Build the future of AI silicon.
The AI-chip development platform for engineers and researchers — design, process, simulate, and manufacture next-generation AI, ML, and Transformer chips. Ask anything below to start building.
The AI-chip development platform for engineers and researchers — design, process, simulate, and manufacture next-generation AI, ML, and Transformer chips. Ask anything below to start building.
ChipFoundryServices is an AI-chip development platform for semiconductor and AI professionals. Design architectures, model process modules, run simulations, plan manufacturing, and build machine-intelligence knowledge — with an AI copilot (CFSGPT) trained across chip design, fab process, and modern AI. Covering the full stack: Artificial Intelligence, Machine Learning, Deep Learning, Large Language Models, AI agents, and AI Transformer chip architecture.
For design, architecture & verification engineers at Nvidia, AMD, Apple & Intel.
For process, equipment & yield engineers at TSMC, ASML, Applied Materials, Lam & KLA.
For AI, ML & infra engineers at Anthropic, OpenAI, Microsoft, Amazon & Oracle.
RTL to layout, accelerator microarchitecture, and design-flow guidance for AI silicon.
Dataflow, memory hierarchy, and systolic/neuromorphic architectures for ML workloads.
Attention-optimized hardware, tensor engines, and transformer accelerator design.
Etch, deposition, litho, CMP and process integration for advanced nodes.
Fab flow, yield, metrology, and defect reduction powered by machine learning.
Throughput, OEE, preventive maintenance, and MES optimization with AI.
Device, circuit, thermal, and physics-based simulation plus synthetic data.
Run an RIE/ICP trench-profile simulation on the compute pool — depth, undercut, sidewall angle.
Run a CVD/ALD conformal-fill simulation — step coverage, conformality, keyhole voids.
Model LLM serving on an AI accelerator — throughput, latency, KV-cache, roofline bottleneck.
Image a line/space grating through the lens — CD, contrast/NILS, k1, depth of focus, EUV vs 193i.
Chemical-mechanical polishing — Preston removal rate, planarization time constant, step-height clearing, dishing and erosion, and a planarity verdict.
Copper BEOL wiring — size-effect resistivity rise, distributed-line RC signal delay, and Black’s-equation electromigration lifetime, with a wire-health verdict.
Junction-temperature stack — temperature-dependent silicon conductivity, hotspot spreading, and the thermal margin against Tⱼ,max with a throttling verdict.
GAA/FinFET electrostatics — the screening length that sets gate control, subthreshold swing & DIBL, and a velocity-saturated I-V with an Iₒₙ/Iₒ꜀ switch-quality verdict.
Wafer economics — gross-die-per-wafer packing inside the edge-exclusion ring, four defect-limited yield models (Poisson, Murphy, negative-binomial, Seeds), and the cost-per-good-die that prices a GPU.
Bit-cell margins — the butterfly-curve hold & read Static Noise Margin, the cell-ratio read-stability vs pull-up-ratio writeability trade, and the statistical Vth-mismatch Vmin that floors a cache array.
AI-accelerator dataflow — map a Transformer GEMM onto an R×C PE grid for the roofline & ridge point, spatial×temporal PE utilization, the HBM-vs-compute bottleneck, on-chip SRAM tile fit, latency and TOPS/W.
High-Bandwidth Memory roofline — peak vs effective bandwidth from DRAM efficiency (row-buffer hits, read/write turnaround, refresh), page-hit & random latency, and the memory-bound LLM decode token rate that HBM bandwidth gates.
Transformer attention-memory model — per-token Key/Value cache cost from layers, KV heads & head dim, total HBM footprint (weights + cache), capacity fit / OOM, max batch & context, and the memory-bound decode throughput long-context serving gates.
LLM training-step memory model — mixed-precision Adam model states (weights + gradients + optimizer = 16 bytes/param) plus activations, how ZeRO/FSDP sharding and activation checkpointing shrink per-GPU HBM, whether a step fits, and the minimum GPU count a run needs.
Chip power & thermal-budget model — dynamic switching power (activity × C × V² × f) plus interconnect and thermally-coupled leakage, a junction-temperature fixed-point solve, TDP/Tjmax fit, thermal-runaway detection, and the thermal-limited maximum clock the cooling can sustain.
Distributed-training model — split an LLM step across a GPU cluster (num_gpus = TP × PP × DP), roofline the per-GPU compute, then add the tensor-parallel activation all-reduces, pipeline point-to-point + fill/drain bubble, and backward-overlapped data-parallel gradient all-reduce to get step time, MFU, bottleneck, and throughput.
An AI copilot over 13k+ curated entries spanning chips, process, and AI.
