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// i18n — Korean (default) + English translations
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const SCHOLAR_URL = "https://scholar.google.com/citations?hl=ko&user=3t5rUVAAAAAJ";

const I18N = {
  ko: {
    // Document
    docTitle: "박태균 — 반도체·AI 자문 (Semiconductor & AI Advisor)",

    // TopBar
    nav: [
      ["about",       "01 / 소개"],
      ["domains",     "02 / 분야"],
      ["cases",       "03 / 포트폴리오"],
      ["recognition", "04 / 수상·논문"],
      ["contact",     "05 / 문의"]
    ],
    menuOpen:  "MENU",
    menuClose: "CLOSE",
    menuAria:  "메뉴 열기",
    brand:     "박태균",

    // Hero
    heroEyebrow:    "▮ INDEPENDENT ADVISOR · SEMICONDUCTOR / AI MANUFACTURING · SEOUL · KR",
    heroHeading1:   "반도체·AI 제조의",
    heroHeadingAcc: "복잡한 신호",
    heroHeading2:   "를",
    heroHeading3:   "의사결정으로 번역합니다.",
    heroSub:        "Samsung 반도체연구소 양산 현장 5.5년 + Siemens EDA 검증 — 제조와 설계를 모두 본 반도체 자문가.",
    heroBody:       "삼성전자 반도체연구소에서 DRAM·NAND 양산 공정의 OES 빅데이터 기반 공정 진단과 수율 안정화를 수행했고, Siemens EDA Calibre OPCV에서 첨단 노드 리소그래피 검증과 제조성 분석을 담당했습니다. 현재는 글로벌 및 국내 IB·PE·MBB·전략컨설팅·기업 전략팀을 대상으로 HBM, 첨단 패키징, 반도체 제조·수율 향상, Fab 장비, EDA/OPC, 특수가스·소재, AI/ML 기반 Smart Fab 분야의 기술·전략 자문을 제공합니다.",
    ctaPrimary:     "자문 문의하기",
    ctaSecondary:   "자문 포트폴리오 보기",
    heroStats: [
      { k: "Ph.D.", u: "Seoul National Univ.", d: "화학생물공학" },
      { k: "100+",  u: "Advisory Sessions",    d: "최근 약 2년간 전문가 자문" },
      { k: "3",     u: "Samsung Awards",       d: "DRAM·OES·EPD 관련 사내 수상" },
      { k: "2",     u: "IEEE T-SM Papers",     d: "반도체 제조 데이터 분석 논문" }
    ],

    // Credibility Bar (Hero 직하)
    credLabel: "Credentials at a glance",
    cred: [
      { v: "100+",                l: "Advisory Sessions",          s: "최근 약 2년" },
      { v: "Global 70% /\nKorea 30%", l: "글로벌·국내 자문 비중",  s: "" },
      { v: "Samsung SRC 5.5Y",     l: "양산 현장 경험",             s: "2018.09 – 2024.01" },
      { v: "Siemens EDA /\nCalibre OPCV", l: "첨단 노드 리소그래피 검증", s: "2024.09 – 2026.01" },
      { v: "IEEE T-SM ×2",          l: "국제 저널 논문",              s: "2023, 2024" }
    ],

    // Section labels
    sec01: "About · 컨설턴트 소개",
    sec02: "Domains · 자문 분야",
    sec03: "Advisory Portfolio · 자문 포트폴리오",
    sec04: "Recognition · 수상 및 논문",
    sec05: "How We Work · 진행 방식",
    sec06: "INQUIRY · 자문 문의",

    // Profile (About)
    profile: {
      nameKo:   "박 태 균",
      nameEn:   "Taekyoon Park",
      title:    "반도체 · AI/ML · Smart Fab · Advanced Packaging 자문",
      subtitle: "Independent Advisor · Ph.D.",
      bio:      "서울대학교 화학생물공학 박사로, 삼성전자 반도체연구소에서 DRAM·NAND 양산 공정의 OES 빅데이터 기반 공정 진단, 수율 안정화, dry etch process optimization을 수행했습니다. 이후 Siemens EDA Calibre OPCV에서 첨단 노드 리소그래피 검증, design-manufacturing interaction, manufacturability 분석을 담당했습니다.\n현재는 독립 반도체 자문가로서 글로벌 및 국내 IB·PE·MBB·전략컨설팅·기업 전략팀을 대상으로 HBM, 첨단 패키징, 반도체 제조·수율 향상, Fab 장비, EDA/OPC, 특수가스·소재, AI/ML 기반 Smart Fab 관련 기술·전략 자문을 제공합니다.\n복잡한 제조·공정·수율·장비·소재 이슈를 투자, 전략, 운영 의사결정에 바로 사용할 수 있는 형태로 구조화하는 데 강점이 있습니다.",
      certsLabel:   "Credentials",
      timelineLabel:"Career Timeline",
      earlierLabel: "Earlier",
      certs: [
        { label: "Ph.D.",        text: "화학생물공학\n서울대학교" },
        { label: "Advisory",     text: "100+ sessions\n최근 약 2년" },
        { label: "Awards",       text: "Samsung 사내 수상 3건\nDRAM·OES·EPD" },
        { label: "Publications", text: "IEEE T-SM 2편\n2023, 2024" },
        { label: "Languages",    text: "Korean Native\nEnglish Business" },
        { label: "Tooling",      text: "Python\nManufacturing Data Analytics" }
      ],
      career: [
        { period: "2026.02 — 현재",        org: "독립 자문가 (Independent Advisor)",          role: "반도체 · AI/ML · Smart Fab · Advanced Packaging Advisor", tier: "A" },
        { period: "2024.09 — 2026.01",     org: "Siemens EDA — Calibre OPCV",            role: "Sr. Product Engineer",                            tier: "A" },
        { period: "2018.09 — 2024.01",     org: "삼성전자 반도체연구소",                     role: "Staff Engineer · OES Big Data / Dry Etch",        tier: "A" },
        { period: "2024.06 — 2024.08",     org: "Wonriedu",                              role: "CCO · AI STAR Engine 설계",                       tier: "B" },
        { period: "2012.04 — 2017.11",     org: "Bapul Co., Ltd.",                       role: "R&D Engineer · 성공적 엑싯 기여 (대학원 병행)",        tier: "B" }
      ]
    },

