、供應商佔有率、產能及顧客需求
• HBM堆疊材料：每個堆疊的消耗模型和按材料類別分類的預測
• 先進封裝平台：CoWoS 的各種變體、SoIC、EMIB 和 Foveros、OSAT 平台、面板級封裝、共封裝光學元件、產能分析
• 先進封裝和基板材料：介電薄膜、芯材、銅箔和電鍍、光阻、光阻劑以及向玻璃芯材的過渡
• 溫度控管材料和封裝內冷卻：按化學成分分類的熱界面材料、微通道蓋和微流體冷卻。
• 供應鏈分析：集中度、瓶頸、出口限制以及區域生產與消費。
• 按分層、應用、地區和材料類別進行綜合預測（包括價格指數和情境分析）
• 142 家公司的企業概況（包括所有權結構、財務資料、資金籌措歷史和技術地位）。公司簡介包括：3M、AMD（超微半導體）、旭硝子（AGC）、味之素精密技術、Akhan Semiconductor、阿里巴巴集團（平頭）、Alpha Assembly Solutions（MacDermid Alpha）、Alphabet（Google）、亞馬遜網路服務（AWS）、Amkor Technology、蘋果、應用材料、日月光半導體控股有限公司、Asetek、亞洲關鍵組件（AVC）、ASMPT、Asperitas、Astera Labs、AT&S、Auras Technology、Avalanche Technology、BE Semiconductor Industries（BESI）、Biren Technology、博通、寒武紀科技、Carbice、Cerebras Technologies Technologiesbice、Cerebras Technologies、博通、寒武紀科技. Systems、CoreWeave、Corintis、康寧、Crossbar、長信儲存科技（CXMT）、台達電子、d-Matrix、陶氏化學、Element Six、Eliyan、Engineered Fluids、Etched、EV Group、Everspin Technologies。 Fabric8Labs、鐵電記憶體公司 (FMC)、富士康工業網際網路 (FII)、Frore Systems、Fujipoly、Furiosa AI、G42、Green Revolution Cooling (GRC)、Groq、漢高、賀利氏、合盛矽業、鴻海 (富士康導)、霍尼韋爾國際、華天科技、華思科技有限公司、Iluden、Iluden Technologies） CoreX、銦泰公司、英特爾、英特爾晶圓代工、江電集團、捷酷科技、Kaneka、Kinsus Interconnect Technology、鎧俠控股、Kulicke & Soffa、京瓷、Lam Research、Lambda、LG Innotek、Liquid Wire、LiquidStack、京瓷、Lam Research、Lambda、LG Innotek、Liquid Wire、LiquidStack、宏瓷d、MetaS、Meta Technologyd、MetaS、Meta Technology、MetaS、Meta Technology、Meta」科、Meta」科。其中包括 Platforms、M&I Materials、美光科技、微軟、Montage Technology、Moore Threads Technology、Multibeam、村田製作所、南亞PCB、南亞科技和Neo Semiconductor。
• 市場展望、技術藍圖與策略結論

目錄

第1章摘要整理

第2章：引言、範圍與調查方法

第3章：人工智慧的需求基礎

• 計算堆疊以及內存在其中的位置。
• AI加速器出貨量與包裝需求
• 每個包裹的堆疊數量和 HBM 需求橋
• 為什麼記憶體和封裝會影響加速器的成本
• 記憶牆
• 封裝面積增大和光罩限制
• 超大規模資料中心業者中心資本投資與需求範圍
• 主權與中國的訴求

第4章：高頻寬記憶體：技術與藍圖

• HBM架構和TSV堆疊的基本原理
• 世代藍圖
• 客製化 HBM 和基礎模具的價值轉移
• 債券整合藍圖
• 堆疊內部的熱結構
• 記憶體內和近記憶體計算
• HBM 背後的儲存層
• 3D DRAM及2032年後的展望

第5章：HBM市場預測（2027-2037）

• 銷售量、平均售價和收入預測
• 按世代分類的收入
• 供應商市場佔有率和競爭結構
• 產能預測
• 客戶對HBM的需求
• 該地區高血脂蛋白（HBM）的生產
• HBM定價趨勢