Practice greetings, names, audio pronunciation, and a short quiz before college.
Everything below is running now on ChipFoundryServices — interactive demos for the semiconductor and AI / ML / DL / LLM / Agent and auto-programming community. Each application is a real, reduced-order engineering model served from our distributed compute pool, with a matching raw-JSON endpoint you can call from any tool and a machine-readable report for every run.
An AI copilot over 13,000+ curated entries across chip design, process, architecture, and AI. Flagship answers with styled tables, hand-drawn diagrams, and rendered math.
JSON · /api.php?q= ● LIVEReduced-order RIE / ICP trench-profile model — depth, undercut, CD top and bottom, sidewall angle, aspect ratio, and anisotropy, with a full etched-wall profile.
JSON · /edge/simulate ● LIVEConformal step-coverage model — top, sidewall, and bottom thickness, conformality, remaining opening, pinch-off detection, and a plain-language fill-quality verdict.
JSON · /edge/deposit ● LIVELLM-serving roofline — decode tokens/s, time-to-first-token, end-to-end latency, KV-cache size, HBM fit, compute-vs-memory bottleneck, and the efficient batch size.
JSON · /edge/infer ● LIVEAbbe partial-coherence imaging — printed line and space CD, image contrast and NILS, k1 factor, Rayleigh half-pitch, depth of focus, and a resolvability verdict.
JSON · /edge/litho ● LIVEChemical-mechanical polishing — Preston-law blanket removal rate, planarization time constant, step-height clearing, post-polish dishing and erosion, and a planarity verdict.
JSON · /edge/cmp ● LIVECopper BEOL wiring — Fuchs–Sondheimer size-effect resistivity, distributed-line RC signal delay, and Black’s-equation electromigration lifetime, with a wire-health verdict.
JSON · /edge/interconnect ● LIVEChip junction temperature — self-consistent junction-to-ambient resistance stack, temperature-dependent silicon conductivity, and hotspot spreading, with a Tⱼ,max throttling verdict.
JSON · /edge/thermal ● LIVEThe switch under every AI accelerator — electrostatic screening length, subthreshold swing and DIBL, threshold roll-off, and velocity-saturated I-V curves, with an Iₒₙ/Iₒ꜀ switch-quality verdict.
JSON · /edge/transistor ● LIVEWhat a wafer actually produces — gross-die-per-wafer packing inside the edge-exclusion ring, the Poisson / Murphy / negative-binomial / Seeds defect-yield models, and the cost-per-good-die that sets the price of an accelerator die.
JSON · /edge/yield ● LIVEWhether a bit holds — the butterfly-curve hold & read Static Noise Margin, the cell-ratio (read) vs pull-up-ratio (write) tug-of-war, and the statistical Vth-mismatch Vmin that sets the floor voltage of an accelerator's on-die cache.
JSON · /edge/sram ● LIVEWhether the array actually works — map a Transformer GEMM onto an R×C PE grid for the roofline & ridge point, spatial×temporal PE utilization, the HBM-bandwidth-vs-compute bottleneck, on-chip SRAM tile fit, and latency and TOPS/W.
JSON · /edge/systolic ● LIVEWhy inference is memory-bound — model HBM3/HBM3E stacks for peak vs effective bandwidth after DRAM efficiency (row-buffer hits, read/write turnaround, refresh), page-hit & random latency, and the LLM decode token rate bandwidth gates.
JSON · /edge/hbm ● LIVEWhy long-context serving is a memory problem — per-token Key/Value cache cost from layers, KV heads & head dim, total HBM footprint (weights + cache), capacity fit / OOM, max batch & context, grouped-query & KV-quant savings, and the decode throughput footprint gates.
JSON · /edge/kvcache ● LIVEWhy training is far hungrier than inference — mixed-precision Adam model states (weights + gradients + fp32 master + moments = 16 bytes/param) plus activation memory, how ZeRO/FSDP stages 1–3 shard across GPUs, how activation checkpointing makes long sequences fit, per-GPU HBM fit, and the minimum GPU count a run needs.
JSON · /edge/training ● LIVEWhere a processor's watts go — dynamic switching power (activity × C × V² × f), interconnect/wire power, and thermally-coupled leakage that doubles roughly every 15 °C. Solve the junction-temperature fixed point, check TDP and Tjmax, detect thermal runaway, and find the thermal-limited maximum clock — the trade-offs that set an AI accelerator's power envelope.
