Safety Certification Guide¶

SC-NeuroCore provides an automated certification pipeline for safety-critical neuromorphic deployments. This guide covers the complete evidence generation workflow for DO-254 Level A (avionics), IEC 61508 SIL 3/4 (industrial), and ISO 26262 ASIL-D (automotive) certification, including compliance matrices, fault trees, reliability prediction, formal equivalence, and CDC analysis.

1. Mathematical Formalism¶

1.1 Failure Rate Model¶

Component failure rates follow the Arrhenius-based MIL-HDBK-217F model:

$$ \lambda = \lambda_b \cdot \pi_T \cdot \pi_E \cdot \pi_Q $$

where: - $\lambda_b$ = base failure rate (FITs) - $\pi_T = e^{-\frac{E_a}{k_B}\left(\frac{1}{T_J} - \frac{1}{T_{\text{ref}}}\right)}$ — temperature acceleration - $\pi_E$ = environmental factor (ground, airborne, space) - $\pi_Q$ = quality factor

1.2 Mean Time To Failure¶

$$ \text{MTTF} = \frac{10^9}{\lambda} \text{ hours} $$

1.3 Fault Tree Quantification¶

The probability of the top event (system failure) given $n$ independent basic events with rates $\lambda_i$:

$$ P_{\text{top}}(t) = 1 - \prod_{i=1}^{n} (1 - P_i(t)) $$

For rare events ($\lambda_i t \ll 1$):

$$ P_{\text{top}}(t) \approx \sum_{i=1}^{n} \lambda_i \cdot t $$

1.4 Minimal Cut Sets (MCS)¶

The Fault Tree Analysis (FTA) identifies minimal cut sets — the smallest combinations of basic events that cause the top event. For a neuron with state variable overflow as the top event:

MCS₁ = {overflow_v} — single state variable overflow
MCS₂ = {overflow_v, overflow_u} — coupled overflow (Izhikevich)

The probability via MCS:

$$ P_{\text{top}} \leq \sum_{j=1}^{|\text{MCS}|} \prod_{i \in C_j} P_i $$

1.5 ODE Stability Criterion¶

The Euler forward method is stable when:

$$ |1 + \lambda_{\max} \cdot \Delta t| < 1 $$

where $\lambda_{\max}$ is the largest eigenvalue of the Jacobian. For a single-variable LIF:

$$ \Delta t < \frac{2 \cdot \tau_m}{1} = 2\tau_m $$

The critical timestep for stability:

$$ \Delta t_{\text{crit}} = \frac{2}{|\lambda_{\max}|} $$

2. Architecture¶

2.1 Certification Pipeline¶

flowchart TB
    A["Neuron Equations"] --> B["generate_compliance_matrix()"]
    B --> C["verify_ode_stability()"]
    C --> D["generate_fault_tree()"]
    D --> E["predict_reliability()"]
    E --> F["generate_provenance_chain()"]
    F --> G["analyze_cdc()"]
    G --> H["generate_equivalence_sketch()"]
    H --> I["generate_testbench()"]
    I --> J["generate_compilation_report()"]

    style A fill:#e1f5fe
    style J fill:#e8f5e9

2.2 Evidence Package Structure¶

Text Only

certification/
├── compliance_matrix.json      # Standard-to-feature mapping
├── ode_stability_proof.json    # Eigenvalue analysis
├── fault_tree.json             # FTA with minimal cut sets
├── reliability_report.json     # MTTF prediction
├── provenance_chain.json       # Hash chain
├── cdc_report.json             # Clock domain crossing
├── equivalence_sketch.sv       # Formal equivalence
├── testbench.py                # Cocotb verification
└── certification_report.md     # Human-readable summary

3. Supported Standards¶

3.1 Certification Feature Map¶

Feature	§	DO-254	IEC 61508	ISO 26262
Compliance Matrix	29	✅	✅	✅
Provenance Chain	28	✅	✅	✅
Fault Tree (FTA)	50	✅	✅	✅
Reliability (MTTF)	49	✅	✅	✅
SEU/TMR Hardening	7	✅	—	✅
Formal Equivalence	26	✅	✅	—
ODE Stability	43	✅	✅	✅
Auto-Testbench	51	✅	✅	✅
Side-Channel Lint	31	—	—	✅
CDC Analysis	52	✅	✅	✅

3.2 Standard-Specific Requirements¶

Standard	Key Requirement	SC-NeuroCore Evidence
DO-254 Level A	100% MC/DC coverage	`generate_testbench()` + formal
DO-254 Level A	Design assurance plan	`generate_compilation_report()`
IEC 61508 SIL 3	FIT rate < 100	`predict_reliability()`
IEC 61508 SIL 4	FIT rate < 10	`predict_reliability()` + TMR
ISO 26262 ASIL-D	FMEDA required	`generate_fault_tree()`
ISO 26262 ASIL-D	Formal verification	`generate_sva()` + SymbiYosys

