Thermal and Packaging Deep Dive

Hook: Module 5's Whole Line

Across 14 chapters: paradigm → memristor → crossbar → architecture → DAC/TDC/TIA → MUX/ECC → compute → noise → power/thermal → IR drop → packaging → generation comparison. This chapter ties them all in one deep dive: Y100 thermal + packaging design.

Then Module 6 (software stack) starts — hardware ready, time for code.

Intuition: Y1 → Y100 Thermal Jump

Generation	TDP	Heat density	Cooling
Y1	3 W / 1 cm²	3 W/cm²	Passive
Y10	30 W / 2 cm²	15 W/cm²	Heat sink
Y100	100 W / 4 cm²	25 W/cm²	Active fan / liquid
Y1000	100 W / 8 cm² (3D)	12.5 W/cm² but 3D-density	Microfluidic

Heat density is below CPUs (~50-100 W/cm²), but 3D stacks make it dense → cooling is critical.

Formalism: Thermal Modeling and Packaging

L1 · Başlangıç

Thermal-resistance network:

Heat path from chip to ambient:

Junction → die: $R_{j-d}$ ~0.5°C/W (Si is highly conductive).
Die → heat spreader: $R_{d-s}$ ~1°C/W (TIM, thermal interface material).
Heat spreader → ambient: $R_{s-a}$ varies (cooling-dependent).

Total: $R_{j-a} = R_{j-d} + R_{d-s} + R_{s-a}$ .

Y1: $R_{j-a} \approx 15$ °C/W (passive). Y100: $R_{j-a} \approx 1$ °C/W (liquid) needed.

Microfluidic cooling:

Thin channels (10-100 µm) etched on top of the chip. Liquid (water or coolant) is pumped through; heat transfers in.

Design:

Channel width: 50 µm.
Flow rate: 1 m/s.
Total flow: 1 mL/s.
$R_{s-a} \approx 0.1$ °C/W → for a 100 W chip, $\Delta T = 10$ °C.

Tuckerman & Pease 1981 is the classic. Reborn for modern 2D AI chips.

L2 · Tam

3D stack thermal problem:

Two dies stacked → the bottom can’t dissipate through the top. Hot spot.

Fix:

Parallel heat path through TSV (Cu conduction).
Optimize TIM between dies.
Multi-layer fluid flow.

Y100 8-layer 3D → each layer dissipates 12.5 W → total 100 W. Top layer normal cooling; lower layers liquid channels.

Vapor chamber:

Alternative: water in a vacuum capsule. High heat transfer via evaporation + condensation. Apple M-series MacBooks use it. Y10/Y100 candidate.

Heat-spreader materials:

Cu (classical): k = 400 W/m·K.
Diamond: k = 2000 W/m·K (5×!). Y1000 experimental.
Graphene: anisotropically high k. Not yet practical.

L3 · Derin

Y100 packaging detail:

Heterogeneous 3D integration:

8 SIDRA die layers (on a CoWoS interposer).
4 HBM3 stacks adjacent.
Photonic die underneath.
All in a 50 mm × 50 mm package.

TSV (Through-Silicon Via): 5-10 µm diameter. Each die ~10K TSVs. High bandwidth.

Failure/manufacture:

3D stacks are hard to test. One failed die → whole stack scrapped. Solution: pre-test every die.

Y100 yield optimistic: 50%. Cost rises.

Sustainability:

SIDRA production:

1 wafer ~$1000-5000.
1 chip ~$100-2000 (yield + test).
E-waste: 3D stacks hard to recycle.

Design strategy: socket-able packaging (Y100 target), good dies refurbished.

Thermal-aware compiler:

The compute engine + Y10+ compiler distribute model loads per the thermal map. Hot spots minimized.

Cryogenic (Y1000):

4 K (liquid helium):

Superconducting metal (R = 0).
Very low thermal noise.
Industry still early.

NIST and IBM Quantum + AI hybrid systems are exploring.

Conclusion:

Thermal and packaging are SIDRA’s “physical shell”. However well Module 5 was designed, packaging + cooling sets the physical ceiling.

Experiment: Y100 Liquid-Cooling Design

Target: Y100 100 W, T_die < 70°C.

Liquid:

Water or 3M Novec 7100.
Flow rate: 0.5 L/min.
Inlet T: 25°C.

Calculation:

$Q = \dot{m} c_p \Delta T$

Water: $c_p = 4180$ J/kg·K, $\rho = 1000$ kg/m³.

$\dot{m} = 0.5$ L/min × 1000 / 60 = 8.3 g/s.

100 W = 8.3e-3 × 4180 × $\Delta T$ → $\Delta T_{water} = 2.9$ °C.

$T_{outlet} = 25 + 2.9 = 27.9$ °C.

Heat exchanger (chip ↔ water): R_th = 0.5°C/W → $T_{die} - T_{water} = 100 \times 0.5 = 50$ °C.

$T_{die} = T_{water} + 50 = 27 + 50 = 77$ °C.

Target 70°C → slightly over. Better heat exchanger or higher flow rate.

