How Multi-code Encoders Push the Limits of Semiconductor Integration in 6G Modems

Read Below:

• Why Multi-code & Multi-User Support Demand Massive Integration

• Semiconductor Node Advances: Why 3 nm / 2 nm Matter

• Putting It All Together: A Hypothetical 6G Multi-code Encoder Block

Introduction

In 6G wireless systems, one of the central challenges is supporting simultaneous users (multi-user access) with extremely high per-user and aggregate data rates (e.g. hundreds of Gbps or even Tbps). The “multi-code” concept — mapping multiple parallel codewords (or multiple spatial streams) into a composite encoded stream — helps in multiplexing, diversity, and parallelism. But implementing multi-code encoding at these scales places intense pressure on the modem baseband’s architecture: you need massive parallelism, extremely high on-chip memory and data bandwidth, low latency, and careful power/area trade-offs. Only leading-edge semiconductor nodes (3 nm, 2 nm) and clever microarchitectural techniques can hope to meet these demands.

Why Multi-code & Multi-User Support Demand Massive Integration

Multi-user scaling & throughput targets

In 6G, we expect systems to support tens to hundreds of simultaneous users per cell, each requiring extremely high data rates. Suppose one user’s physical layer payload is encoded across multiple (say (M)) component codes (e.g. multi-code spreading, parallel coding, or multiplexed polar/LDPC codes). The total aggregate bit rate through the baseband may reach 500 Gbps to 1 Tbps or more (depending on bandwidth, antenna count, modulation, etc.).

To support that, the multi-code encoder must:

• Accept (M) parallel streams of bits (e.g. user streams, layers, or sub-streams).

• Encode them in parallel (or interleaved) to produce a composite bitstream, possibly with interleaving, rate matching, puncturing, or multiplexing.

• Manage internal data movement (buffers, memory reads/writes) at very high rates.

• Interface with the mapper / modulator / precoder with minimal latency.

Thus, the baseband’s multi-code encoder becomes a performance-critical, massively parallel block.

Parallel encoding blocks & pipeline depth

To keep throughput high, one cannot serialize all operations. Instead, one must replicate parallel encoder blocks. For example, if each encoder instance can handle 50 Gbps, you might need 10 parallel blocks to reach 500 Gbps. But that replication increases area, power, and interconnect complexity. Pipeline depth also matters: deeper pipelines can boost clock frequency, but increase latency and complexity of synchronization across streams.

Hence, the design typically entails a grid or array of encoding cores, each handling a slice of the traffic, plus crossbar interconnect (for aligning, merging, or reordering). The challenge is that these cores must be tightly coordinated with memory and interconnect fabric; otherwise, throughput bottlenecks arise.

Semiconductor Node Advances: Why 3 nm / 2 nm Matter

Benefit: transistor density, speed, and power efficiency

Moving from 5 nm → 3 nm → 2 nm lets designers pack more logic per unit area, reduce parasitic capacitances, and shorten transistor switching delays. That allows:

• More parallel encoder cores (higher logic density).

• Higher clock frequencies (or lower pipeline stage delay) for the same logic depth.

• Lower energy per bit (if leakage and switching power are managed correctly).

But there's a catch: as feature sizes shrink, power density (power per unit area) becomes a severe constraint. You can’t scale power indefinitely because of thermal limits and reliability concerns (e.g. electromigration, thermal gradients, IR drop). So you must carefully budget power per core and per unit area.

Power density & thermal constraints

At 3 nm / 2 nm, a chip may already operate close to thermal limits (e.g. (> 150) W/cm² hotspots). If your multi-code encoder block cluster dissipates too much power, you get local hotspots, thermal throttling, or yield issues. Therefore:

• You may need to distribute the encoding blocks across the die, interleave with thermal paths and heatsinks.

• Use dynamic voltage and frequency scaling (DVFS) within the encoder fabrics.

• Employ power gating of idle encoding cores (if the user load fluctuates).

Thus, the semiconductor scaling helps if the design keeps within permissible power densities.

