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Spectre Tech Tips: Device Aging? Yes, even Silicon wears out
While most of us would like our electronic gadgets to last forever, the reality is that these gadgets have a lifetime. Most of the time, the lifetime of devices is limited by either mechanical (switch, relay), or thermal (fuse, capacitor) failures. However, as microchips designed in advanced technologies become more pervasive, the lifetime of microchips has become an additional issue.
Several effects contribute to device aging. Some effects, such as electromigration and Time-Dependent Dielectric Breakdown (TDDB) cause a sudden failure, while other effects, such as Hot Carrier injection (HCI) and Bias Temperature Instability (BTI), continuously degrade the performance with time.
As process feature sizes have scaled down, analyzing device reliability has become more complex as more and more phenomena contribute to the change in device characteristics. This is shown in the figure below.
There is one additional phenomenon (not shown in the figure), Time-Dependent Dielectric Breakdown (TDDB), which has impacted designers since the days of high-voltage linear analog. However, traditional CMOS process scaling rules helped avoid this issue until advanced node processes.
HCI Effect
HCI re-emerged as an issue as CMOS transistors scaled down into the deep submicron region. At shorter gate lengths, the electric field increases because power supply voltages don’t scale as fast as gate length scales down. The higher electric field in the channel leads to impact ionization collisions of electrons flow in the channel with the atoms in the lattice. These collisions generate hot electrons. Some of the hot electrons get trapped in the gate oxide. Electrons in the oxide increase the device threshold voltage and reduce mobility of electrons in the channel.
NBTI Effect
Negative Bias Temperature Instability (NBTI) occurs when positive carriers get trapped at oxide/silicon interface or in the oxide due to electrical stress when the voltage across the gate-source junction is negative. This effect is temperature dependent. NBTI is primarily an issue for PMOS transistors. The effect of NBTI increases when nitrogen is added to the oxide to reduce gate leakage current since less energy is required to create trap sites. Unlike HCI, which occurs gradually, NBTI has a more immediate impact. In addition, a partial recovery from NBTI can occur when electrical stress is removed and/or temperature drops. The NBTI is reversible when it happens in the bulk of the device, but it is permanent when the trapping occurs at the interface.
PBTI Efffect
Positive Bias Temperature Instability (PBTI) has become an issue with the adoption of Metal Gate High-K gate-stack technologies. NMOS transistors experience PBTI whose impact is temperature dependent. Unlike NBTI, interface traps are not created; therefore, full recovery is possible when the electrical stress is removed and/or temperature is lowered.
Spectre Native Reliability Analysis
You can use the Native Reliability feature of Spectre® to perform reliability analysis and address these effects with ease.
To perform reliability analysis, a prerequisite aging model should be available from the foundry. Some foundries use the Spectre® AgeMOS built-in model for Aging, while others use their proprietary model libraries to calculate the aging degradation for transistors.
Spectre® performs reliability analysis in two steps:
- In the first step, Spectre simulates the stress over the transistors and calculates the degradation based on the results of this simulation.
- In the second step, Spectre applies the degradation on the transistors to simulate the behavior of the circuit after the defined aging years.
Below is an example of a simple reliability statement to analyze the behavior of the circuit after 10 years of operation.
rel reliability {
age time=[10.0000y]
accuracy level=1
// Stress simulation
tran_stress tran stop=10u annotate=status
// Aged simulation
tran_aged tran stop=10u annotate=status
}
Related Resources
- Spectre Classic Simulator, Spectre Accelerated Parallel Simulator (APS), and Spectre Extensive Partitioning Simulator (XPS) User Guide
- Spectre RelXpert Reliability Simulation User Guide
You may also contact your Cadence support AE for guidance.
For more information on Cadence products and services, visit www.cadence.com.
About Spectre Tech Tips
Spectre Tech Tips is a blog series aimed at exploring the capabilities and potential of Spectre®. In addition to providing insight into the useful features and enhancements in Spectre, this series broadcasts the voice of different bloggers and experts, who share their knowledge and experience on all things related to Spectre. Enter your email address in the Subscriptions box at the top of the page and click SUBSCRIBE NOW to receive notifications about our latest Spectre Tech Tips posts.
