How Chinese LLMs accelerate chip design workflows through TokenEase's unified API
The semiconductor industry drives every aspect of modern life, yet designing a new chip costs $500M+ and takes 3-5 years. With transistor counts exceeding 100 billion on advanced nodes, human designers struggle to manage complexity. Chinese LLMs like DeepSeek-V4, GLM-4, and Qwen3 provide the reasoning and pattern-matching capabilities needed to automate EDA workflows, optimize yields, and compress design cycles.
Electronic Design Automation tools require thousands of lines of Tcl/Python scripts for design flows. LLMs can generate, debug, and optimize these scripts from natural language descriptions — dramatically reducing setup time and human error.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are an EDA automation engineer specializing in Cadence Innovus and Synopsys ICC2. Generate Tcl scripts for physical design flows. Include error handling, logging, and design rule checks. Use industry-standard naming conventions."},
{"role": "user", "content": """Generate a complete floorplanning and placement script for:
- Technology: TSMC 3nm (N3E)
- Design: AI Accelerator (8x8 MAC array, 2MB SRAM)
- Target frequency: 2.5 GHz
- Power domains: 4 (CORE, SRAM, IO, ALWAYS_ON)
- Constraints: Max utilization 75%, aspect ratio 1:1.2, core margin 10um
- Macros: 16 SRAM blocks (512KB each), 4 PLLs
Include: floorplan creation, macro placement, power planning, standard cell placement, and initial optimization."""}
],
"temperature": 0.3,
"max_tokens": 2500
}
)
script = response.json()["choices"][0]["message"]["content"]
print(script)
# Output: Complete Tcl script with floorplan commands, macro placement strategy,
# power ring creation, and placement optimization settingsWafer yields at advanced nodes can drop below 50%. LLMs analyze scan chain diagnostics, failure bitmaps, and process control monitor data to identify systematic defects, root-cause yield limiters, and recommend process adjustments.
yield_data = """
Wafer Lot: W-2026-0847 (TSMC N3E, 25 wafers)
Overall Yield: 62.3% (target: 78%)
Failure breakdown by die region:
- SRAM array (upper-left quadrant): 18.2% fail rate
- Logic core (center): 8.1% fail rate
- IO ring (perimeter): 3.4% fail rate
- PLL region: 12.7% fail rate
Failure modes from scan diagnosis:
- SRAM: Bit-line leakage (47%), Word-line shorts (31%), Cell stability (22%)
- Logic: Setup violations (58%), Hold violations (25%), Crosstalk (17%)
- PLL: VCO frequency drift (89%), Lock time failures (11%)
Process control data:
- Gate oxide thickness: Mean 1.82nm, spec 1.80+/-0.05nm, Cpk=0.92
- Metal 1 CD: Mean 18nm, spec 18+/-1.5nm, Cpk=1.15
- Contact resistance: Elevated in upper-left die region only
- Anneal temperature: Profile shows 3C gradient across wafer
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "glm-4",
"messages": [
{"role": "system", "content": "You are a semiconductor yield engineer. Analyze yield loss data, identify root causes using statistical and physical reasoning, and recommend specific process or design improvements. Prioritize by impact and feasibility."},
{"role": "user", "content": f"Analyze this yield data and recommend actions:\n\n{yield_data}"}
],
"temperature": 0.2,
"max_tokens": 1800
}
)
analysis = response.json()["choices"][0]["message"]["content"]
print(analysis)
# Output: Identified anneal temperature gradient as primary root cause,
# Recommended process equipment calibration and SRAM design margin increaseLVS verification ensures physical layouts match circuit schematics. When thousands of errors appear, LLMs can parse error reports, group related issues, suggest fixes, and even generate corrected layout edits.
lvs_errors = """
LVS Report: Design "AI_Accelerator_Top"
Total Errors: 1,247
Top error categories:
- Net mismatches: 423 (expected: 0)
- Device count mismatches: 312
- Floating nets: 198
- Short circuits: 189
- Missing instances: 125
Sample net mismatch details:
Net "clk_tree/clk_div2" in schematic has 45 connections
Net "clk_tree/clk_div2" in layout has 43 connections
Missing in layout: Connections to u_sram_ctrl/clk_in, u_dma/clk_in
Sample floating net:
Net "rst_n_sync[3]" connected to only 1 pin in layout
Expected: Connected to 12 flip-flop reset pins
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "qwen3-32b",
"messages": [
{"role": "system", "content": "You are a physical verification engineer. Analyze LVS error reports, group related errors by root cause, and provide specific debugging steps. Consider common layout mistakes and EDA tool quirks. Output structured action plan."},
{"role": "user", "content": f"Debug these LVS errors:\n\n{lvs_errors}"}
],
"temperature": 0.2,
"max_tokens": 1500
}
)
debug_plan = response.json()["choices"][0]["message"]["content"]
print(debug_plan)
# Output: Grouped errors by root cause, identified clock tree routing issue,
# Provided step-by-step debugging procedure with Calibre commandsMeeting timing closure at 2+ GHz requires analyzing millions of timing paths. LLMs can parse STA reports, identify critical path bottlenecks, recommend buffer sizing and placement, and generate ECO scripts for timing fixes.