JSON · /edge/power ● LIVEHow an LLM training step splits across a GPU cluster — num_gpus = TP × PP × DP. Roofline the per-GPU compute, then layer the tensor-parallel activation all-reduces, the pipeline point-to-point sends and fill/drain bubble, and the backward-overlapped data-parallel gradient all-reduce. Get step time, strong-scaling efficiency / MFU, the dominant bottleneck, and token throughput.
JSON · /edge/parallelism ● LIVEA seven-node distributed compute fabric with automatic failover. Every simulation above runs here; the dashboard reports live per-node test results across the pool.
Dashboard · /edge/ ● LIVEAn installable progressive web app for chip and AI engineers — chat and collaboration that runs full-screen from your phone's Home Screen.
PWA · /app/ ● LIVEAn interactive English-learning module — greetings and names, tap-to-play audio pronunciation, vocabulary flashcards, a dialogue, and an auto-graded quiz.
Web + App · /english/| Application | Method & endpoint | Example request | Returns (JSON) |
|---|---|---|---|
| CFSGPT Knowledge | GET /api.php?q={query} |
/api.php?q=what+is+euv |
{ response } — a full Markdown answer (prose, tables, diagrams, math). |
| Plasma Etch | GET /edge/simulate?{params} |
/edge/simulate?etch_time_s=120&source_power_w=800 |
model, inputs, outputs (depth_nm, cd_top_nm, sidewall_angle_deg, aspect_ratio, anisotropy), profile wall polyline. |
| Deposition | GET /edge/deposit?{params} |
/edge/deposit?trench_width_nm=100&sticking_coeff=0.1 |
outputs (step_coverage_pct, conformality_pct, pinch_off, fill_quality), profile fill polyline. |
| Inference | GET /edge/infer?{params} |
/edge/infer?params_b=70&batch=64 |
outputs (decode_tokens_s, ttft_ms, kv_cache_gb, fits_in_hbm, efficient_batch), profile.batch_curve. |
| Lithography | GET /edge/litho?{params} |
/edge/litho?wavelength_nm=13.5&na=0.55 |
outputs (printed_line_cd_nm, nils, k1_factor, depth_of_focus_nm, resolvable), profile.aerial intensity curve. |
| CMP | GET /edge/cmp?{params} |
/edge/cmp?down_pressure_kpa=20&pattern_density=0.5 |
outputs (blanket_removal_rate_nm_min, time_to_clear_s, planarization_efficiency_pct, dishing_nm, erosion_nm, verdict), profile.step_curve & profile.surface. |
| Interconnect | GET /edge/interconnect?{params} |
/edge/interconnect?wire_width_nm=20¤t_density_ma_cm2=8 |
outputs (effective_resistivity_uohm_cm, resistivity_ratio, rc_delay_ps, em_mttf_years, em_margin, verdict), profile.resistivity_curve & profile.geometry. |
| Thermal | GET /edge/thermal?{params} |
/edge/thermal?die_power_w=700&hotspot_power_frac=0.35 |
outputs (thermal_resistance_ja_c_w, silicon_conductivity_w_mk, junction_temp_uniform_c, junction_temp_hotspot_c, hotspot_delta_c, thermal_margin_c, verdict), profile.stack & profile.lateral. |
| Transistor | GET /edge/transistor?{params} |
/edge/transistor?gate_length_nm=16&gate_config=4 |
outputs (natural_length_nm, subthreshold_swing_mv_dec, dibl_mv_v, vt_eff_v, ion_ua_um, ioff_na_um, ion_ioff_ratio, transconductance_us_um, intrinsic_delay_ps, verdict), profile.transfer & profile.output. |
| Yield | GET /edge/yield?{params} |
/edge/yield?die_width_mm=26&die_height_mm=33&defect_density_d0=0.12 |
outputs (die_area_mm2, gross_die_per_wafer, defects_per_die, yield_percent, good_die_per_wafer, packing_efficiency_percent, cost_per_good_die_usd, yield_poisson_percent, yield_murphy_percent, yield_negbinom_percent, yield_seeds_percent, verdict), profile.yield_curve & profile.wafer. |
| SRAM | POST /edge/sram |
/edge/sram?vdd=0.8&cell_ratio=1.5&pull_up_ratio=1.0&array_mbit=32 |
outputs (snm_hold_mv, snm_read_mv, write_margin_mv, trip_point_v, snm_read_worstcase_mv, sigma_target, snm_read_sigma_mv, vmin_v, read_snm_ratio_percent, writable, verdict), profile.vtc_hold, profile.vtc_read, profile.snm_vs_cell_ratio & profile.wwm_vs_pullup_ratio. |
| Systolic Array | POST /edge/systolic |
/edge/systolic?rows=128&cols=128&gemm_m=2048&gemm_k=8192&gemm_n=8192 |