3.3 Design Assurance Levels¶

DAL	Standard	Single-Fault Tolerance	Formal Required?
A	DO-254	Yes	Yes
B	DO-254	Yes	Recommended
C	DO-254	No	No
SIL 3	IEC 61508	Yes	Recommended
SIL 4	IEC 61508	Yes	Yes
ASIL-D	ISO 26262	Yes	Yes

4. Python API¶

4.1 Generate Compliance Matrix¶

Python

from sc_neurocore.compiler.intelligence import generate_compliance_matrix

matrix = generate_compliance_matrix(
    standard="DO-254",
    equations={"v": "-(v - v_rest) / tau + I"},
    profile_name="bae_rad750_sq",
)
# Matrix maps SC-NeuroCore features to DO-254 objectives
for req in matrix.requirements:
    print(f"  {req['id']}: {req['description']} — {req['status']}")

4.2 Verify ODE Stability¶

Python

from sc_neurocore.compiler.intelligence import verify_ode_stability

result = verify_ode_stability(
    equations={"v": "-(v - v_rest) / tau + I"},
    dt=0.1,
    time_constants={"v": 10.0},
)
assert result.stable, f"UNSTABLE: critical dt = {result.critical_dt}"
print(f"Stable: dt={result.dt}, critical_dt={result.critical_dt:.2f}")
print(f"Stability margin: {result.margin:.1%}")

4.3 Generate Fault Tree¶

Python

from sc_neurocore.compiler.intelligence import generate_fault_tree

ft = generate_fault_tree("sc_lif", {"v": "-(v)/tau + I"})
print(f"Top event: {ft.top_event}")
print(f"Basic events: {len(ft.basic_events)}")
print(f"Minimal cut sets: {len(ft.mcs)}")

# Each basic event has failure rate for quantitative FTA
for e in ft.basic_events:
    print(f"  {e['id']}: λ = {e['rate']:.0e} /hr — {e['description']}")

4.4 Predict Reliability¶

Python

from sc_neurocore.compiler.intelligence import predict_reliability

r = predict_reliability(
    voltage_v=0.9,
    temperature_c=85.0,  # worst-case junction temp
    node_nm=28,
)
print(f"MTTF: {r.mttf_years:.1f} years")
print(f"FIT rate: {r.fit_rate:.0f}")
print(f"Dominant failure: {r.failure_mode}")

4.5 Generate Provenance Chain¶

Python

from sc_neurocore.compiler.intelligence import generate_provenance_chain

chain = generate_provenance_chain(
    equations={"v": "-(v)/tau + I"},
    profile_name="bae_rad750_sq",
    author="avionics_team",
)
print(f"Chain hash: {chain.chain_hash}")
# Hash is reproducible — same inputs produce same hash

4.6 CDC Analysis¶

Python

from sc_neurocore.compiler.intelligence import analyze_cdc

report = analyze_cdc(
    {"v": "-(v)/tau + I", "w": "a*(b*v - w)"},
    clock_domains={"v": "clk_100mhz", "w": "clk_10mhz"},
)
assert report.safe, f"CDC violations: {report.violations}"
print(f"Clock domains: {report.num_domains}")
print(f"Crossings: {report.num_crossings}")
print(f"Violations: {report.num_violations}")

4.7 Generate Certification Testbench¶

Python

from sc_neurocore.compiler.intelligence import generate_testbench

tb = generate_testbench(
    "sc_lif", {"v": "-(v)/tau + I"},
    framework="cocotb", num_cycles=10000,
)
with open("test_sc_lif_cert.py", "w") as f:
    f.write(tb)

4.8 Formal Equivalence Sketch¶

Python

from sc_neurocore.compiler.intelligence import generate_equivalence_sketch

sketch = generate_equivalence_sketch(
    equations={"v": "-(v - v_rest) / tau + I"},
    data_width=16, fraction=8,
)
with open("sc_lif_equiv.sv", "w") as f:
    f.write(sketch)

4.9 Full Certification Report¶

Python

from sc_neurocore.compiler.intelligence import generate_compilation_report

report = generate_compilation_report(
    "sc_lif", {"v": "-(v)/tau + I"}, "bae_rad750_sq",
    include_carbon=True, include_reliability=True,
)
with open("certification_report.md", "w") as f:
    f.write(report)

