3D集成电路：设计、EDA和架构概述

嵌入式系统

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"新兴的三维(3D)集成技术在电子设计自动化(EDA)和架构方面的应用" 三维(3D)集成技术是当前半导体行业解决互连规模限制的关键创新之一。这种技术通过将多个芯片垂直堆叠，实现了性能的显著提升，同时也提供了更高的带宽和异构集成的可能性。随着CMOS技术的进步，3D集成技术成为了持续优化性能的有效途径。文章首先概述了3D集成技术的基本概念。在传统的二维集成电路中，随着芯片尺寸的减小，互连延迟问题愈发突出，而3D集成则通过垂直堆叠芯片，减少了互连距离，从而降低了延迟，提高了系统性能。此外，3D集成还允许不同类型的芯片（如逻辑、存储和传感器）在同一封装内紧密集成，实现了功能的多样化和系统的高效能。接着，文章深入探讨了与3D集成电路相关的电子设计自动化(EDA)挑战。EDA工具在3D集成技术的实现中扮演着至关重要的角色，因为它们支持设计、验证和测试等关键流程。这些挑战包括但不限于：复杂的热管理、新的布局和布线策略、信号完整性和电源完整性分析，以及跨层互联的设计优化。文章可能详细介绍了现有的EDA解决方案，如热模拟工具、三维布局算法和布线技术，这些都是确保3D ICs设计可行性和可靠性的关键。在架构层面，文章详述了3D集成技术的应用。这包括对多种3D IC设计方法的调查，如通过硅通孔(TSV)实现的芯片间连接、芯片堆叠的不同架构（如系统级封装(SiP)和芯片多芯片模块(MCM))，以及如何利用这些技术来实现快速延迟、高带宽通信和异构集成的优势。例如，3D堆叠的DRAM和逻辑芯片可以显著提高内存访问速度，而不同功能的异构集成则可以优化功耗和计算效率。最后，文章可能还讨论了未来3D集成电路发展的趋势和潜在的研究方向，比如更先进的封装技术、新的材料探索以及对新计算模型的支持，这些都是为了进一步推动3D集成技术的发展和广泛应用。这篇文献提供了一个全面的视角，涵盖了3D集成技术的基础、设计、EDA工具的挑战以及在实际应用中的架构策略，对于理解并利用这项技术来推动嵌入式系统和其他领域的进步具有重要价值。

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3.3. Heterogenous Integration 13

Optical Die

Package

Fiber: to off-chip

Processor Core Layer

Non-volatile Memory Layer

DRAM Layer

Fiber: to off-chip

Fig. 3.1: An illustration of 3D heterogeneous architecture with non-

volatile memory stacking and optical die stacking.

fabrication challenge and exploit the beneﬁts of PRAM and MRAM

technologies. For example, Sun et al. [21] demonstrated that the op-

timized MRAM L2 cache on top of multi-core processor can improve

performance by 4.91% and reduce power by 73.5% compared to the

conventional SRAM L2 cache with the similar area.

Optical Device Layer Stacking. Even though 3D memory stack-

ing can help mitigate the memory bandwidth problem, when it comes

to oﬀ-chip communication, the pin limitations, the energy cost of elec-

trical signaling, and the non-scalability of chip-length global wires are

still signiﬁcant bandwidth impediments. Recent developments in silicon

nanophotonic technology have the potential to meet the oﬀ-chip com-

munication bandwidth requirements at acceptable power levels. With

the heterogeneous integration capability that 3D technology oﬀers, one

can integrate optical die together with CMOS processor dies. For ex-

ample, HP Labs proposed a Corona architecture [22], which is a 3D

many-core architecture that uses nanophotonic communication for both

inter-core communication and oﬀ-stack communication to memory or

I/O devices. A photonic crossbar fully interconnects its 256 low-power

multithreaded cores at 20 terabyte per second bandwidth, with much

lower power consumption.

Figure 3.1 illustrates such a 3D heterogenous processor architec-

ture, which integrates non-volatile memories and optical die together

16 3D IC EDA Design Tools and Methodologies

one power hungry block over another in the multi-layered 3D stack.

