ASIC and VLSI Design Flashcards
“Lambda” (λ) in VLSI design
It doesn’t have a name, they just call it lambda.
It represents the
Moore’s Law
The observation that the number of transistors on a chip doubles approximately every two years (not quite true anymore, beginning to slow)
Origin of the term VLSI
The level of integration of chips has been classified as small-scale, medium-scale, large-scale, and very large scale. Small-scale integration (SSI) circuits, such as the 7404 inverter, have fewer than 10 gates, with roughly half a dozen transistors per gate. Medium-scale integration (MSI) circuits, such as the 74161 counter, have up to 1000 gates. Large-scale integration (LSI) circuits, such as simple 8-bit microprocessors, have up to 10,000 gates. It soon became apparent that new names would have to be created every five years if this naming trend continued and thus the term very large-scale integration (VLSI) is used to describe most integrated circuits from the 1980s onward
Historical Rate at which the number of transistors on a chip increases
Since the beginning of the atomic age (1945), the number of transistors (N) that can fit in a processor has gone up 100-fold every 15 years:
N=100 ^ ((t - 1945) / 15)
*This gives a trendline very close to actual historical data for real Intel processors
Small scale integration
Up to 10 gates per chip
Medium scale integration
Up to 1000 gates per chip
Large scale integration
Up to 10,000 gates per chip
Example: 8-bit processors
Very Large Scale Integration (VLSI)
Over 10,000 gate gates per chip
*Nearly all modern chips are VLSI
*Expected to achieve 100 billion transistors per processor by Summer 2027
three Reasons for the decline of Moore’s Law
1) Physical Limitations: As transistors approach atomic scales, issues like quantum tunneling and heat dissipation become significant challenges. Shrinking transistors further to fit more on a chip becomes increasingly difficult due to these effects
2) Rising Costs: The complexity of manufacturing smaller transistors, using technologies like FinFET and Gate-All-Around (GAA), has made semiconductor production more expensive and challenging. This has slowed down the frequency of doubling transistor counts
3) Alternative Strategies: Instead of continuing with transistor scaling, chip manufacturers like Intel are focusing on multichip architectures and 3D stacking to increase performance. These methods allow for more transistors without needing to shrink individual transistors
Heterogeneous computing
Instead of relying solely on general-purpose CPUs, companies are developing specialized accelerators for specific tasks, such as AI processing, graphics (GPUs), and networking. This has led to a rise in application-specific integrated circuits (ASICs) and system-on-chip (SoC) designs that optimize for particular workloads
Financial Implications of the decline of Moore’s Law
Semiconductor companies have been forced to invest heavily in research and development (R&D) for advanced manufacturing techniques like Extreme Ultraviolet Lithography (EUV) and new materials (e.g., FinFETs, Gate-All-Around FETs). This has driven up costs significantly. As a result, the industry has seen consolidation, with fewer companies able to compete at the leading edge of semiconductor technology.
The investment required to maintain competitive manufacturing capabilities has pushed some companies to focus on fabless models, relying on pure play foundries like TSMC or Samsung for fabrication, while concentrating on chip design
“Slower Node Transitions”
Because of the decline of Moore’s Law, the move from one process node to the next (e.g., 10nm to 7nm) now takes longer than in the past, resulting in longer product cycles. This impacts both IDMs and pure play foundries as they need to optimize their current process nodes and innovate in other areas like power efficiency and packaging to differentiate their products
Current strategy used by manufacturers to optimize power consumption
They are designing low-power cores and adopting multi-core architectures, where different cores handle different tasks to balance performance with efficiency.
Apple’s M-series chips (M1, M2) exemplify this trend, integrating performance and efficiency cores to optimize energy usage, especially in mobile and battery-powered devices
Emerging Chip manufacturing business models
1) Chiplet Ecosystem: Companies are now developing chiplets that can be integrated into larger systems. This has led to collaborations between chipmakers and foundries to develop modular and customizable chip solutions.
2) Collaboration Across the Supply Chain: As costs rise, collaboration between design firms, foundries, and packaging companies has intensified to share the burden of R&D and manufacturing investments. For instance, Intel’s IDM 2.0 strategy involves manufacturing for external clients, similar to pure play foundries
3) Vertical Integration: Companies like Apple are increasingly designing their own processors (e.g., M-series), controlling the entire hardware-software stack to optimize performance without relying on general-purpose chips from external vendors
Multi-chip Modules
MCMs are packages that contain multiple integrated circuits (ICs) or dies assembled onto a single substrate. These dies are typically placed side by side (in a 2D arrangement) and connected using wire bonds or a silicon interposer.
Key Features:
- 2D Integration: Multiple dies are placed on a substrate in close proximity.
- Parallel Processing: MCMs allow for several chips to work together, which increases overall system performance by distributing workloads across multiple dies.
- Cost-Effective: MCMs can reduce costs by using different process nodes for different chips (e.g., using an older process for less critical parts).
- Used for: Applications requiring multiple functionalities (e.g., CPU and memory) on the same package, such as in graphics processors (GPUs) or system-on-chip (SoC) designs.
Example: GPUs often use MCMs to combine processing cores and memory chips within a single package, allowing faster communication and reducing overall system latency.
3D Stacking in VLSI
Overview:
3D stacking involves stacking multiple layers of integrated circuits (ICs) on top of each other, forming a vertical integration. These layers are interconnected through Through-Silicon Vias (TSVs), which allow for shorter interconnects between layers compared to traditional 2D layouts.