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Accurate Pin-to-Pin Resistance Modeling for Wide, Slotted Metal Structures Using Advanced Adaptive Mesh Extraction Technology in Quantus
In Analog/RF layouts, designers frequently use slotted metal structures. Such slotting is done either to satisfy DRC requirements from foundries to satisfy max. density rule criteria, or to reduce eddy current losses in return paths of a transmission line/coiled spiral inductor. Current flow in such slotted metal structures is non-uniform, hence, traditional parasitic extraction approaches with predefined fracture length specifications do not result in accurate pin-to-pin resistance. Hence, Cadence Design Systems sign-off extraction solution – Quantus, provides a mesh extraction approach for these cases, where the slotted/wide metal structures are broken down into smaller squares each representing a small parasitic resistance. This approach of moving from a lumped to distributed model for resistance extraction leads to much better DC V/I current modelling. For example, the MIMCAP layout structure shown below has its 2 terminals ctm (top) and cbm (bot), connected to Metal 13 for its VSS and VDD connections. Its ESR (Effective Series Resistance) is calculated at 50GHz by doing an AC V/I simulation.
Image Source: PVS Quick View Editor
ESR @ 50GHz with regular square counting based extraction is 19.04 ohms (incorrect), whereas by using advanced adaptive mesh gives 5.16 ohms (correct). The MIMCAP structure has very wide top and bottom terminals which needs to be modeled using mesh approach to give accurate results. However, the challenge with this approach is increased netlist size, simulation run time and user overhead in setting up the mesh layers and corresponding mesh sizes to be extracted.
Quantus has newly introduced advanced adaptive mesh extraction, where intelligent meshing is applied to automatically detect non-uniform current regions near discontinuous/slotted metal structures where adaptive grid meshing is applied and detect uniform current regions near continuous metal structures where square counting-based meshing is applied.
The below case study is performed on a ST BiCMOS 55nm process. However, the advanced adaptive mesh technology can be applied to other advanced technology nodes too with similar benefits.
Image Source: Cadence Virtuoso Layout Editor
Adaptive mesh fractured the M1 slotted ground plane by using smaller fracture regions even in right most regions of the layout structure, where there are no slots and current flow is uniform. Whereas, the new Advanced adaptive mesh (available in latest Quantus builds) was able to automatically distinguish between the slotted and continuous metal regions and apply intelligent meshing only near the slotted metal portion, where current flow would be non-uniform and distributed modelling is required. Also, in Advanced adaptive mesh, the size per mesh is 5.96x5.96 which is larger than the 2.59x2.59 determined automatically in Adaptive mesh. Since each fractured square region corresponds to 1 parasitic resistor, this helps reduce the total number of parasitic resistors. A combination of these 2 techniques, helped reduce the netlist size dramatically by ~34%.
In cases where only a certain region of the layout structure needs distributed modelling, meshR user region can be defined, so that Quantus performs advanced adaptive or adaptive meshing in only a certain portion of the layout, whereas the remaining would be square counting based fracturing approach. This further helps reduce netlist size.
The reduction in number of parasitic R and C’s in the advanced adaptive mesh netlist as a result can speed up downstream simulation run time while consuming lesser memory. Its critical to also ascertain the accuracy of the reduced netlist wrt. adaptive mesh to confirm the reduction doesn’t come at the cost of accuracy. Hence, a pin-to-pin DC resistance check is performed on few fractured sub-nodes from the 2 DSPF netlists, keeping in mind the sub-nodes are picked with roughly similar x,y coordinates. Advanced adaptive mesh can also be applied to spice extracted view output formats. As demonstrated, pin to pin resistance accuracy in advanced adaptive mesh is highly accurate.
Depending on the level of simulation accuracy needed, there is a capability to specify a k-scaling factor to increase or decrease the auto mesh size. This helps fine tune the netlist size vs. simulation accuracy desired.
The Adaptive Mesh feature is based on the user-specified mesh size for the conductor layers that are to be meshed. Alternatively, Advanced Adaptive Mesh feature is based on automatic mesh sizing for the conductor layers. Thus, setup and usage of Advanced adaptive mesh is simpler for these users. With the advent of advanced nodes and slotted layout structures, distributed MIMCAPs designs and other newer design artifacts, it is recommended to use the Advanced adaptive mesh solution in Quantus to achieve better netlist size, accuracy and simulation run times.