    // Domains
    domainsHeading: <>6개 핵심 영역에 걸친<br />반도체·AI 제조 자문.</>,
    domainsBody:    "각 분야는 1:1 전문가 통화, 서면 자문, 단기 프로젝트, 정기 자문, 임원 브리핑 등 다양한 방식으로 진행 가능합니다. 진행 전 범위, 기밀 범위, 일정, 산출물 형식을 사전에 정리합니다.",
    domains: [
      {
        code: "01",
        title: "HBM · AI Memory · 첨단 패키징",
        en: "HBM & Advanced Packaging",
        desc: "HBM3/3E, HBM4 roadmap, TSV, hybrid bonding, 2.5D/3D integration, advanced packaging yield를 제조 관점에서 해석합니다.",
        bullets: ["HBM roadmap / supplier dynamics", "TSV · hybrid bonding · 2.5D/3D", "Packaging yield · KGD/KGS"]
      },
      {
        code: "02",
        title: "수율 · 공정 진단",
        en: "Yield & Process Diagnostics",
        desc: "OES 등 고차원 제조 신호를 기반으로 DRAM·NAND 양산 공정의 수율 및 안정성 이슈에 대한 근본 원인을 분석합니다.",
        bullets: ["고차원 제조 signal 해석", "Process excursion / defect mechanism", "Structured RCA / yield ramp"]
      },
      {
        code: "03",
        title: "장비 · 식각 · 세정 · 계측",
        en: "Fab Equipment & Process",
        desc: "Dry etch, wet cleaning, chamber matching, probe card cleaning, metrology, equipment ROI 등 fab 장비·공정 이슈를 분석합니다.",
        bullets: ["Dry etch · wet cleaning", "Chamber matching · tool stability", "Equipment ROI · maintenance strategy"]
      },
      {
        code: "04",
        title: "리소그래피 · OPC · 제조성 검증",
        en: "EDA & Lithography Verification",
        desc: "Siemens EDA Calibre OPCV 경험을 기반으로 첨단 노드 리소그래피 검증, pattern fidelity, design-manufacturing interaction을 해석합니다.",
        bullets: ["OPCV / verification workflow", "Design-manufacturing interaction", "Manufacturability / process window"]
      },
      {
        code: "05",
        title: "소재 · 특수가스 · 공급망",
        en: "Materials & Supply Chain",
        desc: "Specialty gas, LCO2, PFAS-free PR/PAG, semiconductor materials qualification, supplier dynamics 및 supply chain risk를 분석합니다.",
        bullets: ["Specialty gas · LCO2", "PFAS-free PR/PAG", "Supplier dynamics / qualification"]
      },
      {
        code: "06",
        title: "AI·ML 제조 최적화 · Smart Fab",
        en: "AI-driven Smart Fab",
        desc: "OES big data, anomaly detection, fault diagnosis, APC/VM, smart fab ROI, digital transformation 전략을 제조 현실성 관점에서 평가합니다.",
        bullets: ["Anomaly detection / fault diagnosis", "APC · VM · manufacturing analytics", "Smart fab ROI / deployment feasibility"]
      }
    ],
    domainOtherArea: "기타 / 복합 자문",
    engagementSuffix: "engagement",