5. CLI Usage¶

5.1 Generate Compliance Matrix¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import generate_compliance_matrix
m = generate_compliance_matrix('DO-254', {'v': '-(v)/tau + I'})
print(f'Standard: {m.standard}')
print(f'Requirements: {len(m.requirements)}')
for r in m.requirements[:5]:
    print(f'  {r[\"id\"]}: {r[\"status\"]}')
"

5.2 Verify Stability¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import verify_ode_stability
r = verify_ode_stability({'v': '-(v)/tau + I'}, dt=0.1, time_constants={'v': 10.0})
print(f'Stable: {r.stable}, critical dt: {r.critical_dt:.2f}')
"

5.3 Generate Fault Tree¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import generate_fault_tree
ft = generate_fault_tree('sc_lif', {'v': '-(v)/tau + I'})
print(f'Top event: {ft.top_event}')
print(f'Basic events: {len(ft.basic_events)}')
print(f'MCS: {len(ft.mcs)}')
"

6. Space-Qualified Workflow¶

6.1 Complete Space Deployment Pipeline¶

For space-grade deployments, combine the certification pipeline with:

SEU hardening (§7) — Triple Modular Redundancy
Supply chain risk (§36) — ITAR/sole-source analysis
Thermal envelope (§40) — junction temp under vacuum/radiation
License compliance (§56) — export control verification

Python

from sc_neurocore.compiler.intelligence import (
    score_supply_chain_risk, estimate_thermal_envelope,
    check_license_compliance,
)

# Supply chain
risk = score_supply_chain_risk("bae_rad750_sq")
print(f"Supply chain risk: {risk.overall_risk}")

# Thermal (in vacuum — higher theta_ja)
thermal = estimate_thermal_envelope(
    power_mw=2000, theta_ja=40.0, t_ambient=60.0,
)
assert thermal.pass_fail == "PASS"

# License (export control)
lic = check_license_compliance("proprietary", {
    "sc_neurocore": "AGPL-3.0",
})

6.2 Radiation Hardening Considerations¶

Rad-Hard Feature	Implementation	Coverage
TMR registers	Voter logic on state regs	SEU mitigation
ECC on BRAM	SECDED encoding	MBU tolerance
Configuration scrubbing	Periodic readback	Configuration SEU
Latch-up protection	Current limiters	SEL prevention

6.3 Space-Grade Target Profiles¶

Profile	Rad Tolerance	Process	MTTF (LEO)
BAE RAD750-SQ	100 krad	150 nm	> 15 years
Microchip RT PolarFire	100 krad	28 nm	> 10 years
Xilinx XQRKU060	200 krad	20 nm	> 12 years

7. Performance Characteristics¶

7.1 Evidence Generation Time¶

Evidence Type	Complexity	Time
Compliance matrix	O(1)	< 1 ms
ODE stability	O(n²)	< 10 ms
Fault tree	O(n·m)	< 50 ms
Reliability	O(1)	< 1 ms
Provenance chain	O(n)	< 5 ms
CDC analysis	O(n²)	< 20 ms
Equivalence sketch	O(n)	< 10 ms
Full report	O(n²)	< 100 ms

7.2 Certification Evidence Completeness¶

Standard	Required Evidence	Auto-Generated	Manual
DO-254 A	12 items	10	2
IEC 61508 SIL 4	8 items	7	1
ISO 26262 ASIL-D	10 items	8	2

7.3 Reliability by Process Node¶

Process	Base FIT Rate	MTTF (commercial)	MTTF (industrial)
7 nm	15	7.6 years	5.2 years
16 nm	10	11.4 years	7.8 years
28 nm	5	22.8 years	15.6 years
45 nm	3	38.1 years	26.0 years
65 nm	2	57.1 years	39.0 years

8. Test Suite and Verification¶

8.1 Compliance Matrix Test¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import generate_compliance_matrix
for std in ['DO-254', 'IEC-61508', 'ISO-26262']:
    m = generate_compliance_matrix(std, {'v': '-(v)/tau + I'})
    assert len(m.requirements) > 0
    print(f'{std}: {len(m.requirements)} requirements — PASS')
"

8.2 ODE Stability Test¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import verify_ode_stability

# Stable case
r = verify_ode_stability({'v': '-(v)/tau + I'}, dt=0.1, time_constants={'v': 10.0})
assert r.stable

# Unstable case (dt too large)
r = verify_ode_stability({'v': '-(v)/tau + I'}, dt=100.0, time_constants={'v': 10.0})
assert not r.stable
print('ODE stability: PASS')
"

8.3 Fault Tree Generation Test¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import generate_fault_tree

# Single variable
ft = generate_fault_tree('sc_lif', {'v': '-(v)/tau + I'})
assert len(ft.basic_events) > 0
assert len(ft.mcs) > 0

