Relevance:
| Parameter | Status / Data |
| Mission | ₹76,000 crore India Semiconductor Mission (ISM) |
| Total Investments Approved | ₹1.6 lakh crore across 6 states |
| Projects Approved | 10 semiconductor projects |
| Chip Design Projects Sanctioned | 24 |
| Design Companies Using Advanced Tools | 87 |
| Academic Institutions Supported (DLI Scheme) | 288 |
| Estimated Job Creation | Thousands of high-skill jobs (MeitY projection) |
| Feature | Description |
| Launched | 2013 by IIT Madras’ RISE Lab |
| Architecture | Based on open-source RISC-V Instruction Set |
| Goal | Develop indigenous microprocessor IPs free from foreign licensing restrictions |
| Variants | Target IoT, mobile, embedded, and high-performance computing |
| Advantage | Startups and academia can build upon open-source designs, enabling distributed innovation |
| Country | Advanced Node Capability | Notable Companies | Relevance |
| USA | 3 nm & below | Intel, NVIDIA | Advanced design & fabrication |
| Taiwan | 3 nm | TSMC | Global fabrication leader |
| South Korea | 3 nm | Samsung | Advanced fab + design |
| China | 7 nm (SMIC) | SMIC, Huawei | Indigenous despite export controls |
| India | 7 nm (design) | IIT Madras (SHAKTI) | Design breakthrough; fab yet to come |
Inference: India joins the second-tier semiconductor innovators (design capability without domestic fabs), but the roadmap points toward full-stack ecosystem development.
| Challenge | Policy/Action Needed |
| No domestic advanced fabrication | Accelerate partnerships with TSMC, Intel, or GlobalFoundries. |
| High CapEx (>$10 bn per fab) | Viable PPP + long-term fiscal incentives. |
| Talent shortage in VLSI design | Expand semiconductor design courses in IITs/NITs under ISM. |
| Supply chain dependence | Build indigenous material & component ecosystem (e.g., silicon wafers, photolithography tools). |
| Sustainability | Focus on water & energy-efficient fabrication models. |
Relevance:
| Category | Modules | Duration | Focus Areas |
| Students (Class 6–12) | 3 modules | 15 hours each | Basics of AI, ML, data handling, ethical AI, and applications |
| Educators | 1 module | 45 hours | Integrating AI in teaching, curriculum design, and ethics |
| Institution / Policy | Contribution to AI Education |
| CBSE | Introduced AI as a subject in Class IX (2019–20) and Class XI (2020–21). |
| AICTE | Mandated AI electives across technical and engineering institutions. |
| IITs | Advanced courses in Deep Learning, Predictive Analytics, and Applied AI. |
| Centre for Excellence in AI (CoE-AI) | Research hub for AI in education, Indian languages, and pedagogy innovation. |
| NEP 2020 | Encouraged integration of contemporary tech into school curricula. |
Fostering AI Awareness
Supporting Atmanirbhar Bharat
Bridging the Digital Divide
Enhancing Employability and Innovation
Alignment with Viksit Bharat @2047
| Challenges | Way Forward |
| Limited AI faculty and infrastructure in schools | Teacher training through SOAR educator module; CoE-AI support |
| Urban-rural digital gap | Leverage SIDH; integrate AI courses in ITIs and rural digital labs |
| Low female participation in tech | Gender-inclusive AI skilling drives; partnerships with Skill India’s “Nari Shakti” initiatives |
| Ethical and privacy concerns | Emphasize ethics modules; collaborate with MeitY for AI governance norms |
| Scalability of program | Public-private partnerships (e.g., with NASSCOM, Google India, Intel India) |
| Indicator | Data |
| Apprentices trained in AI (FY 2022–26) | 1,480 |
| Budget allocation (2025–26) | ₹500 crore |
| Schools introducing AI in CBSE | ~25,000+ |
| Students covered under AI elective (since 2019) | ~12 lakh |
| Skill India-trained workforce (since 2015) | ~1.3 crore |
| Target of SOAR (initial phase) | 10 lakh students, 50,000 educators (2025–27) |
Conclusion