    // Advisory Portfolio (구 Cases)
    portfolioHeading: <>100+건의 반도체·AI<br />제조 자문 포트폴리오.</>,
    portfolioBody:    "최근 약 2년간 글로벌 및 국내 IB·PE·MBB·전략컨설팅·기업 전략팀을 대상으로 100건 이상의 expert consultation 및 strategic advisory를 수행했습니다. 프로젝트는 글로벌 고객사 약 70%, 국내 고객사 약 30% 수준으로 구성되며, 모든 사례는 익명화된 segment 기준으로만 표시합니다.",
    portfolioSegmentsLabel: "Advisory Segments",
    portfolioActivitiesLabel: "자문 주요 활동",
    portfolioTopicsLabel: "주요 키워드",
    portfolioSegments: [
      {
        title: "HBM · AI Memory · Advanced Packaging",
        topics: "HBM3/3E, HBM4 roadmap, AI accelerator memory, hybrid bonding, 2.5D/3D integration",
        summary: "HBM3/3E·HBM4 로드맵, TSV·hybrid bonding·2.5D/3D integration의 제조 가능성과 yield 리스크를 양산 현장 경험 기반으로 해석합니다. AI 가속기·메모리 투자 분석 및 supplier 경쟁력 평가에 직접 활용 가능합니다.",
        activities: ["HBM 세대 전환 타이밍 및 기술 로드맵 분석", "TSV · hybrid bonding 제조 현실성 평가", "2.5D/3D integration yield · KGD/KGS 리스크", "AI accelerator 메모리 요구사항 및 적합성 평가"]
      },
      {
        title: "Manufacturing · Yield Ramp",
        topics: "DRAM/NAND yield ramp, process excursion, defect mechanism, structured RCA",
        summary: "DRAM·NAND 양산 공정의 수율 이슈, excursion 원인, ramp 속도를 OES 빅데이터 진단 경험 기반으로 분석합니다. 투자 대상 팹의 기술 수준과 수율 성숙도를 구조화된 RCA 프레임으로 평가합니다.",
        activities: ["Yield ramp 속도 및 불량 메커니즘 분석", "Process excursion · defect 원인 구조화", "OES signal 기반 공정 상태 진단", "Structured RCA · correction 전략 평가"]
      },
      {
        title: "Fab Equipment · Etch · Cleaning",
        topics: "dry etch, wet cleaning, chamber matching, probe card cleaning, tool stability",
        summary: "Dry etch, wet cleaning, chamber matching, probe card cleaning 등 fab 장비·공정 이슈를 기술적으로 분석하고 equipment ROI를 평가합니다. 장비 투자 타당성, 유지보수 전략, 공정 안정성 진단을 포함합니다.",
        activities: ["Dry etch 공정 이슈 진단 및 개선 방향", "Wet cleaning · chamber matching 분석", "Probe card cleaning · tool stability 평가", "Equipment ROI · maintenance 전략 검토"]
      },
      {
        title: "EDA · OPC · Lithography",
        topics: "Calibre OPCV, lithography verification, pattern fidelity, manufacturability",
        summary: "Siemens EDA Calibre OPCV 실무 경험을 바탕으로 첨단 노드 리소그래피 검증, OPC 워크플로우, design-manufacturing interaction을 분석합니다. EDA 기업 기술 포지셔닝 및 경쟁사 비교에 활용됩니다.",
        activities: ["Calibre OPCV 검증 워크플로우 분석", "OPC 기술 현황 및 경쟁사 비교", "Design-manufacturing interaction 해석", "Manufacturability · process window 평가"]
      },
      {
        title: "Materials · Specialty Gas",
        topics: "specialty gas, LCO2, PFAS-free PR/PAG, supplier dynamics, material qualification",
        summary: "Specialty gas, LCO2, PFAS-free PR/PAG 등 반도체 소재·특수가스의 기술 자격화, supplier dynamics, 공급망 리스크를 분석합니다. 규제 변화 대응 및 대체재 전략도 포함합니다.",
        activities: ["Specialty gas 기술 현황 및 시장 분석", "LCO2 · PFAS-free PR/PAG 동향 분석", "Supplier dynamics · qualification 리스크", "공급망 리스크 및 대체재 전략"]
      },
      {
        title: "Smart Fab · AI Manufacturing",
        topics: "OES big data, anomaly detection, APC/VM, smart fab ROI, digital transformation",
        summary: "OES 빅데이터 기반 이상 진단, APC/VM, smart fab ROI, digital transformation 전략을 제조 현실성 관점에서 평가합니다. 실제 fab 적용 가능성과 투자 타당성 분석을 중심으로 제공합니다.",
        activities: ["Anomaly detection · fault diagnosis 기술 평가", "APC · VM · manufacturing analytics 분석", "Smart fab ROI · 투자 타당성 검토", "Digital transformation 전략 수립 지원"]
      },
      {
        title: "AI Data Center · Memory Value Chain",
        topics: "AI infrastructure, HBM/eSSD value chain, memory content, data center semiconductor demand",
        summary: "AI 인프라 수요, HBM·eSSD value chain, memory content, 데이터센터 반도체 수요를 기술·시장 관점에서 분석합니다. AI 가속기 로드맵 해석과 메모리 공급망 구조 분석을 포함합니다.",
        activities: ["AI 가속기 수요 전망 및 로드맵 분석", "HBM · eSSD value chain 구조 분석", "Memory content · BOM 분석", "데이터센터 반도체 수요 구조 파악"]
      }
    ],
    portfolioMetrics: [
      { v: "100+",  l: "자문 프로젝트 수행 건수" },
      { v: "~70%",  l: "글로벌 고객사 비중" },
      { v: "7개",   l: "전문 자문 세그먼트" }
    ],
    portfolioDisclaimer: "모든 자문 사례는 고객사명, 프로젝트명, 금액, 세부 기밀 정보를 제외한 익명화된 segment 기준으로만 표시합니다.",