第6章：HBM堆疊的材料與耗材

• HBM材料清單
• 每堆材料消耗量（按代）
• TSV 形成：接種、屏障、種子
• CMP：針對混合鍵結分別產生的類別
• 微凸冶金及其衰落
• 基材填充材：毛細管、非導電薄膜、模塑製品
• 混合結用電介質和耗材
• 減薄、載體鍵結和處理
• HBM材料市場預測
• HBM材料科學的重點與資質

第7章：人工智慧半導體的先進封裝平台

• 2.5D 和 3D 建築連續性
• CoWoS 和容量限制
• SOIC和3D混合鍵結邏輯
• 英特爾和三星平台
• OSAT層
• 面板級包裝
• 光電介面
• 先進包裝服務市場預測

第8章：先進封裝與基板

• 一堆包裝材料
• 基材結構和體型
• 介電膜的形成
• 覆銅層壓板、預浸料、玻璃布、填充材
• 銅箔、電鍍化學品、光阻劑
• 中介材料
• 玻璃核中的相變及其材料效應
• 組裝材料：底部填充膠、模塑化合物、焊料
• 包裝材料市場預測
• 區域包裝材料供應

第9章：導熱材料和封裝內冷卻

• 包裝熱問題
• 熱界面堆疊
• 液態金屬、相變與約束問題
• 底部冷卻和直接矽冷卻
• 鑽石和人造碳
• 熱材料市場預測
• 熱能產業的供應商整合

第10章：供應鏈、集中度與地緣政治

• 從原料到加速器的鍊式反應
• 出口限制和並行堆疊
• 取得資格的期限是一項具有約束力的條件。
• 資本投資的不對稱性
• 風險登記冊

第11章：2027-2037年綜合市場預測

• 總價值池
• 按應用領域分類的預測
• 區域預測
• 綜合材料預測
• 價格和平均售價的趨勢
• 情境分析

第12章：公司簡介

• 記憶體製造商（21家公司簡介）
• 人工智慧加速器、超大規模資料中心業者和系統供應商（35 家公司簡介）
• 晶圓代工廠、整合裝置製造商、外包半導體測試與製造公司（18 家公司簡介）
• 基板、中介層和玻璃供應商（15家公司簡介）
• 包裝及電子材料供應商（15家公司簡介）
• 溫度控管和封裝內冷卻（26家公司簡介）
• 設備和製程技術（12家公司簡介）

第13章 市場展望

• 2027-2037年市場展望
• 技術展望
• 10項戰略結論
• 影響這項預測的因素
• 最終考慮

附錄

參考

High-bandwidth memory (HBM) and advanced packaging materials form the supply base for artificial intelligence semiconductors. An AI accelerator package combines stacked DRAM, a logic base die, a silicon or glass interposer, a multilayer organic substrate and an integrated thermal solution. Memory and packaging together account for a larger share of package cost than the logic die.

The supply chain comprises five layers. HBM devices represent the largest share of value. Advanced packaging services, including TSMC's CoWoS and SoIC platforms, Intel's EMIB and Foveros, Samsung's I-Cube and X-Cube, and OSAT equivalents, form the second layer. Substrates and interposers form the third. Packaging and memory materials, including build-up dielectric film, copper-clad laminate, glass cloth, photoresist, plating chemistry, underfill and mould compound, form the fourth. Thermal materials and in-package cooling hardware form the fifth.

Demand is driven by accelerator shipments, by the number of HBM stacks per package, and by package body area. All three are increasing. Stack heights are progressing from 8-Hi and 12-Hi toward 16-Hi and beyond, build-up layer counts are rising, and package body areas are growing as reticle multiples increase.

Several technology transitions are underway. Hybrid copper-to-copper bonding is expected to displace micro-bump interconnect in HBM stacks, removing in-stack underfill and solder while introducing bonding dielectrics and associated consumables. Glass core substrates and glass interposers are entering qualification as alternatives to organic cores and silicon interposers. Panel-format packaging is being developed as a successor to wafer-format assembly. Package thermal design power is rising, moving thermal management from conventional lids and thermal interface materials toward micro-channel lids and direct-to-silicon microfluidic cooling.

The materials layer is characterised by high supplier concentration. Build-up dielectric film, low-coefficient glass cloth and several assembly consumables have limited qualified supply bases. Qualification cycles for these materials are long relative to product generations, which constrains the pace at which alternative sources can be introduced.