R_th = 0.3°C/W (vapor chamber + liquid): $T_{die} = 27 + 30 = 57$ °C. OK!

Pump energy:

Pump ~5 W. Total datacenter power: 100 W chip + 5 W pump + chiller PUE 1.5 → 150 W per chip.

1000 Y100 datacenter: 150 kW. 1 year: 150 × 8760 = 1.3 GWh. CO₂: 1.3 × 0.4 = 520 tons.

Still GPU datacenter (1000 H100 = 700 kW) is 80% more.

Module 5 Closing Quiz

Tests Module 5’s 14 chapters.

1/8SIDRA's solution to the von Neumann bottleneck?

Integrated Lab: Y10 Chip Design Decisions

You’re SIDRA’s design team. Y10 spec planning:

Data (Y1 mature, Y10 target setting):

Y10 target market: edge + datacenter.
TDP budget: 30 W max.
Production volume: 1M chips/year, mini-fab.

Decisions:

(a) Crossbar size: 256² (same as Y1, more crossbars) vs 512² (new). Which?

(b) Cell count: 10B target. 262K per crossbar (512²) → 40K crossbars. Or 65K (256²) → 154K crossbars. Which?

(c) ADC vs TDC: Y1 ADC + Y10 hybrid TDC?

(d) 3D-stack: when to start? 4 layers vs 8 layers?

(e) Training support: last-layer analog backward, or none?

(f) Market price target: $500/chip or$ 2000/chip?

Solutions

(a) 512² crossbar. Bigger model layers fit (BERT-base attention 768 → 1.5 crossbars at 512² vs 6 crossbars at 256²). Throughput rises. IR-drop downside managed via compensation.

(b) 40K × 512² crossbar preferred. Less ADC, less control overhead.

(c) TDC standard. ADC area + power savings. Y1 prototyped it; Y10 productizes.

(d) Start with 4-layer 3D. 8 layers too hard to manufacture. 4 layers production-ready, density 4×.

(e) Last-layer analog backward. Hybrid training: transfer learning, fine-tuning. Full training reserved for Y100.

(f) ** $500/chip.** Edge devices target a wide market. Datacenter has higher margin ($ 2000 possible) but volume less.

Result Y10 spec: 14 nm CMOS + 70 nm cell + 1S1R 3D 4-layer + TDC + 40K crossbars 512² + 10B memristors + 300 TOPS + 30 W + hybrid training + $500.

Tape-out 2028, production 2029.

Module 5 Cheat Sheet

14 chapters in summary:

5.1 Neuromorphic paradigm (CIM).
5.2 Memristor physics.
5.3 Crossbar array.
5.4 YILDIRIM architecture (4-level hierarchy).
5.5 DAC + ISPP programming.
5.6 TDC time-domain readout.
5.7 TIA sense circuit.
5.8 MUX, decoder, ECC.
5.9 Compute engine + DMA.
5.10 Noise models.
5.11 Power + thermal management.
5.12 IR drop.
5.13 Signal chain + packaging.
5.14 Y1/Y10/Y100 evolution.
5.15 Thermal + packaging deep dive (this).

Module 5 message: SIDRA YILDIRIM = an integrated neuromorphic AI chip platform. Hardware + analog circuits + packaging + cooling co-designed.

Vision: From Hardware to Software

Module 5 is hardware. Module 6 is software: how do we program this hardware?

Driver: Linux kernel module, PCIe communication.
Firmware: RISC-V control on SIDRA.
Compiler: PyTorch model → SIDRA assembly.
SDK: developer API.
Simulator: for digital-twin tests.

Module 6 makes SIDRA usable. Hardware alone is not enough — the software stack productizes.

For Türkiye: software is a Turkish strength. SIDRA hardware + Turkish software engineering = practical AI products.

Thermal and Packaging Deep Dive

Prerequisites

What you'll learn here

Hook: Module 5's Whole Line

Intuition: Y1 → Y100 Thermal Jump

Formalism: Thermal Modeling and Packaging

Experiment: Y100 Liquid-Cooling Design

Module 5 Closing Quiz

Integrated Lab: Y10 Chip Design Decisions

Module 5 Cheat Sheet

Vision: From Hardware to Software

Further Reading

Prerequisites

What you'll learn here

🪝 Hook: Module 5's Whole Line

🧭 Intuition: Y1 → Y100 Thermal Jump

📐 Formalism: Thermal Modeling and Packaging

🧪 Experiment: Y100 Liquid-Cooling Design

📝 Module 5 Closing Quiz

🛠️ Integrated Lab: Y10 Chip Design Decisions

🗂️ Module 5 Cheat Sheet

🔮 Vision: From Hardware to Software

📚 Further Reading

Hook: Module 5's Whole Line

Intuition: Y1 → Y100 Thermal Jump

Formalism: Thermal Modeling and Packaging

Experiment: Y100 Liquid-Cooling Design

Module 5 Closing Quiz

Integrated Lab: Y10 Chip Design Decisions

Module 5 Cheat Sheet

Vision: From Hardware to Software

Further Reading