# Multi-variable
ft = generate_fault_tree('sc_izh', {'v': '-(v)/tau + I', 'u': 'a*(b*v - u)'})
assert len(ft.basic_events) > len(generate_fault_tree('sc_lif', {'v': '-(v)/tau + I'}).basic_events)
print('Fault tree: PASS')
"

8.4 Reliability Prediction Test¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import predict_reliability

# Different process nodes should give different MTTF
r28 = predict_reliability(0.9, 85.0, 28)
r7 = predict_reliability(0.75, 85.0, 7)
assert r28.mttf_years > r7.mttf_years  # Larger node = more reliable
print(f'28nm MTTF: {r28.mttf_years:.1f} years')
print(f'7nm MTTF:  {r7.mttf_years:.1f} years')
print('Reliability: PASS')
"

8.5 Provenance Chain Reproducibility Test¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import generate_provenance_chain

c1 = generate_provenance_chain({'v': '-(v)/tau + I'}, 'artix7', 'test')
c2 = generate_provenance_chain({'v': '-(v)/tau + I'}, 'artix7', 'test')
assert c1.chain_hash == c2.chain_hash, 'Provenance not reproducible!'
print(f'Provenance hash: {c1.chain_hash[:16]}... — PASS')
"

8.6 CDC Analysis Test¶

Bash

python -c "
from sc_neurocore.compiler.intelligence import analyze_cdc

# Same clock — should be safe
r = analyze_cdc({'v': '-(v)/tau + I'}, clock_domains={'v': 'clk'})
assert r.safe

# Different clocks — may have crossings
r = analyze_cdc(
    {'v': '-(v)/tau + I', 'w': 'a*(b*v - w)'},
    clock_domains={'v': 'clk_fast', 'w': 'clk_slow'},
)
print(f'CDC crossings: {r.num_crossings}, safe: {r.safe} — PASS')
"

8.7 E2E Pipeline Test¶

Bash

python -m pytest tests/e2e/test_e2e_pipeline.py -v -k "safety or certification"

8.8 Troubleshooting¶

Symptom	Cause	Fix
Stability fails	dt too large	Reduce timestep
FIT rate too high	High temperature	Add cooling
MCS too many	Complex equations	Simplify or add guard bits
CDC violation	Multi-clock design	Add synchroniser
Provenance mismatch	Non-deterministic inputs	Fix random seeds

References¶

DO-254 standard: RTCA. "Design Assurance Guidance for Airborne Electronic Hardware." DO-254, Rev A, 2005.
IEC 61508: IEC. "Functional Safety of Electrical/Electronic/Programmable Electronic Safety-Related Systems." IEC 61508, 2010.
ISO 26262: ISO. "Road Vehicles — Functional Safety." ISO 26262, 2018.
MIL-HDBK-217F reliability prediction: US DoD. "Reliability Prediction of Electronic Equipment." MIL-HDBK-217F, Notice 2, 1995.

Safety Certification Guide¶

1. Mathematical Formalism¶

1.1 Failure Rate Model¶

1.2 Mean Time To Failure¶

1.3 Fault Tree Quantification¶

1.4 Minimal Cut Sets (MCS)¶

1.5 ODE Stability Criterion¶

2. Architecture¶

2.1 Certification Pipeline¶

2.2 Evidence Package Structure¶

3. Supported Standards¶

3.1 Certification Feature Map¶

3.2 Standard-Specific Requirements¶

3.3 Design Assurance Levels¶

4. Python API¶

4.1 Generate Compliance Matrix¶

4.2 Verify ODE Stability¶

4.3 Generate Fault Tree¶

4.4 Predict Reliability¶

4.5 Generate Provenance Chain¶

4.6 CDC Analysis¶

4.7 Generate Certification Testbench¶

4.8 Formal Equivalence Sketch¶

4.9 Full Certification Report¶

5. CLI Usage¶

5.1 Generate Compliance Matrix¶

5.2 Verify Stability¶

5.3 Generate Fault Tree¶

6. Space-Qualified Workflow¶

6.1 Complete Space Deployment Pipeline¶

6.2 Radiation Hardening Considerations¶

6.3 Space-Grade Target Profiles¶

7. Performance Characteristics¶

7.1 Evidence Generation Time¶

7.2 Certification Evidence Completeness¶

7.3 Reliability by Process Node¶

8. Test Suite and Verification¶

8.1 Compliance Matrix Test¶

8.2 ODE Stability Test¶

8.3 Fault Tree Generation Test¶

8.4 Reliability Prediction Test¶

8.5 Provenance Chain Reproducibility Test¶

8.6 CDC Analysis Test¶

8.7 E2E Pipeline Test¶

8.8 Troubleshooting¶

References¶

Further Reading¶