Case Study · Wearables · Thermal Architecture
When 1–2W decides wearability
In smart glasses, heat is the primary constraint. The device is worn on the face with minimal surface area and almost entirely passive heat rejection. Local hotspots directly impact comfort, safety margins, and signal quality.
The problem: why does it heat up?
The imaging pipeline (sensor → ISP/AI → memory → radios) generates heat that concentrates in the temples and nose‑bridge structures. As temperature rises, temperature‑dependent sensor artifacts (such as dark‑signal components) and noise can increase, degrading SNR especially in low light. This can create a loop: more processing → more heat → more noise → more processing.
The IP‑ST approach: Thermal Insight + Evidence
- 1
Insight Engine: Build a thermal resistance network model to predict hotspots and compare alternatives (placement, load split, materials). This modeling approach is established in smart‑glasses thermal analysis and supports actionable countermeasures.
- 2
Secure Vault: Store model parameters, measurement data, and design "recipes" in a protected environment so the invention stays defensible.
- 3
Institutional Data Room: Publish design rationale and evidence in a partner‑ready documentation package (DD‑friendly).
Concrete levers that reduce hotspots
Heat‑source split: Place high‑dissipation blocks across both temples to reduce local peaks (load distribution is a known lever in smart‑glasses thermal countermeasures).
Material combination: Use high‑conductivity paths to spread heat and low‑conductivity interfaces to reduce skin‑contact temperature (core principle in wearable thermal management).
Asynchronous transfer: Reduce peak‑driven thermal stress in sensors and interconnects by smoothing burst behavior (architecture principle).
FAQ
Why is heat the biggest limit in smart glasses?
Because small size + skin contact + passive cooling makes hotspots immediately noticeable, while higher temperature also affects noise and battery runtime.
How does IP‑ST reduce heat?
By modeling heat paths, reshaping load to reduce peak‑driven losses, and using event‑driven "detect‑and‑wake" so heavy compute runs only when it adds value.
Ready to go deeper?