Custom HBM, in which accelerator vendors specify the logic base die, is transferring value from memory manufacturers to logic foundries. Production is concentrated in Korea, Taiwan and Japan. Korea and Taiwan account for the majority of value across the chain. The United States is increasing share through domestic memory and packaging investment. China is developing a domestic supply chain under export-control conditions.

High-Bandwidth Memory (HBM) and Advanced Packaging Materials for Artificial Intelligence (AI) Semiconductors: 2027–2037 provides market analysis and eleven-year forecasts for high-bandwidth memory and the advanced packaging materials used in AI semiconductor manufacture. It covers five value layers: HBM devices, advanced packaging services, substrates and interposers, packaging and memory materials, and thermal materials and in-package cooling.

Forecasts are presented annually from 2027 to 2037 in constant 2026 US dollars, segmented by layer, material class, application and region, with base, bear and bull scenarios. Material demand is modelled from physical drivers, including stack heights, build-up layer counts, bonding interface area and package body area, and is presented in both value and volume terms.

The report includes a per-stack materials consumption model for HBM covering through-silicon via and plating chemistry, CMP consumables, bonding dielectrics, underfill and non-conductive film, micro-bump solder, and thinning and handling materials. Supplier concentration is assessed for each material class using CR1, CR3 and Herfindahl-Hirschman Index measures calculated at AI-relevant grade.

The study is intended for materials suppliers, substrate and packaging companies, memory manufacturers, semiconductor equipment suppliers, accelerator vendors, and investors requiring segment-level forecasts and supply-chain risk assessment.

Contents include:

• Executive summary, headline findings and market summary tables
• Scope, methodology, model architecture and scenario definitions
• AI demand base: accelerator shipments, HBM stacks per package, package bill of materials, package area roadmap
• HBM technology: HBM2 to HBM6 specifications, custom HBM, base-die value migration, bonding roadmap, in-stack thermal management, 3D DRAM
• HBM market forecasts: shipments, average selling prices, revenue, generation mix, supplier shares, capacity and demand by customer
• HBM stack materials: per-stack consumption model and forecasts by material class
• Advanced packaging platforms: CoWoS variants, SoIC, EMIB and Foveros, OSAT platforms, panel-level packaging, co-packaged optics, capacity analysis
• Advanced packaging and substrate materials: build-up dielectric film, core materials, copper foil and plating, photoresist, interposers, glass core transition
• Thermal materials and in-package cooling: thermal interface materials by chemistry, micro-channel lids, microfluidic cooling
• Supply chain analysis: concentration, chokepoints, export controls, regional production and consumption
• Consolidated forecasts by layer, application, region and material class, with price indices and scenario analysis
• 142 company profiles, including ownership, financial data, funding history and technology positions. Companies profiled include 3M, Advanced Micro Devices (AMD), AGC (Asahi Glass), Ajinomoto Fine-Techno, Akhan Semiconductor, Alibaba T-Head (PingTouGe), Alpha Assembly Solutions (MacDermid Alpha), Alphabet (Google), Amazon Web Services (AWS), Amkor Technology, Apple, Applied Materials, ASE Technology Holding, Asetek, Asia Vital Components (AVC), ASMPT, Asperitas, Astera Labs, AT&S, Auras Technology, Avalanche Technology, BE Semiconductor Industries (BESI), Biren Technology, Broadcom, Cambricon Technologies, Carbice, Cerebras Systems, Chemours, ChipMOS Technologies, Cooler Master, CoolIT Systems, CoreWeave, Corintis, Corning, Crossbar, CXMT (ChangXin Memory Technologies), Delta Electronics, d-Matrix, Dow, Element Six, Eliyan, Engineered Fluids, Etched, EV Group, Everspin Technologies, Fabric8Labs, Ferroelectric Memory Company (FMC), Foxconn Industrial Internet (FII), Frore Systems, Fujipoly, Furiosa AI, G42, Green Revolution Cooling (GRC), Groq, Henkel, Heraeus, Hesheng Silicon Industry, Hon Hai (Foxconn), Honeywell International, Hua Tian Technology, Huawei Technologies (HiSilicon), Ibiden, Iceotope Technologies, Iluvatar CoreX, Indium Corporation, Intel, Intel Foundry, JCET Group, JetCool Technologies, Kaneka, Kinsus Interconnect Technology, Kioxia Holdings, Kulicke & Soffa, Kyocera, Lam Research, Lambda, LG Innotek, Liquid Wire, LiquidStack, Macronix International, Marvell Technology, MatX, MediaTek, Meta Platforms, M&I Materials, Micron Technology, Microsoft, Montage Technology, Moore Threads Technology, Multibeam, Murata Manufacturing, Nan Ya PCB, Nanya Technology, Neo Semiconductor and more.....
• Market outlook, technology roadmap and strategic conclusions

