What are the latest trends and innovations in VLSI design for DRAM and SRAM?

It comes in terms of speed, cost, and proximity to the processor. The farther memory types from the processor, like HDDs and SSDs, are slower but offer large storage, mostly in terabytes. The closer it gets to the processor, the faster it is—RAM, typically some gigabytes. RAM is volatile, meaning it loses data without power; it is used for active tasks. Basically, there are two major types: DRAM, Dynamic RAM, and SRAM, Static RAM. The former is represented by storing its data in capacitors, which do require frequent refreshing. SRAM is represented by using transistors; it never requires refreshing. SRAM is faster and is used for cache memory, while DRAM, colloquially known as main memory, is used for general storage during processing.

3D DRAM Architectures: Novel 3D DRAM architectures are being developed, incorporating integrated gate-around (GA) and channel-around (CA) IGZO FETs. These designs enable higher density and better scalability, crucial for future DRAM applications (Find Sessions - 2024 VLSI Symposium). Advanced Lithography and Materials: The use of Extreme Ultraviolet Lithography (EUVL) and High-K Metal Gate (HKMG) technology is becoming more widespread in DRAM production. Processing-in-Memory (PIM): This technology integrates processing capabilities within the memory itself, reducing data transfer latency and enhancing overall system performance.

Using FinFETs (Fin Field-Effect Transistors) instead of traditional planar transistors to improve performance and reduce power consumption.

DRAM - High-density storage with a simple design (one transistor, one capacitor). - Loses data when powered off, requiring periodic refresh cycles. - Main memory in computing systems. - DDR5, GDDR6, and LPDDR5 offer improved bandwidth and energy efficiency, used in desktops, servers, and mobile devices. Data Rate: Up to 6400 MT/s. Size: Common module size up to 64 GB. SRAM - Faster access than DRAM due to no refresh cycles and a more complex design (six transistors per bit). - Consumes less power at idle, ideal for cache applications needing low power and quick access. - Cache Memory: Used for on-chip caches (L1, L2, L3) in processors. Intel’s Alder Lake processors use SRAM for L3 cache, with up to 30 MB in models like the Core i9-12900K.

RAM is crucial in VLSI for fast, efficient memory storage, essential in designs with millions of transistors. DRAM, known for its high density, stores large data in a small space. SRAM, though less dense, offers faster access, ideal for high-performance needs like cache memory. Both types are key for managing power in VLSI: DRAM is more energy-efficient, and suited for edge devices and AIoT, while SRAM, though power-consuming, provides speed where needed. Their roles are increasingly vital in real-time data processing for deep learning and AIoT, enabling efficient on-device computation.

1. 3D-stacked DRAM: Increasing the number of layers in a DRAM chip to boost capacity and performance while reducing footprint. 2. High-bandwidth memory (HBM): Developing DRAM architectures with wider data paths and faster interfaces to meet the demands of high-performance computing applications. 3. In-memory computing: Exploring new DRAM architectures and circuit designs to perform computations directly within the memory array, accelerating data-intensive workloads.

1. 3D-stacked SRAM: Integrating multiple SRAM layers vertically to increase density and reduce power consumption. 2. Spin-transfer torque (STT)-MRAM hybrid SRAM: Combining STT-MRAM with SRAM for improved data retention and write endurance. 3. Nano-wire based SRAM: Exploring new materials and structures for smaller, faster, and lower-power SRAM cells. 4. Advanced power management techniques: Developing innovative power gating and voltage scaling strategies to optimize SRAM energy efficiency. 5. Machine learning-aided SRAM design: Utilizing AI algorithms to automate design processes, optimize layouts, and predict performance.

Dynamic Voltage Scaling (DVS) is being increasingly adopted in DRAM to optimise power consumption dynamically based on workload demands. In a VLSI project focused on cloud storage systems, I implemented DVS to adjust the voltage levels of DRAM modules depending on the intensity of memory operations. During periods of low memory activity, the system automatically reduced voltage, conserving energy without impacting performance. DVS in DRAM is particularly useful in data centres, where power efficiency directly affects operational costs and environmental impact.