Since the increasing power density and the resulting thermal impact

are already major concerns in 2D ICs, the move to 3D ICs could ac-

centuate the thermal problem due to increased power density, resulting

in higher on-chip temperatures. High temperature has adverse impacts

on circuit performance. The interconnect delay becomes slower while

the driving strength of a transistor decreases with increasing temper-

ature. Leakage power has an exponential dependence on the temper-

ature and increasing on-chip temperature can even result in thermal

runaways. In addition, at suﬃciently high temperatures, many failure

mechanisms, including electromigration (EM), stress migration (SM),

time-dependent dielectric (gate oxide) breakdown (TDDB), and ther-

mal cycling (TC), are signiﬁcantly accelerated, which leads to an over-

all decrease in reliability. Consequently, it is very critical to model the

thermal behaviors in 3D ICs and investigate possible solutions to mit-

igate thermal problems in order to fully take advantage of the beneﬁts

that 3D technologies oﬀer. This section surveys several thermal mod-

eling approaches for 3D ICs. A detailed 3D modeling method is ﬁrst

introduced, and then compact thermal models are introduced.

4.1.1 3D Thermal Analysis based on Finite Diﬀerence

Method or Finite Element Method

Sapatnekar et al proposed a detailed 3D thermal model [24]. The heat

equation ( 4.1), which is a parabolic partial diﬀerential equation (PDE)

deﬁnes on-chip thermal behavior at the macroscale.

ρc

∂T (r, t)

∂t

= k

∇

T (r, t) + g(r, t) (4.1)

where p represents the density of the material (in kg/m3), c

is the

heat capacity of the chip material (in J/(kg K)), T is the temperature

(in K), r is the spatial coordinate of the point where the temperature

is determined, t is time (in sec), k

is the thermal conductivity of the

material (in W/(m K)), and g is the power density per unit volume

(in W/m3). The solution of Equation (4.1) is the transient thermal

response. Since all derivatives with respect to time go to zeroes in the

steady state, steady-state analysis is needed to solve the PDE, which

4.1. Thermal Analysis for 3D ICs 17

is the well-known Poisson’s equation.

A set of boundary conditions must be added in order to get a well-

deﬁned solution to Equation (4.1). It typically involves building a pack-

age macro model and assuming a constant ambient temperature is in-

teracted with the model. There are two methods to discretize the chip

and form a system of linear equations representing the temperature

distribution and power density distribution: ﬁnite diﬀerence method

(FDM) and ﬁnite element method (FEM). The diﬀerence between them

is that FDM discretizes the diﬀerential operator while the FEM dis-

cretizes the temperature ﬁeld. Both of them can handle complicated

material structures such as non-uniform interconnects distributions in

a chip.

In FDM, a heat transfer theory is adopted, which builds an equiva-

lent thermal circuit through the thermal-electrical analogy. The steady-

state equation represents the network with thermal resistors connected

between nodes and with thermal current sources mapping to power

sources. The voltage and the temperature at the nodes can be com-

puted by solving the circuit. The ground node is considered as a con-

stant temperature node, typically the ambient temperature. The FEM

has diﬀerent perspective to solve Poisson’s equation. The design space

is discretized into elements, with shapes such as tetrahedral or hexa-

hedra. A reasonable discretization divides the chip into 8-node rectan-

gular hexahedral elements. The temperature within an element then is

calculated using an interpolation function.

Since FDM methods are similar to power grid analysis problem,

similar solution techniques can be used, such as the multigrid-based

approaches. Li et al [25] proposed multigrid (MG) techniques for fast

chip level thermal steady-state and transient simulation. This approach

avoids an explicit construction of the matrix problem that is intractable

for most full-chip problems. Speciﬁc MG treatments are proposed to

cope with the strong anisotropy of the full-chip thermal problem that

is created by the vast diﬀerence inmaterial thermal properties and chip

geometries. Importantly, this work demonstrates that only with careful

thermal modeling assumptions and appropriate choices for grid hier-

archy, MG operators, and smoothing steps across grid points can a

full-chip thermal problem be accurately and eﬃciently analyzed. The

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