    // Recognition
    recognitionHeading: <>Samsung 사내 수상 3건과<br />IEEE T-SM 논문 2편.</>,
    recognitionBody:    "Samsung 재직 중 DRAM 원천기술상, SRD e-paper 장려상, Samsung Best Paper Silver Award 등 3건의 사내 수상을 받았습니다. 또한 plasma etching endpoint detection, OES signal analytics, chamber variation, nonlinear manifold learning 관련 연구를 IEEE Transactions on Semiconductor Manufacturing에 게재했습니다.",
    recognitionAwardsLabel:       "Samsung Internal Awards",
    recognitionPublicationsLabel: "Peer-reviewed Publications",
    awards: [
      { year: "2023", title: "DRAM 원천기술상",             org: "Samsung Semiconductor R&D Center", note: "OES 기반 비파괴 패턴 특성 추정" },
      { year: "2022", title: "SRD e-paper 장려상",          org: "Samsung Semiconductor R&D Center", note: "다변량 벡터 최적화 기반 공정 레시피 개선" },
      { year: "2021", title: "Best Paper Silver",          org: "Samsung Semiconductor R&D Center", note: "VNAND 저개구율 EPD 알고리즘" }
    ],
    publications: [
      {
        year: "2024",
        venue: "Plasma Etching Endpoint Detection in the Presence of Chamber Variations through Nonlinear Manifold Learning and Density-Based Clustering",
        note: "IEEE Transactions on Semiconductor Manufacturing"
      },
      {
        year: "2023",
        venue: "Improvement of Plasma Etching Endpoint Detection with Data-Driven Wavelength Selection and Gaussian Mixture Model",
        note: "IEEE Transactions on Semiconductor Manufacturing"
      }
    ],
    scholarUrl:   SCHOLAR_URL,
    scholarLabel: "Google Scholar에서 보기",

    // How We Work (구 Schedule)
    scheduleHeading: <>문의 후 적합한 방식과<br />일정을 조율합니다.</>,
    scheduleBody:    "자문 주제, 예상 범위, 희망 일정, 산출물 형식을 먼저 확인한 뒤 아래 네 가지 방식 중 적합한 형태로 진행합니다. 일정 예약 링크는 사전 범위 확인 후 개별적으로 전달드립니다.",
    scheduleFormatsLabel: "Engagement Formats",
    scheduleFormats: [
      { code: "01", k: "1:1 Expert Call",     d: "단발성 전문가 콜로 핵심 질문에 집중해 답합니다." },
      { code: "02", k: "Written Advisory",    d: "서면 분석·메모 형태로 기술·전략 인사이트를 전달합니다." },
      { code: "03", k: "Short-term Project",  d: "수 주 단위의 집중 프로젝트로 범위를 정해 진행합니다." },
      { code: "04", k: "Executive Briefing",  d: "임원 의사결정용 언어로 기술 이슈를 구조화해 브리핑합니다." }
    ],
    scheduleNote:    "",
    scheduleCtaLabel: "자문 문의하기",

    // Contact
    contactHeading: <>문의 후 적합한 방식과<br />일정을 함께<br />정합니다.</>,
    contactBody:    "다루고자 하는 주제, 의사결정 배경, 희망 일정, 원하는 산출물 형식을 자유롭게 공유해 주세요.\n\n적합한 자문 방식으로는 1:1 전문가 통화, 서면 자문, 단기 프로젝트, 경영진 브리핑 등이 있으며, 초기 범위 확인 후 개별적으로 일정 링크를 안내해 드립니다.",
    contactSteps: [
      { n: "01", k: "문의 접수",   d: "문의 내용을 접수하고 기본 정보를 확인합니다." },
      { n: "02", k: "범위 확인",   d: "주제와 배경을 확인하고 적합한 자문 범위와 방식을 제안합니다." },
      { n: "03", k: "일정 조율",   d: "상호 가능한 일정을 조율하고 세부 일정을 확정합니다." },
      { n: "04", k: "자문 진행",   d: "정해진 방식과 일정에 따라 자문을 진행합니다." }
    ],
    contactRows: [
      ["BASE",  "Seoul, South Korea · KST"],
      ["MODE",  "Expert Call · Written · Project · Briefing"],
      ["REPLY", "영업일 기준 24시간 이내"]
    ],
    formName:    "성함",
    formCompany: "회사",
    formTitle:   "직함 / 부서",
    formEmail:   "이메일",
    formArea:    "관심 분야",
    formInquiry: "문의 유형",
    formTiming:  "희망 일정",
    formNda:     "NDA 필요 여부",
    formMessage: "문의 내용",
    formNamePh:    "홍길동",
    formCompanyPh: "ACME Semiconductor",
    formTitlePh:   "예: 전략기획팀 / Associate",
    formEmailPh:   "name@company.com",
    formTimingPh:  "예: 6월 중 / 3주 내",
    formMessagePh: "현재 상황, 다루고자 하는 주제, 의사결정 배경 등을 자유롭게 적어주세요.",
    formInquiryOpts: ["1:1 전문가 통화", "서면 자문", "단기 프로젝트", "경영진 브리핑", "기타 / 미정"],
    formNdaOpts:     ["선택해 주세요", "NDA 필요", "NDA 불필요"],
    formNote:    "※ 일반적으로 영업일 기준 24시간 이내 회신드립니다.",
    formSubmit:      "자문 문의 보내기",
    formSent:         "메일 앱 열림 ✓",
    formMailFallback: "메일 앱이 열리지 않으면 여기를 클릭하세요",
    err: {
      name:    "이름을 입력해주세요.",
      company: "회사명을 입력해주세요.",
      email:   "유효한 이메일 형식이 아닙니다.",
      message: "최소 10자 이상 입력해주세요."
    },
    mailSubjectPrefix: "[자문 문의]",
    mailLabels: {
      name:    "성함",
      company: "회사",
      title:   "직함 / 부서",
      email:   "회신 이메일",
      area:    "관심 분야",
      inquiry: "문의 유형",
      timing:  "희망 일정",
      nda:     "NDA 필요 여부",
      sep:     "─────────────────────────",
      bodyTitle: "문의 내용"
    },