Key Features:
- Vertical Integration: Chips are stacked vertically, resulting in a smaller footprint, which reduces latency and power consumption by minimizing the distance signals need to travel.
- Higher Density: By stacking layers of logic, memory, or both, 3D ICs achieve higher transistor densities.
- Better Power Efficiency: Signals travel shorter distances, and stacking reduces power loss, making 3D ICs highly efficient.
Applications:
3D stacking is particularly useful in high-performance computing, where memory bandwidth is crucial, such as in High Bandwidth Memory (HBM) for GPUs and AI processors.
Example: High Bandwidth Memory (HBM) and stacked DRAM solutions are popular examples of 3D stacking technology used in advanced GPUs and AI chips.
Chiplets
Overview:
Chiplets are small functional blocks or “mini-chips” that can be mixed and matched within a package to form a larger, more complex system. Each chiplet performs a specific function (e.g., I/O, memory, or CPU), and they are connected using high-speed interconnects on a substrate or interposer.
Key Features:
- Modularity: Chiplets allow designers to reuse blocks of IP across different products, saving design time and costs.
- Scalability: Different chiplets can be manufactured using different process nodes, optimizing costs and performance for specific functions.
- Customization: Chiplets enable a modular approach to building chips, allowing manufacturers to create highly customized solutions for specific workloads.
Application: High-performance processors (e.g., AMD’s Ryzen and EPYC processors) and heterogeneous systems where different tasks require specialized chiplets for optimal performance.
Example: AMD’s Zen architecture uses chiplets to separate CPU cores from I/O, allowing the cores to be manufactured on an advanced node (e.g., 7nm) while using an older process for I/O, thus optimizing cost and performance.
MCM v. 3D Stacking v. Chiplets
Process Nodes in VLSI
refers to the manufacturing technology used to produce semiconductor chips, specifically denoted by the feature size of the transistors on a chip. It is often measured in nanometers (nm) and historically corresponded to the minimum length of a transistor’s gate (or the half-pitch of a memory cell).
Key Aspects of Process Nodes:
Transistor Size: Process node size (e.g., 7nm, 5nm, 3nm) indicates the transistor’s minimum feature size. As process nodes shrink, transistors get smaller, allowing more of them to fit on a chip, thus improving performance and energy efficiency.
Scaling: The reduction of process node sizes, known as scaling, follows the historical trend described by Moore’s Law, where the number of transistors on a chip doubles approximately every two years. However, modern process nodes have become more challenging to shrink due to physical and quantum limitations.
Performance and Power Efficiency: Smaller nodes allow transistors to switch faster and consume less power. As the node shrinks, the switching speed increases, which boosts computational performance, and leakage power is reduced, improving energy efficiency.
Current Process Node Usage: Modern process nodes (as of 2024) include 5nm, 3nm, and 2nm technologies, mainly led by foundries like TSMC, Samsung, and Intel. For example, TSMC’s 5nm process is widely used in high-performance devices like Apple’s A14/A15 chips.
Examples of Nodes:
7nm: Used in AMD’s Ryzen processors and high-end GPUs.
5nm: Used in Apple’s M1 chips and other flagship mobile processors.
3nm: Expected in upcoming high-performance chips, pushing the boundaries of current semiconductor technology.
Challenges of implementing process nodes at smaller scales
Quantum Effects and Manufacturing complexity
1) Quantum Effects: As transistors approach atomic-scale sizes, quantum tunneling and leakage current become significant problems.
2) Manufacturing Complexity: Smaller process nodes require more complex manufacturing techniques such as Extreme Ultraviolet (EUV) lithography to etch the small patterns onto silicon wafers.
Basic VLSI technology element
MOSFET
Reasons why MOSFETs dominate over BJTs in VLSI design
1) Scalability and Density: MOSFETs are more scalable than BJTs due to their structure, allowing for higher transistor densities on a chip. This scalability is crucial for VLSI, where billions of transistors are integrated into a single chip. MOSFETs remain more adaptable to miniaturization, enabling more compact and powerful integrated circuits.
2) Power Efficiency: MOSFETs consume significantly less power compared to BJTs. This lower power consumption is vital for VLSI applications, where reducing power usage and heat dissipation is a top priority (due to the density)
3) High switching speed: MOSFETs can switch on and off much faster than BJTs, making them ideal for high-speed digital circuits. This fast switching capability is essential in modern processors and memory devices, where billions of operations occur per second. Plus, MOSFETs primarily dissipate power when switching (their static power consumption is minimal).
4) Simplicity in Fabrication: MOSFETs are easier and cheaper to fabricate at smaller geometries because they require fewer processing steps compared to BJTs, which require precise doping profiles and complex junction fabrication.
5) Density requirements of VLSI: MOSFETs are inherently more compatible with modern VLSI processes, where hundreds of billions of transistors are integrated onto a single chip. They are able to be densely packed and are compatible with automated design tools
Measurements/Units for Process Nodes
nanometers
In current process nodes (like 7 nm, 5 nm, and 3 nm), the node name doesn’t necessarily reflect a direct physical measurement like the gate length. Instead, it refers more generally to the technology’s overall capability, transistor density, and performance improvement
Relationship between channel length and process nodes
As process nodes shrink, the channel length decreases, leading to faster transistors but introducing challenges like leakage currents and short-channel effects.
Smaller process nodes offer improved performance but require advanced techniques to manage power consumption and ensure reliability.