Table of Contents

1. EXECUTIVE SUMMARY

• 1.1 Scope and headline definitions
• 1.2 Key findings
• 1.3 Market size, growth and CAGR summary
• 1.4 Headline forecasts by material class
• 1.5 Headline forecasts by region
• 1.6 Supply concentration at a glance
• 1.7 The asymmetry between device growth and material growth
• 1.8 Strategic implications for suppliers
• 1.9 Strategic implications for buyers
• 1.10 Major market players

2. INTRODUCTION, SCOPE AND METHODOLOGY

• 2.1 Report objectives
• 2.2 Product scope: the five value layers
• 2.3 Geographic scope
• 2.4 Units, conventions and abbreviations
• 2.5 Forecast methodology
  • 2.5.1 Device shipments to stack demand
  • 2.5.2 Stack demand to material demand
  • 2.5.3 Price modelling and ASP erosion
• 2.6 Demand scenario definitions

3. THE AI DEMAND BASE

• 3.1 The compute stack and where memory sits in it
• 3.2 AI accelerator shipments and package demand
• 3.3 Stacks per package and the HBM demand bridge
• 3.4 Why memory and packaging dominate accelerator cost
• 3.5 The memory wall in serving
• 3.6 Package area growth and the reticle constraint
• 3.7 Hyperscaler capital expenditure and the demand envelope
• 3.8 Sovereign and Chinese demand

4. HIGH-BANDWIDTH MEMORY: Technology and Roadmap

• 4.1 HBM architecture and TSV stacking fundamentals
• 4.2 Generation roadmap
  • 4.2.1 HBM3E: the generation that funded the transition
  • 4.2.2 HBM4 and HBM4E: interface doubling and the logic base die
  • 4.2.3 HBM5, HBM5E and HBM6
• 4.3 Custom HBM and the base-die transfer of value
• 4.4 The bonding integration roadmap
• 4.5 Thermal architecture inside the stack
• 4.6 Processing-in-memory and near-memory compute
• 4.7 The memory tier behind HBM
• 4.8 3D DRAM and the post-2032 path

5. HBM MARKET FORECASTS 2027–2037

• 5.1 Unit, ASP and revenue forecast
• 5.2 Revenue by generation
• 5.3 Supplier share and competitive structure
• 5.4 Capacity outlook
• 5.5 HBM demand by customer
• 5.6 Regional HBM manufacture
• 5.7 HBM pricing dynamics

6. HBM STACK MATERIALS AND CONSUMABLES

• 6.1 The HBM materials bill
• 6.2 Material consumption per stack by generation
• 6.3 TSV formation: plating, barrier and seed
• 6.4 CMP: the class that hybrid bonding creates
• 6.5 Micro-bump metallurgy and its decline
• 6.6 Underfill: capillary, non-conductive film and molded
• 6.7 Hybrid bonding dielectric and consumables
• 6.8 Thinning, carrier bonding and handling
• 6.9 HBM materials market forecast
• 6.10 Concentration and qualification in the HBM material base

7. ADVANCED PACKAGING PLATFORMS FOR AI SEMICONDUCTORS

• 7.1 The 2.5D and 3D architecture continuum
• 7.2 CoWoS and the capacity constraint
  • 7.2.1 The CoWoS sub-variants
• 7.3 SoIC and 3D hybrid-bonded logic
• 7.4 Intel and Samsung platforms
• 7.5 The OSAT layer
• 7.6 Panel-level packaging
• 7.7 The photonics interface
• 7.8 Advanced packaging services market forecast