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a timing closure engineer. Analyze static timing analysis reports, identify critical path bottlenecks, and recommend specific optimization strategies including: cell sizing, buffer insertion, route optimization, and clock skew balancing. Generate ECO scripts where applicable."},
{"role": "user", "content": """Optimize timing for this critical path:
Path: u_mac_array/row_7/col_3/mult_out_reg/CK -> u_accumulator/acc_reg_255/D
Setup slack: -127ps (VIOLATION, target: 0ps)
Required time: 400ps (2.5 GHz clock)
Actual arrival: 527ps
Path breakdown:
- Clock source latency: 45ps
- Launch clock path: 78ps (u_mac_array/clk_tree)
- Data path: 312ps
* u_mult_7_3/mult_out: 87ps (cell delay)
* Route: net_7842 (45ps, 680um)
* u_adder_tree/level1: 56ps
* Route: net_8912 (38ps, 520um)
* u_adder_tree/level2: 48ps
* Route: net_9234 (38ps, 490um)
* u_accumulator/acc_reg_255: 0ps (setup)
- Capture clock path: 92ps
Other context:
- This path is in the MAC accumulation chain
- 256 similar paths in the same structure
- Current cell library: 7nm std cells, VT: LVT
- Available optimization: cell sizing, buffer insertion, clock skew tuning"""}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
optimization = response.json()["choices"][0]["message"]["content"]
print(optimization)
# Output: Identified long routes as primary bottleneck,
# Recommended buffer insertion points, cell upsizing strategy,
# Generated ECO script for automated implementationScanning Electron Microscopes and optical inspection tools capture millions of wafer images. LLMs with vision capabilities can classify defect types, measure critical dimensions, and flag process excursions — accelerating quality control.
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "glm-4v",
"messages": [
{"role": "system", "content": "You are a semiconductor metrology engineer. Analyze SEM images for defects. Classify defect type (particle, scratch, bridging, void, misalignment), estimate severity, and recommend process step investigation. Use standard defect nomenclature."},
{"role": "user", "content": [
{"type": "text", "text": "Analyze this SEM image of Metal 2 layer after CMP. Identify defects and assess impact on yield."},
{"type": "image_url", "image_url": {"url": "https://example.com/wafer-m2-sem-2026-0847.jpg"}}
]}
],
"temperature": 0.3,
"max_tokens": 1200
}
)
defect_report = response.json()["choices"][0]["message"]["content"]
print(defect_report)
# Output: Identified Cu dishing in wide metal lines,
# Classified as CMP process excursion,
# Recommended pad pressure adjustmentChip designs depend on hundreds of suppliers across geopolitical boundaries. LLMs can analyze bill-of-materials, supplier financials, geopolitical news, and logistics data to predict supply disruptions and recommend alternative sourcing.
bom_data = """
Design: AI_Accelerator_Top
Total unique parts: 847
Critical long-lead items:
1. HBM3 Memory (SK Hynix)
- Lead time: 26 weeks
- Allocation: 60% of demand
- Risk factors: Geopolitical (US-China tech restrictions)
2. Advanced Packaging Substrate (Unimicron)
- Lead time: 18 weeks
- Allocation: 85% of demand
- Risk factors: Taiwan earthquake zone, single-source
3. 3nm Wafer Fab (TSMC)
- Lead time: 16 weeks
- Allocation: 90% of demand
- Risk factors: Water shortage, power grid stability
4. RF Front-End Module (Qorvo)
- Lead time: 12 weeks
- Allocation: 100% of demand
- Risk factors: Acquisition by Apple rumored
Geopolitical context (last 30 days):
- New US export controls on AI chips to China
- Taiwan Strait military exercises
- Japan earthquake disrupted substrate supply chain
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "qwen3-32b",
"messages": [
{"role": "system", "content": "You are a supply chain risk analyst for the semiconductor industry. Assess BOM risks considering: geopolitical factors, supplier financial health, natural disaster exposure, and single-source dependencies. Calculate overall risk score and recommend mitigation strategies with cost estimates."},
{"role": "user", "content": f"Assess supply chain risks:\n\n{bom_data}"}
],
"temperature": 0.3,
"max_tokens": 1800
}
)
risk_report = response.json()["choices"][0]["message"]["content"]
print(risk_report)
# Output: Overall risk score: HIGH (7.2/10), identified HBM3 and substrate as top risks,
# Recommended dual-sourcing strategy with cost impact analysis| Application | Recommended Model | Why |
|---|---|---|
| EDA Scripting | DeepSeek-V4 | Code generation, Tcl/Python fluency |
| Yield Analysis | GLM-4 | Statistical reasoning, process knowledge |
| LVS Debugging | Qwen3-32B | Structured error analysis, pattern grouping |
| STA Optimization | DeepSeek-V4 | Numerical optimization, path reasoning |
| Defect Detection | GLM-4V | Vision analysis for SEM images |
| Supply Chain | Qwen3-32B | Multi-factor risk assessment |
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