Cleanroom & Biotech Contamination Control

A.1 Cleanroom & Biotech Contamination Control

High-grade semiconductor fabs, biopharma facilities, and advanced manufacturing lines rely on strict control of particulate contamination, pressure cascades, and controlled access. Today, contamination control is typically enforced by:

• low-level alarms on individual particle counters or pressure sensors,
• procedural training for operators (gowning, airlock sequencing), and
• offline root-cause analysis when excursions or batch failures occur.

What is often missing is a spatially and temporally coherent language for:

• describing pressure cascades and contamination pathways across rooms;
• identifying recurring patterns of misuse (doors propped, airlocks bypassed);
• correlating subtle sensor deviations with later yield hits.

Spaxiom can act as a cleanroom contamination cortex: fusing particle counts, pressure differentials, door states, and occupancy into structured INTENT events that agents and engineers can reason about.

Sensors and deployment

Typical cleanroom-relevant sensors include:

• Particle counters p_z(t) per zone z (e.g., counts of ≥0.3μm particles per cubic meter);
• Pressure differential sensors ΔP_z,u(t) between adjacent zones z and upstream reference u;
• Door interlocks and access logs d_z(t) (door open/closed, authorized/unauthorized badge);
• Environmental sensors T_z(t) and H_z(t) (temperature, humidity);
• Optional occupancy sensors o_z(t) (PIR, ceiling ToF, or in some zones, floor tiles).

Spaxiom represents each zone as a Zone with associated sensor objects and topological relationships (e.g., which rooms feed which in the pressure cascade).

INTENT events and contamination indices

From raw signals, we define higher-level INTENT events, such as:

• PressureBreach: pressure differential below spec across a boundary;
• ParticleExcursion: temporary spike of particulate counts above class limit;
• AirlockViolation: door sequencing broken (both doors open, or bypass of required dwell);
• HighRiskMovement: occupancy trajectory that crosses from dirtier to cleaner areas under non-compliant conditions.

Mathematically, for a given zone z and monitoring window [t₀, t₁] we can define:

Pressure breach indicator. Let ΔP_z,u(t) be the measured pressure differential between z and upstream zone u (e.g., corridor or anteroom). For a minimum acceptable differential ΔP_min > 0, define

The total number of breach-seconds in the window is:

Particle excursion integral. Let p_z(t) be the particle count for zone z, with a threshold p^max_z that defines acceptable class performance. Define the (non-negative) excursion:

Airlock violation count. Let V_a be the count of airlock a violations in the window, derived from door state sequences and configured policies (e.g., both doors open simultaneously, insufficient purge dwell).

Composite contamination risk index. We can define a simple contamination risk index (CRI) for zone z over [t₀, t₁] as:

where α, β, γ are tunable weights reflecting domain expertise, and 𝒜(z) is the set of airlocks affecting z. This can be normalized by window length or baseline values to obtain a dimensionless score in [0, 1].

Spaxiom-style implementation sketch

A sketch of how a Spaxiom-based contamination monitor might look in Python-embedded DSL form:

from spaxiom import Zone, Condition, Quantity
from spaxiom.temporal import within
from spaxiom.logic import on

class CleanroomZone:
    def __init__(self, name, particle_sensor, dp_sensors, airlocks):
        self.zone = Zone.named(name)
        self.particle_sensor = particle_sensor  # e.g. counts / m^3
        self.dp_sensors = dp_sensors            # dict: upstream_zone -> sensor
        self.airlocks = airlocks                # list of Airlock objects

        # Configurable thresholds
        self.max_particles = 3500        # domain-specific
        self.min_dp = Quantity(5.0, "Pa")

    def pressure_breach_seconds(self, window_s: float) -> float:
        total = 0.0
        for upstream, sensor in self.dp_sensors.items():
            # integrate indicator over the window
            series = sensor.history(window_s=window_s)
            for dt, value in series:  # dt in seconds, value in Pa
                if value < self.min_dp:
                    total += dt
        return total

    def particle_excursion(self, window_s: float) -> float:
        # approximate integral of (p - p_max)+ over the window
        total = 0.0
        series = self.particle_sensor.history(window_s=window_s)
        for dt, value in series:
            excess = max(0.0, value - self.max_particles)
            total += excess * dt
        return total

    def airlock_violations(self, window_s: float) -> int:
        return sum(a.violation_count(window_s=window_s)
                   for a in self.airlocks)

    def contamination_risk_index(self, window_s: float) -> float:
        B = self.pressure_breach_seconds(window_s)
        E = self.particle_excursion(window_s)
        V = self.airlock_violations(window_s)

        alpha, beta, gamma = 1e-3, 1e-6, 1.0  # example scaling
        score = alpha * B + beta * E + gamma * V
        # Optionally squash to [0,1] via logistic
        return 1.0 - (1.0 / (1.0 + score))

# Example zone wiring
zone_a = CleanroomZone(
    name="ISO7_bio_room_3",
    particle_sensor=particles_z3,
    dp_sensors={"antechamber": dp_z3_ante, "corridor": dp_z3_corr},
    airlocks=[airlock_3A, airlock_3B],
)

# Condition that fires when CRI exceeds a threshold in the last hour
high_risk = Condition(
    lambda: zone_a.contamination_risk_index(window_s=3600) > 0.7
)

@on(within(3600, high_risk))
def contamination_agent():
    snapshot = {
        "zone": zone_a.zone.name,
        "CRI": zone_a.contamination_risk_index(window_s=3600),
        "breach_seconds": zone_a.pressure_breach_seconds(3600),
        "particle_excursion": zone_a.particle_excursion(3600),
        "airlock_violations": zone_a.airlock_violations(3600),
    }
    # Hand off to LLM agent or workflow system:
    # e.g., propose root-cause checks, quarantine, or extra cleaning.
    recommend_actions(snapshot)

Here the Spaxiom layer:

• abstracts away sensor-specific histories and units;
• defines windowed functions (history(window_s=...)) for time integration;
• exposes a simple scalar contamination_risk_index for agents, instead of raw time series.