8. ADVANCED PACKAGING AND SUBSTRATE MATERIALS

• 8.1 The package materials stack
• 8.2 Substrate architecture and body size
• 8.3 Build-up dielectric film
• 8.4 Copper-clad laminate, prepreg, glass cloth and fillers
• 8.5 Copper foil, plating chemistry and photoresist
• 8.6 Interposer materials
• 8.7 The glass core transition and its material consequences
• 8.8 Assembly materials: underfill, mold compound and solder
• 8.9 Package materials market forecast
• 8.10 Regional supply of package materials

9. THERMAL MATERIALS AND IN-PACKAGE COOLING

• 9.1 The thermal problem at the package
• 9.2 The thermal interface stack
• 9.3 Liquid metal, phase change and the confinement problem
• 9.4 In-package and direct-to-silicon cooling
• 9.5 Diamond and engineered carbon
• 9.6 Thermal materials market forecast
• 9.7 Supplier consolidation in thermal

10. SUPPLY CHAIN, CONCENTRATION AND GEOPOLITICS

• 10.1 The chain from material to accelerator
• 10.2 Export controls and the parallel stack
• 10.3 Qualification duration as the binding constraint
• 10.4 Capacity investment asymmetry
• 10.5 Risk register

11. CONSOLIDATED MARKET FORECASTS 2027–2037

• 11.1 Total value pool
• 11.2 Forecast by application segment
• 11.3 Forecast by region
• 11.4 Consolidated materials forecast
• 11.5 Price and ASP trajectories
• 11.6 Scenario analysis
  • 11.6.1 Sensitivity to the stacks-per-package coefficient
  • 11.6.2 Sensitivity to hybrid bonding timing
  • 11.6.3 Sensitivity to glass core adoption

12. COMPANY PROFILES

• 12.1 Memory manufacturers (21 company profiles)
• 12.2 AI accelerator, hyperscaler and system vendors (35 company profiles)
• 12.3 Foundries, IDMs and OSATs (18 company profiles)
• 12.4 Substrate, interposer and glass suppliers (15 company profiles)
• 12.5 Packaging and electronic materials suppliers (15 company profiles)
• 12.6 Thermal management and in-package cooling (26 company profiles)
• 12.7 Equipment and process technology (12 company profiles)

13. MARKET OUTLOOK

• 13.1 Market outlook 2027–2037
• 13.2 Technology outlook
• 13.3 Ten strategic conclusions
• 13.4 What would change this forecast
• 13.5 Closing observation

APPENDICES

• Appendix A - Methodology notes

REFERENCES

• 14.1 Retrieved sources
• 14.2 Primary corporate, standards and institutional sources

