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Mixed-Size Placement Algorithm for PPA Optimization
Mixed-Size Placement is one of
the key steps in the IC Physical Design (PD). It requires effective placement of modules
of different sizes, such as macros and standard cells, within a chip. The ultimate goal of
PD is to optimize the chip Performance, Power consumption, and Area (PPA). The placement
of macros and standard cells impacts each other and plays a crucial role in the quality of
the overall chip layout, thus determining the final quality of the chip's physical
implementation.
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OpenROAD
Placement methods
PPA
Chair
Dr. Yu Huang
Scientist of semiconductors at Huawei, EDA Chief Architect of
HiSilicon
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Technical salon
A Pattern Matching Algorithm Based on Hanqing
Hanqing solution realizes the
upstream and downstream collaboration of China's EDA, completes the series of analog,
digital, and manufacturing EDA tool solutions, and promotes the domestic computing
ecosystem. The pattern matching based on Hanqing can fully utilize the capabilities of the
of Hanqing.
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DRC
OPC
C++
Spatial Geometry
Chair
Dr. Yu Huang
Scientist of semiconductors at Huawei, EDA Chief Architect of
HiSilicon
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Technical salon
Internship opportunities
1 vs 1 mentor opportunity with a renowned company
Logic and Structural Analysis Algorithms for Large Scale Digital Circuits
Logic analysis based on circuit
structure and function is at the core of many EDA tools, especially for logic
synthesis,
optimization, and verification. Since logic analysis is based on the theory of Boolean
Algebra, the algorithmic complexity of most problems is NP-Complete. Therefore, EDA
tools
need to provide efficient heuristic algorithms tailored to different problems and
applications. The algorithms involved in this task can be widely applied in EDA tools
such
as Logic Equivalence Checking (LEC), Automatic Test Pattern Generation (ATPG), and
testability analysis. A high-performance and robust algorithm is key to ensuring that
the
relevant tools can quickly obtain results in various application environments when
dealing
with ultra-large-scale circuit designs.
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Internship opportunities
1 vs 1 mentor opportunity with a renowned company
C++
Static Verification
Logic Synthesis
DFT
BDD
Chair
Dr. Yingmeng Li
VP R&D, EnnoCAD
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Monte Carlo implantation
In semiconductor process
simulation, two methods are commonly used for ion implantation: the analytic
implantation
and the Monte Carlo implantation. The analytic approach uses a statistical
distribution
function to approximate the doping profile, which requires relatively little CPU-time.
However, the analytic implant cannot accurately predict the doping profile for complex
device structures, especially for small dimensions. In contrast to that, the
physically
based Monte Carlo implantation uses an atomistic scattering method to describe the
trajectory of ions, is able to predict better results.
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Monte Carlo
Binary Collision
approximation
Si crystalline
Ion trajectory
Chair
JianWei Lu
Algorithm Researcher
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Technical salon
Functional Safety Verification Computing Acceleration
Functional safety verification
tests and verifies the resilience of mission-critical electronic systems for
systematic
and random faults. Systematic faults arising from design or manufacturing flaws
typically
are resolved by functional and manufacturing tests before delivery, while random
faults
require mechanisms that operate throughout the lifetime of a part. Hardware portions
of
the system, specifically integrated circuits, insert special safety mechanisms (SMs)
into
the design. The validation of theses SMs could be achieved in advance by software
simulation.
Bonus
Mentorship opportunities
Technical salon
FUSA
FuSa Verification
Fault Simulation
Parallel
Distributed
Chair
Jie Ren
EDA CDO, Semitronix