    // Footer
    footerLeft:  "© 2026 PARK TAEGYUN",
    footerRight: "SEOUL, KOREA",

    // Tweaks
    tweaksTitle:     "Tweaks",
    tweakSecColor:   "컬러 / 톤",
    tweakSecLayout:  "레이아웃",
    tweakAccent:     "강조색",
    tweakSurface:    "배경 톤",
    tweakSurfaceOpts: [{ value: "warm", label: "Warm" }, { value: "cool", label: "Cool" }, { value: "dark", label: "Dark" }],
    tweakHero:       "히어로 형식",
    tweakHeroOpts:   [{ value: "split", label: "분할" }, { value: "stacked", label: "수직" }],
    tweakDensity:    "섹션 밀도",
    tweakDensityOpts:[{ value: "comfortable", label: "여유" }, { value: "compact", label: "촘촘" }]
  },

  en: {
    // Document
    docTitle: "Taekyoon Park — Semiconductor & AI Advisor",

    // TopBar
    nav: [
      ["about",       "01 / About"],
      ["domains",     "02 / Domains"],
      ["cases",       "03 / Portfolio"],
      ["recognition", "04 / Recognition"],
      ["contact",     "05 / Contact"]
    ],
    menuOpen:  "MENU",
    menuClose: "CLOSE",
    menuAria:  "Open menu",
    brand:     "Taekyoon Park",

    // Hero
    heroEyebrow:    "▮ INDEPENDENT ADVISOR · SEMICONDUCTOR / AI MANUFACTURING · SEOUL · KR",
    heroHeading1:   "Translating the complex signals of",
    heroHeadingAcc: "semiconductor & AI manufacturing",
    heroHeading2:   "",
    heroHeading3:   "into decisions.",
    heroSub:        "5.5 years of Samsung volume manufacturing + Siemens EDA verification — an advisor who has seen both manufacturing and design.",
    heroBody:       "At Samsung Semiconductor R&D Center I worked on OES big-data process diagnostics and yield stabilization for DRAM/NAND volume production, and at Siemens EDA Calibre OPCV on advanced-node lithography verification and manufacturability analysis. Today I advise global and Korean IB, PE, MBB, strategy consulting, and corporate strategy teams across HBM, advanced packaging, semiconductor manufacturing, yield ramp, fab equipment, EDA/OPC, specialty gas/materials, and AI/ML-driven smart fab.",
    ctaPrimary:     "Request an engagement",
    ctaSecondary:   "View advisory portfolio",
    heroStats: [
      { k: "Ph.D.", u: "Seoul National Univ.", d: "Chemical & Biological Eng." },
      { k: "100+",  u: "Advisory Sessions",    d: "Expert consultations, ~2 yrs" },
      { k: "3",     u: "Samsung Awards",       d: "DRAM · OES · EPD related" },
      { k: "2",     u: "IEEE T-SM Papers",     d: "Mfg. data analytics research" }
    ],

    // Credibility Bar
    credLabel: "Credentials at a glance",
    cred: [
      { v: "100+",                  l: "Advisory Sessions",              s: "~2 years" },
      { v: "Global 70% /\nKorea 30%", l: "Global·Korea advisory mix",       s: "" },
      { v: "Samsung SRC 5.5Y",       l: "Volume manufacturing",           s: "2018.09 – 2024.01" },
      { v: "Siemens EDA /\nCalibre OPCV", l: "Advanced-node lithography",  s: "2024.09 – 2026.01" },
      { v: "IEEE T-SM ×2",           l: "Journal publications",           s: "2023, 2024" }
    ],

    // Section labels
    sec01: "About · Consultant",
    sec02: "Domains · Advisory Areas",
    sec03: "Advisory Portfolio",
    sec04: "Recognition · Awards & Publications",
    sec05: "How We Work",
    sec06: "Inquiry · Advisory Contact",