List of Tables

• Table 1. Headline findings summary (Base case).
• Table 2. The AI memory and advanced packaging value pool, 2027–2037 (US$ billion).
• Table 3. Materials and consumables summary by stream, 2027–2037 (US$ million).
• Table 4. Value pool by region of manufacture, 2027–2037 (US$ billion).
• Table 5. Concentration summary, HBM and advanced packaging material classes (concentration ratios in per cent; qualification in months).
• Table 6. Indexed growth, 2027 = 100.
• Table 7. Model architecture and principal coefficients (coefficients and their units as stated).
• Table 8. Scenario summary, total value pool 2027–2037 (US$ billion).
• Table 9. Training versus inference: memory and packaging requirements (qualitative comparison).
• Table 10. AI accelerator package shipments by class, 2026–2037 (thousand units).
• Table 11. HBM stacks per package by accelerator class, 2026–2037 (HBM stacks per package).
• Table 12. HBM stack demand bridge, 2026–2037 (million stacks).
• Table 13. Indicative flagship accelerator package cost structure, 2024–2037 (per cent of factory cost).
• Table 14. Compute-to-bandwidth ratio by accelerator generation (relative throughput indexed to Volta = 1.0; bandwidth in TB/s).
• Table 15. Package area roadmap for flagship accelerators, 2024–2037 (package body area in mm²; layers per side).
• Table 16. AI infrastructure capital expenditure and derived component demand, 2026–2037 (capital expenditure in US$ billion; derived demand as stated).
• Table 17. Non-hyperscaler AI accelerator demand, 2027–2037 (thousand packages).
• Table 18. HBM stack construction elements and their function (descriptive).
• Table 19. HBM generation technical specifications, HBM2 to HBM6 (specifications per stack).
• Table 20. HBM4 supplier status and positioning, mid-2026 (status as at mid-2026).
• Table 21. Custom HBM programmes, suppliers and base-die sources (programmes and first volume year).
• Table 22. Standard versus custom HBM revenue split, 2026–2037 (US$ billion).
• Table 23. HBM bonding integration roadmap (bond pitch in µm; interconnect density in I/O per mm²).
• Table 24. Hybrid bonding adoption in DRAM stacks, 2026–2037 (per cent of stacks shipped).
• Table 25. In-stack thermal architecture adoption and content per stack (adoption in per cent of stacks; content in US$ per stack).
• Table 26. Near-memory and in-memory computing landscape (qualitative landscape).
• Table 27. Memory tier characteristics for AI serving (bandwidth in GB/s; latency in ns; cost in US$ per GB).
• Table 28. 3D DRAM readiness assessment, 2026 (readiness assessment, 2026).
• Table 29. HBM unit shipments, ASP and revenue forecast, 2026–2037 (shipments in millions; ASP in US$ per stack; revenue in US$ billion).
• Table 30. HBM revenue by generation, 2026–2037 (per cent of HBM revenue).
• Table 31. HBM capacity per stack and bit shipment forecast, 2026–2037 (capacity in GB per stack; bit shipments in exabytes).
• Table 32. HBM revenue share by supplier, 2022–2037F (per cent).
• Table 33. HBM wafer capacity and share of total DRAM wafer starts, 2026–2037 (wafer starts in thousand wafers per month; share in per cent).
• Table 34. Announced HBM-related capacity investments, 2025–2030 (announced investment in US$ billion).
• Table 35. HBM consumption by customer, 2026–2037 (million stacks).
• Table 36. HBM manufacture by region, 2027–2037 (US$ billion).
• Table 37. HBM pricing by generation and configuration, 2026–2037 (US$ per stack).
• Table 38. HBM stack material classes, function and supply base (descriptive; supply base as stated).
• Table 39. Material consumption per HBM stack by generation (consumption per stack, units as stated per row).
• Table 40. TSV formation material demand, 2027–2037 (US$ million).
• Table 41. CMP and cleaning consumables demand, 2027–2037 (US$ million).
• Table 42. Micro-bump and solder material demand, 2027–2037 (US$ million).
• Table 43. Underfill and encapsulation demand, 2027–2037 (US$ million).
• Table 44. Hybrid bonding consumables demand, 2027–2037 (US$ million).
• Table 45. Thinning, carrier and handling consumables demand, 2027–2037 (US$ million).
• Table 46. HBM stack materials and consumables market, 2027–2037 (US$ million).
• Table 47. HBM material class concentration and entry conditions.
• Table 48. Advanced packaging platform comparison for AI applications.
• Table 49. CoWoS-class capacity forecast by variant, 2026–2037 (thousand wafer equivalents per month).
• Table 50. CoWoS-class supply and demand balance, 2026–2033.
• Table 51. SoIC variants and AI adoption.
• Table 52. Comparative platform roadmap, TSMC / Intel / Samsung.
• Table 53. OSAT capacity, AI exposure and advanced packaging platforms.
• Table 54. Panel-level packaging adoption forecast, 2027–2037.
• Table 55. Co-packaged optics packaging demand, 2027–2037.
• Table 56. Advanced packaging services revenue for AI semiconductors, 2027–2037 (US$ billion).
• Table 57. Package material classes mapped to process step and multiplier.
• Table 58. Substrate specification roadmap for AI accelerators, 2027–2037.
• Table 59. Build-up dielectric film demand, 2027–2037.
• Table 60. Core material demand, 2027–2037.
• Table 61. Plating, foil and resist demand, 2027–2037.
• Table 62. Interposer material comparison.
• Table 63. Interposer material mix and value, 2027–2037.
• Table 64. Glass core adoption and material redistribution, 2027–2037.
• Table 65. Package assembly materials demand, 2027–2037 (US$ million).
• Table 66. Advanced package materials and consumables market, 2027–2037 (US$ million).
• Table 67. Package material supply by producing region, 2037.
• Table 68. Thermal design power and flux by package generation.
• Table 69. Thermal interface material families for AI packages.
• Table 70. In-package cooling architecture adoption, 2027–2037 (per cent of flagship AI packages).
• Table 71. Advanced thermal materials, readiness and application.
• Table 72. Thermal materials and in-package cooling market for AI semiconductors, 2027–2037 (US$ million).
• Table 73. TIM demand by chemistry family, AI packages, 2027–2037 (US$ million).
• Table 74. Supply chain tier concentration and geographic exposure.
• Table 75. Chinese domestic HBM and packaging stack, 2027–2037.
• Table 76. Qualification cycle by class and implied earliest volume entry.
• Table 77. Announced capacity investment by tier, 2025–2030 commitments (US$ billion, indicative).
• Table 78. Key risk register, probability and impact.
• Table 79. Total AI memory and advanced packaging value pool, 2027–2037 (US$ billion).
• Table 80. Value pool by end application, 2027–2037 (US$ billion).
• Table 81. Value pool by region of manufacture, 2027–2037 (US$ billion).
• Table 82. Total materials and consumables market, 2027–2037 (US$ million).
• Table 83. Top fifteen material classes by 2037 value.
• Table 84. Indexed real price trajectories by class, 2027 = 100.
• Table 85. Scenario detail by layer, 2037 (US$ billion).
• Table 86. Scenario trajectories, total value pool 2027–2037 (US$ billion).
• Table 87. Structural corporate events in the AI hardware and thermal supply base, 2024–2026.
• Table 88. Technology roadmap summary, 2027–2037.
• Table 89. Strategic conclusions mapped to stakeholder and action window.

