Hi

Dear honorable ministers, Advances in sciences translate into technologies and hence the importance of research programs. Expansion of academic programs is crucial for the nation as it builds a vast talent pool in all subjects of study. Here is an example of important areas of research that the nation cannot delay any longer as national security depends upon it

Innovation / Technical Perspective Three innovations that will emerge at this intersection: 1. Neuromorphic Chips: Chips that work like the human brain, not like traditional computers - 1000x more energy efficient for AI. 2. AI-designed Chips: Google already used AI to design its TPU in weeks instead of months. Soon AI will design semiconductors we humans can't even imagine. 3. Sustainable AI Hardware: Today's AI training consumes massive energy. The next wave will be semiconductors made with 2D materials (like Graphene) that make AI sustainable. Innovation must be sustainable, as India's BRICS theme says.

AI and semiconductors are no longer two separate industries. They are becoming one technology stack, AI determines what compute is needed, while semiconductors determine what AI is economically possible. India shouldn't ask, “How do we become another TSMC?” or “How do we build an Indian NVIDIA?” Instead, the question should be: “Where in the semiconductor + AI value chain can India become indispensable?” NITI Aayog's current roadmap makes a similar argument: rather than trying to win every part of the semiconductor race, India should focus on areas where it can leapfrog, particularly design, advanced packaging, compound semiconductors, materials and system architecture. One mistake I see in discussions about semiconductors is focusing too much on: “We need to manufacture chips.” The higher-value question is: “What complete products can we build around those chips?” For example: AI chip + software + board + sensors + model + cloud platform It could become a complete product.

From my perspective, India’s semiconductor journey should go beyond manufacturing and move towards designing the advanced computing systems that will power the next generation of AI. India should focus on indigenous AI-powered GPUs, NPUs, AI optimized CPUs, TPUs and specialized accelerators, along with silicon photonics, high-speed interconnects and advanced networking chips. Dedicated silicon for data movement, memory, storage and security can reduce bottlenecks and improve overall efficiency. Future architectures could combine CPU, GPU, NPU and QPU technologies, enabling heterogeneous computing for AI, scientific research and quantum applications. India should also explore ultra low precision computing at the hardware level to achieve more AI computation with the same energy. The goal should be not only to manufacture chips, but to design complete AI computing platforms from chip architecture and advanced packaging to interconnects, networking and AI software. click pdf for more info

AI governance: Use AI to detect tax evasion, welfare leakage, fraud and duplicate beneficiaries. AI for Indian languages: Build high-quality models for Hindi, Telugu, Tamil, Bengali, Marathi and other Indian languages.

AI for agriculture: Predict crop diseases, weather risks, irrigation needs and market prices. AI healthcare: AI-assisted diagnosis, telemedicine and screening in rural areas. AI education: Personalised learning in Indian languages and AI tutors for government schools.

Dear honorable ministers, The following tracks in advanced technology development may be worth considering.

AI-Assisted Semiconductor Design: From Specifications to Silicon-Ready Exploration Artificial Intelligence can transform semiconductor design by assisting engineers not only in software and verification, but also in complex analog and mixed-signal circuit development. One promising area is PLL (Phase-Locked Loop) design, which is fundamental to clock generation and synchronization in modern chips. PLLForge is a prototype EDA-style platform that automates key stages of PLL design, including specification-driven parameter calculation, loop-filter design, phase-margin analysis, behavioral lock simulation, validation and design export. The next step is to integrate AI into this workflow. An AI-assisted PLL design system could interpret design requirements, recommend architectures and parameters, analyse simulation results, identify potential issues and iteratively optimise the design against targets such as lock time, stability, power and jitter.

More sustainable AI: Energy consumption is becoming a major constraint. Semiconductor innovation combined with AI optimization could enable substantially more computation per watt, making large-scale AI more economically and environmentally sustainable.