    // Profile (About)
    profile: {
      nameKo:   "Taekyoon Park",
      nameEn:   "박 태 균",
      title:    "Semiconductor · AI/ML · Smart Fab · Advanced Packaging Advisor",
      subtitle: "Independent Advisor · Ph.D.",
      bio:      "Holding a Ph.D. in Chemical & Biological Engineering from Seoul National University, I worked at Samsung Semiconductor R&D Center on OES big-data process diagnostics, yield stabilization, and dry etch process optimization for DRAM/NAND volume production. I then moved to Siemens EDA Calibre OPCV, working on advanced-node lithography verification, design-manufacturing interaction, and manufacturability analysis.\nToday, as an independent semiconductor advisor, I serve global and Korean IB, PE, MBB, strategy consulting, and corporate strategy teams across HBM, advanced packaging, semiconductor manufacturing, yield ramp, fab equipment, EDA/OPC, specialty gas/materials, and AI/ML-driven smart fab.\nMy strength is structuring complex manufacturing, process, yield, equipment, and materials issues into a form that can be used directly in investment, strategy, and operational decisions.",
      certsLabel:   "Credentials",
      timelineLabel:"Career Timeline",
      earlierLabel: "Earlier",
      certs: [
        { label: "Ph.D.",        text: "Chem. & Bio. Eng.\nSeoul National Univ." },
        { label: "Advisory",     text: "100+ sessions\nLast ~2 years" },
        { label: "Awards",       text: "3 Samsung Awards\nDRAM · OES · EPD" },
        { label: "Publications", text: "2 IEEE T-SM Papers\n2023, 2024" },
        { label: "Languages",    text: "Korean Native\nEnglish Business" },
        { label: "Tooling",      text: "Python\nManufacturing Data Analytics" }
      ],
      career: [
        { period: "Feb 2026 — Present",   org: "Independent Advisor",         role: "Semiconductor · AI/ML · Smart Fab · Advanced Packaging Advisor", tier: "A" },
        { period: "Sep 2024 — Jan 2026",  org: "Siemens EDA — Calibre OPCV",               role: "Sr. Product Engineer",                       tier: "A" },
        { period: "Sep 2018 — Jan 2024",  org: "Samsung Semiconductor R&D Center",         role: "Staff Engineer · OES Big Data / Dry Etch",   tier: "A" },
        { period: "Jun 2024 — Aug 2024",  org: "Wonriedu",                                 role: "CCO · Designed AI STAR Engine",              tier: "B" },
        { period: "Apr 2012 — Nov 2017",  org: "Bapul Co., Ltd.",                          role: "R&D Engineer · contributed to successful exit (during graduate studies)", tier: "B" }
      ]
    },

    // Domains
    domainsHeading: <>Semiconductor & AI manufacturing advisory<br />across six key areas.</>,
    domainsBody:    "Each domain can be engaged in various ways, including 1:1 expert calls, written advisory, short-term projects, advisory retainers, and executive briefings. Scope, confidentiality boundaries, schedule, and deliverable formats are defined prior to starting.",
    domains: [
      {
        code: "01",
        title: "HBM · AI Memory · Advanced Packaging",
        en: "HBM & Advanced Packaging",
        desc: "Interpreting HBM3/3E, HBM4 roadmap, TSV, hybrid bonding, 2.5D/3D integration, and advanced packaging yield from a manufacturing perspective.",
        bullets: ["HBM roadmap / supplier dynamics", "TSV · hybrid bonding · 2.5D/3D", "Packaging yield · KGD/KGS"]
      },
      {
        code: "02",
        title: "Yield & Process Diagnostics",
        en: "Yield & Process Diagnostics",
        desc: "Root-cause analysis of yield and stability issues in DRAM/NAND volume production based on high-dimensional manufacturing signals such as OES.",
        bullets: ["High-dim. signal analysis", "Process excursion / defect mechanism", "Structured RCA / yield ramp"]
      },
      {
        code: "03",
        title: "Fab Equipment & Process",
        en: "Fab Equipment & Process",
        desc: "Analyzing fab equipment and process issues including dry etch, wet cleaning, chamber matching, probe card cleaning, metrology, and equipment ROI.",
        bullets: ["Dry etch · wet cleaning", "Chamber matching · tool stability", "Equipment ROI · maintenance strategy"]
      },
      {
        code: "04",
        title: "EDA & Lithography Verification",
        en: "EDA & Lithography Verification",
        desc: "Drawing on Siemens EDA Calibre OPCV experience to interpret advanced-node lithography verification, pattern fidelity, and design-manufacturing interaction.",
        bullets: ["OPCV / verification workflow", "Design-manufacturing interaction", "Manufacturability / process window"]
      },
      {
        code: "05",
        title: "Materials & Supply Chain",
        en: "Materials & Supply Chain",
        desc: "Analyzing specialty gas, LCO2, PFAS-free PR/PAG, semiconductor materials qualification, supplier dynamics, and supply chain risk.",
        bullets: ["Specialty gas · LCO2", "PFAS-free PR/PAG", "Supplier dynamics / qualification"]
      },
      {
        code: "06",
        title: "AI-driven Smart Fab",
        en: "AI-driven Smart Fab",
        desc: "Evaluating OES big data, anomaly detection, fault diagnosis, APC/VM, smart fab ROI, and digital transformation strategy from a manufacturing-reality standpoint.",
        bullets: ["Anomaly detection / fault diagnosis", "APC · VM · manufacturing analytics", "Smart fab ROI / deployment feasibility"]
      }
    ],
    domainOtherArea: "Other / mixed advisory",
    engagementSuffix: "engagement",

