Structural netlist efficiently verifies analog IP
Naveen Srivastava , Rohit Ranjan & Amit Bathla (Freescale)
EDN (June 04, 2015)
One of the major issues faced in the verification of analog or AMS IP in the SOC environment is the behavioral model’s limitations. Since behavioral models are not perfectly able to replicate analog behavior in a verification environment, many critical bugs are left uncovered.
We will be focusing on this problem, and will discuss how to achieve more accurate analog behavior by using a structural netlist instead of a behavioral model to reduce the number of silicon defects and the verification cycle time.
SPICE model netlist conversion to structural netlist
This approach talks about using methodologies which directly convert transistor level SPICE model into Structural netlist. The principle of these methodologies is to work by isolating Analog circuits from logic and automatically recognizing the latch and flip-flop structures. The design is partitioned into cells, and an automatic algorithm on pattern based function extraction is run. The output is a structural netlist which is used in place of behavioral model for verification purpose. The structural netlist so obtained is pretty much close to actual analog SPICE model.
To read the full article, click here
Related Semiconductor IP
- HBM4 PHY IP
- eFuse Controller IP
- Secure Storage Solution for OTP IP
- Ultra-Low-Power LPDDR3/LPDDR2/DDR3L Combo Subsystem
- MIPI D-PHY and FPD-Link (LVDS) Combinational Transmitter for TSMC 22nm ULP
Related Articles
- Analog IP verification guidelines
- The common silicon issues in analog IP integration
- Agile Analog's Approach to Analog IP Design and Quality --- Why "Silicon Proven" is NOT What You Think
- Time Interleaving of Analog to Digital Converters: Calibration Techniques, Limitations & what to look in Time Interleaved ADC IP prior to licensing
Latest Articles
- Making Strong Error-Correcting Codes Work Effectively for HBM in AI Inference
- Sensitivity-Aware Mixed-Precision Quantization for ReRAM-based Computing-in-Memory
- ElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update
- A 14ns-Latency 9Gb/s 0.44mm² 62pJ/b Short-Blocklength LDPC Decoder ASIC in 22FDX
- Pipeline Stage Resolved Timing Characterization of FPGA and ASIC Implementations of a RISC V Processor