List of Figures

• Figure 1. The AI memory and packaging value pool, layer map 2027–2037.
• Figure 2. Value pool by layer, stacked area 2027–2037.
• Figure 3. Materials market value by class, 2027–2037.
• Figure 4. Value pool by region, 2027–2037.
• Figure 5. Concentration heat map by material class.
• Figure 6. Material demand growth versus device unit growth, indexed.
• Figure 7. Bull, Base and Bear scenario comparison, 2027–2037.
• Figure 8. AI accelerator package shipments by class, 2026–2037.
• Figure 9. HBM stack demand versus modelled supply, 2026–2037.
• Figure 10. Accelerator package cost structure evolution, 2024–2037.
• Figure 11. Package area and build-up layer roadmap, 2024–2037.
• Figure 12. Non-hyperscaler accelerator demand by pool, 2027–2037.
• Figure 13. HBM stack architecture, cross-section.
• Figure 14. HBM bandwidth and capacity evolution, HBM2 to HBM6.
• Figure 15. HBM generation transition timeline and bond-type migration, 2024–2037.
• Figure 16. Custom HBM share and base-die value migration, 2026–2037.
• Figure 17. Micro-bump versus hybrid bond stack cross-sections and interconnect density.
• Figure 18. DRAM cell architecture roadmap and its intersection with the HBM generation cadence.
• Figure 19. HBM unit shipments and revenue, 2026–2037.
• Figure 20. HBM revenue share by supplier, 2022–2037.
• Figure 21. HBM consumption by customer, 2026–2037.
• Figure 22. HBM price per gigabyte by generation, 2026–2037.
• Figure 23. Material consumption per HBM stack by generation, indexed.
• Figure 24. Underfill versus bonding dielectric demand crossover, 2027–2037.
• Figure 25. HBM stack materials market by class, 2027–2037.
• Figure 26. Advanced packaging platform positioning by reticle multiple and interconnect density.
• Figure 27. CoWoS-class capacity by variant, 2026–2037.
• Figure 28. Advanced packaging capacity by provider and geography, 2026–2037.
• Figure 29. Co-packaged optics adoption in AI packages, 2027–2037.
• Figure 30. The AI accelerator package materials stack, exploded cross-section.
• Figure 31. Build-up dielectric film demand and constrained-grade share, 2027–2037.
• Figure 32. Interposer material mix by area, 2027–2037.
• Figure 33. Advanced package materials market by class, 2027–2037.
• Figure 34. Material production versus consumption by region, 2037.
• Figure 35. In-package cooling architecture adoption, 2027–2037.
• Figure 36. TIM demand by chemistry family, 2027–2037.
• Figure 37. Risk matrix, probability against impact.
• Figure 38. Total value pool by layer, 2027–2037.
• Figure 39. Value pool by region, 2027–2037.
• Figure 40. Material classes by 2037 value and growth rate.
• Figure 41. Scenario trajectories, 2027–2037.