    // Advisory Portfolio
    portfolioHeading: <>100+ semiconductor & AI<br />manufacturing advisory projects.</>,
    portfolioBody:    "Over the past ~2 years, I have delivered 100+ expert consultations and strategic advisory projects for global and Korean IB, PE, MBB, strategy consulting, and corporate strategy teams. The portfolio is roughly 70% global and 30% Korea, and every case is shown only as an anonymized segment.",
    portfolioSegmentsLabel: "Advisory Segments",
    portfolioActivitiesLabel: "Advisory Activities",
    portfolioTopicsLabel: "Key Topics",
    portfolioSegments: [
      {
        title: "HBM · AI Memory · Advanced Packaging",
        topics: "HBM3/3E, HBM4 roadmap, AI accelerator memory, hybrid bonding, 2.5D/3D integration",
        summary: "Interpreting the manufacturing feasibility and yield risk of HBM3/3E, HBM4 roadmaps, TSV, hybrid bonding, and 2.5D/3D integration from volume-production experience. Directly applicable to AI accelerator/memory investment analysis and supplier competitive assessment.",
        activities: ["HBM generation transition timing & technology roadmap", "TSV · hybrid bonding manufacturability assessment", "2.5D/3D integration yield · KGD/KGS risk", "AI accelerator memory requirements & suitability assessment"]
      },
      {
        title: "Manufacturing · Yield Ramp",
        topics: "DRAM/NAND yield ramp, process excursion, defect mechanism, structured RCA",
        summary: "Analyzing yield issues, excursion root causes, and ramp speed in DRAM/NAND volume production based on hands-on OES big-data diagnostics. Evaluating the technology maturity and yield readiness of target fabs through a structured RCA framework.",
        activities: ["Yield ramp speed & defect mechanism analysis", "Process excursion · defect root cause structuring", "OES signal-based process state diagnostics", "Structured RCA · correction strategy assessment"]
      },
      {
        title: "Fab Equipment · Etch · Cleaning",
        topics: "dry etch, wet cleaning, chamber matching, probe card cleaning, tool stability",
        summary: "Technical analysis of fab equipment and process issues — dry etch, wet cleaning, chamber matching, probe card cleaning — alongside equipment ROI and investment justification. Includes maintenance strategy and process stability diagnostics.",
        activities: ["Dry etch process issue diagnostics & improvement", "Wet cleaning · chamber matching analysis", "Probe card cleaning · tool stability assessment", "Equipment ROI · maintenance strategy review"]
      },
      {
        title: "EDA · OPC · Lithography",
        topics: "Calibre OPCV, lithography verification, pattern fidelity, manufacturability",
        summary: "Drawing on Siemens EDA Calibre OPCV hands-on experience to analyze advanced-node lithography verification, OPC workflows, and design-manufacturing interactions at practitioner level. Applicable to EDA company technology positioning and competitive comparison.",
        activities: ["Calibre OPCV verification workflow analysis", "OPC technology landscape & competitive comparison", "Design-manufacturing interaction interpretation", "Manufacturability · process window assessment"]
      },
      {
        title: "Materials · Specialty Gas",
        topics: "specialty gas, LCO2, PFAS-free PR/PAG, supplier dynamics, material qualification",
        summary: "Analyzing technical qualification, supplier dynamics, and supply chain risk for specialty gas, LCO2, PFAS-free PR/PAG, and other semiconductor materials. Includes regulatory shift response and alternative material strategies.",
        activities: ["Specialty gas technology & market landscape", "LCO2 · PFAS-free PR/PAG trend analysis", "Supplier dynamics · qualification risk", "Supply chain risk & alternative material strategy"]
      },
      {
        title: "Smart Fab · AI Manufacturing",
        topics: "OES big data, anomaly detection, APC/VM, smart fab ROI, digital transformation",
        summary: "Evaluating OES big-data anomaly detection, APC/VM, smart fab ROI, and digital transformation strategy from a manufacturing-reality standpoint. Focused on actual fab deployment feasibility and investment justification.",
        activities: ["Anomaly detection · fault diagnosis technology assessment", "APC · VM · manufacturing analytics analysis", "Smart fab ROI · investment feasibility review", "Digital transformation strategy development support"]
      },
      {
        title: "AI Data Center · Memory Value Chain",
        topics: "AI infrastructure, HBM/eSSD value chain, memory content, data center semiconductor demand",
        summary: "Analyzing AI infrastructure demand, HBM/eSSD value chain, memory content, and data center semiconductor demand from a technology and market perspective. Includes AI accelerator roadmap interpretation and memory supply chain structure.",
        activities: ["AI accelerator demand outlook & roadmap analysis", "HBM · eSSD value chain structure analysis", "Memory content · BOM analysis", "Data center semiconductor demand structure"]
      }
    ],
    portfolioMetrics: [
      { v: "100+",  l: "Advisory projects delivered" },
      { v: "~70%",  l: "Global client share" },
      { v: "7",     l: "Advisory segments" }
    ],
    portfolioDisclaimer: "Every advisory case is shown only as an anonymized segment, excluding client names, project names, amounts, and confidential details.",

    // Recognition
    recognitionHeading: <>Three Samsung awards<br />and two IEEE T-SM papers.</>,
    recognitionBody:    "During my tenure at Samsung I received three internal awards including the DRAM Foundational Technology Award, the SRD e-paper Encouraging Award, and the Samsung Best Paper Silver Award. My research on plasma etching endpoint detection, OES signal analytics, chamber variation, and nonlinear manifold learning is published in IEEE Transactions on Semiconductor Manufacturing.",
    recognitionAwardsLabel:       "Samsung Internal Awards",
    recognitionPublicationsLabel: "Peer-reviewed Publications",
    awards: [
      { year: "2023", title: "DRAM Original Technology Award", org: "Samsung Semiconductor R&D Center", note: "OES-based non-destructive pattern characteristic estimation algorithm" },
      { year: "2022", title: "SRD e-paper Encouraging Award", org: "Samsung Semiconductor R&D Center", note: "Process recipe optimization based on non-dimensionalization and multivariate vector optimization" },
      { year: "2021", title: "SAMSUNG Best Paper Silver Award", org: "Samsung Semiconductor R&D Center", note: "Securing EPD algorithm for VNAND at low open ratio based on PCA" }
    ],
    publications: [
      {
        year: "2024",
        venue: "Plasma Etching Endpoint Detection in the Presence of Chamber Variations through Nonlinear Manifold Learning and Density-Based Clustering",
        note: "IEEE Transactions on Semiconductor Manufacturing"
      },
      {
        year: "2023",
        venue: "Improvement of Plasma Etching Endpoint Detection with Data-Driven Wavelength Selection and Gaussian Mixture Model",
        note: "IEEE Transactions on Semiconductor Manufacturing"
      }
    ],
    scholarUrl:   SCHOLAR_URL,
    scholarLabel: "View on Google Scholar",