On-chip memory bandwidth & data movement

One of the most stringent constraints is memory bandwidth. The multi-code encoder is not just combinational logic; it often needs:

• Input buffering (to align or interleave user streams).

• Scratch memory for internal metrics, pointers, or state (especially with codes like LDPC, polar, TURBO, etc.).

• Output staging buffers before handing off to mapping/precoding.

If each encoding core needs, say, (b) bits per cycle, and you have (N) cores, the memory (or SRAM) interface must deliver N x b  (Nxb) bits per cycle. At high frequencies (e.g. 2 GHz clock), even moderate (N) and (b) create multi-Tbps demands on internal SRAM interconnect.

To meet that, integration at 3 nm / 2 nm allows embedding large, high-bandwidth embedded SRAM blocks close to the logic. The wiring lengths between logic and SRAM are shortened, helping with latency and energy per bit. Also, advanced back-end metallization (e.g. fine pitch Mx/Mx+1 layers) helps reduce interconnect delay.

However, the ratio of logic to memory becomes critical: dedicate too much area to SRAM and you lose encoding core density; too little SRAM and you bottleneck on bandwidth.

Trade-Offs: Area vs Latency vs Throughput

Designers must juggle three axes:

1. Area (or resource usage): More cores, more SRAM, more interconnect, crossbars, registers.

2. Latency: Pipeline depth, buffer replay, multi-stage processing, context switches.

3. Throughput: Aggregate bit rate per unit time.

Some trade-off principles:

• Shrinking area by reducing core replication or memory width may force cores to operate at higher frequency, narrower data paths, or more multiplexing — increasing latency.

• Reducing latency by flattening pipelines or reducing buffering sometimes means sacrificing maximum clock rate or requiring more parallelism (hence more area).

• Maximizing throughput tends to push both area and power upward; if you aggressively replicate cores, you may exceed power/thermal budgets or violate routing/signal integrity.

Hence, the architecture often uses a mixed strategy: a moderate number of cores operating at high frequency with deep pipelining, plus smart interconnect and memory partitioning. Also, designers may adopt folding or time-multiplexing when user load is low, trading off dynamic latency for energy efficiency.

Another key technique is clock domain partitioning: you might run the encoding cores at a higher frequency domain, but buffer transfers to slower domains with FIFOs to mitigate latency boundaries.

Putting It All Together: A Hypothetical 6G Multi-code Encoder Block

Here’s a conceptual sketch of how a 6G multi-code encoder might be architected (assuming a 3 nm or 2 nm logic/SRAM process):

• Encoder core array: e.g. 16 parallel cores, each processing 32 Gb/s (total: 512 Gb/s).

• Interleaver / cross-shuffle fabric: a network that takes partial outputs and reorders or aligns bits (e.g. for rate matching) — implemented as a lightweight crossbar or switching network.

• Buffering memory banks: e.g. 8 banks of SRAM (e.g. 64 kB each) interleaved physically among the cores to share read/write load.

• Input input staging FIFOs: to align streams to cycle boundaries.

• Output stage pipeline: to hand off encoded codewords to mapper/precoder in real time.

In such a design, the internal SRAM memory bandwidth could exceed 1 Tb/s, depending on frequency and bit widths. The layout must minimize wiring distance to avoid large RC delays, so cores and SRAM must be tightly co-located. Power density must stay within thermal limits, so cores may run at different voltages or be power-gated dynamically when load is low. The pipeline depth must be balanced to maintain low coding latency (important for HARQ, retransmission scheduling, etc.).

When such a block is integrated within a full 6G modem SoC, it must share floorplan, power rails, cooling with other high-throughput blocks (e.g. FFT, precoder, channel decoder, modulation) — all of which further strain the integration.

Conclusion

Multi-code encoders in 6G modems are a microcosm of the broader challenges of next-generation semiconductor integration. The relentless push for multi-user support and ultra-high throughput drives the need for massive parallelism, enormous on-chip memory bandwidth, and architectural finesse to balance area, latency, and power. Scaling to 3 nm and eventually 2 nm nodes is not just a luxury, it's becoming a necessity to embed the density, speed, and energy efficiency to support such clusters of encoding blocks.