    // How We Work
    scheduleHeading: <>Inquire first, then align<br />on format and timing.</>,
    scheduleBody:    "After confirming the advisory topic, expected scope, preferred timing, and deliverable format, the engagement proceeds in whichever of the four formats below fits best. The booking link is shared individually after scope is confirmed.",
    scheduleFormatsLabel: "Engagement Formats",
    scheduleFormats: [
      { code: "01", k: "1:1 Expert Call",     d: "A focused expert call answering your key questions directly." },
      { code: "02", k: "Written Advisory",    d: "Technical and strategic insight delivered as written analysis or memos." },
      { code: "03", k: "Short-term Project",  d: "A scoped, multi-week intensive project." },
      { code: "04", k: "Executive Briefing",  d: "Technical issues structured in the language of executive decisions." }
    ],
    scheduleNote:    "",
    scheduleCtaLabel: "Request an engagement",

    // Contact
    contactHeading: <>Inquire first, then align<br />on format and<br />timing together.</>,
    contactBody:    "Share the advisory topic, decision-making context, preferred timing, and desired deliverable format freely.\n\nSuitable formats include 1:1 expert calls, written advisory, short-term projects, and executive briefings — a scheduling link will be shared individually after initial scope confirmation.",
    contactSteps: [
      { n: "01", k: "Inquiry Received",   d: "We receive your inquiry and confirm the basic information." },
      { n: "02", k: "Scope Confirmation", d: "We review the topic and context, then propose the appropriate scope and format." },
      { n: "03", k: "Scheduling",         d: "We align on a mutually available schedule and confirm the details." },
      { n: "04", k: "Advisory Begins",    d: "The advisory proceeds according to the agreed format and schedule." }
    ],
    contactRows: [
      ["BASE",  "Seoul, South Korea · KST"],
      ["MODE",  "Expert Call · Written · Project · Briefing"],
      ["REPLY", "Within 24 business hours"]
    ],
    formName:    "Name",
    formCompany: "Company",
    formTitle:   "Title / Dept.",
    formEmail:   "Email",
    formArea:    "Area of interest",
    formInquiry: "Inquiry type",
    formTiming:  "Preferred timing",
    formNda:     "NDA required",
    formMessage: "Message",
    formNamePh:    "Jane Doe",
    formCompanyPh: "ACME Semiconductor",
    formTitlePh:   "e.g. Strategy team / Associate",
    formEmailPh:   "name@company.com",
    formTimingPh:  "e.g. mid-June / within 3 weeks",
    formMessagePh: "Current situation, advisory topic, decision-making context — feel free to describe freely.",
    formInquiryOpts: ["1:1 Expert Call", "Written Advisory", "Short-term Project", "Executive Briefing", "Other / Undecided"],
    formNdaOpts:     ["Please select", "NDA required", "No NDA needed"],
    formNote:    "※ We generally reply within 24 business hours.",
    formSubmit:      "Send advisory inquiry",
    formSent:         "Mail app opened ✓",
    formMailFallback: "If your mail app didn't open, click here",
    err: {
      name:    "Please enter your name.",
      company: "Please enter your company.",
      email:   "Please enter a valid email.",
      message: "Please write at least 10 characters."
    },
    mailSubjectPrefix: "[Advisory Inquiry]",
    mailLabels: {
      name:    "Name",
      company: "Company",
      title:   "Title / Dept.",
      email:   "Reply-to",
      area:    "Area of interest",
      inquiry: "Inquiry type",
      timing:  "Preferred timing",
      nda:     "NDA required",
      sep:     "─────────────────────────",
      bodyTitle: "Message"
    },

    // Footer
    footerLeft:  "© 2026 PARK TAEGYUN",
    footerRight: "SEOUL, KOREA",

    // Tweaks
    tweaksTitle:     "Tweaks",
    tweakSecColor:   "Color / Tone",
    tweakSecLayout:  "Layout",
    tweakAccent:     "Accent",
    tweakSurface:    "Surface",
    tweakSurfaceOpts: [{ value: "warm", label: "Warm" }, { value: "cool", label: "Cool" }, { value: "dark", label: "Dark" }],
    tweakHero:       "Hero layout",
    tweakHeroOpts:   [{ value: "split", label: "Split" }, { value: "stacked", label: "Stacked" }],
    tweakDensity:    "Density",
    tweakDensityOpts:[{ value: "comfortable", label: "Roomy" }, { value: "compact", label: "Compact" }]
  }
};

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