outputs (peak_tops, achieved_tops, pe_utilization_percent, map_efficiency_percent, temporal_efficiency_percent, arithmetic_intensity, ridge_point_flop_byte, bottleneck, latency_ms, hbm_traffic_gb, tile_working_set_mb, sram_fits, tops_per_watt, power_w, verdict), profile.roofline & profile.util_vs_dim. |
| HBM Bandwidth | POST /edge/hbm |
/edge/hbm?num_stacks=6&data_rate_gbps=6.4&model_params_b=70&batch=1 |
outputs (peak_bandwidth_gbs, effective_bandwidth_gbs, dram_efficiency_percent, row_efficiency_percent, rw_efficiency_percent, refresh_efficiency_percent, channels, per_channel_gbs, page_hit_latency_ns, random_latency_ns, model_size_gb, arithmetic_intensity, time_per_token_ms, decode_tokens_per_s, power_w, gbs_per_watt, verdict), profile.bw_vs_access & profile.bw_vs_hit. |
| KV Cache / Attention | POST /edge/kvcache |
/edge/kvcache?model_params_b=8&num_layers=32&context_length=8192&batch=32 |
outputs (kv_bytes_per_token, kv_kb_per_token, kv_per_sequence_gb, kv_total_gb, weight_gb, footprint_gb, capacity_gb, utilization_percent, kv_fraction_percent, free_for_kv_gb, fits, max_batch_at_context, max_context_at_batch, bytes_per_step_gb, time_per_step_ms, decode_tokens_per_s, bottleneck, verdict), profile.footprint_vs_ctx & profile.tps_vs_ctx. |
| LLM Training Memory | POST /edge/training |
/edge/training?model_params_b=8&num_gpus=8&seq_length=4096&zero_stage=3 |
outputs (model_states_unsharded_gb, model_states_per_gpu_gb, activations_per_gpu_gb, per_gpu_total_gb, gpu_capacity_gb, utilization_percent, fits, bytes_per_param, weights_per_gpu_gb, grads_per_gpu_gb, optimizer_per_gpu_gb, min_gpus_to_fit, aggregate_cluster_gb, global_batch, zero_stage, checkpointing, verdict), profile.mem_vs_gpus & profile.mem_vs_seq. |
POST /edge/power |
/edge/power?transistor_count_b=20&core_voltage_v=0.85&clock_ghz=2.0&tdp_w=700 |
outputs (dynamic_power_w, logic_dynamic_w, interconnect_power_w, leakage_power_w, total_power_w, junction_temp_c, tjmax_c, tdp_w, power_density_w_mm2, leakage_fraction_percent, within_tdp, within_tjmax, thermal_runaway, max_clock_ghz, energy_per_cycle_nj, gops_per_w, verdict), profile.power_vs_freq & profile.power_vs_volt. |
|
POST /edge/parallelism |
/edge/parallelism?model_params_b=70&num_gpus=64&tensor_parallel=8&pipeline_parallel=4 |
outputs (data_parallel, used_gpus, idle_gpus, valid_layout, tokens_per_step, compute_time_ms, tp_comm_ms, pp_comm_ms, pipeline_bubble_ms, dp_allreduce_ms, dp_allreduce_raw_ms, step_time_ms, comm_fraction_percent, bubble_fraction_percent, scaling_efficiency_percent, mfu_percent, achieved_tflops_per_gpu, throughput_tokens_s, per_gpu_tokens_s, bottleneck, verdict), profile.eff_vs_gpus & profile.eff_vs_bw. |
Every simulator parameter is an optional query argument — omit any and a sensible default is used. Responses are plain JSON with permissive access, so you can pipe them straight into a notebook, an agent tool, or an auto-programming pipeline.
Each simulation returns a structured JSON report — the model name, the exact inputs used, every computed output metric, and a numeric profile curve — ready to log, diff, or chart.
The /edge/ dashboard reports live execution results per node across the seven-machine pool, so you can see which nodes ran your workload and confirm failover health.
Knowledge queries return a formatted report — narrative prose with bold lead-ins, styled comparison tables, hand-authored SVG diagrams, and rendered KaTeX math.
Beyond the live tools, ChipFoundryServices ships a hand-authored knowledge base of flagship answers — each one renders with styled comparison tables, real engineering diagrams, and typeset math. Tap any keyword to ask CFSGPT and see the full answer inline. These are the curated flagships across the AI-chip stack; the wider base holds 13,000+ entries.
Each keyword resolves to a full flagship answer — narrative prose with bold lead-ins, styled comparison tables, hand-authored SVG diagrams, and rendered KaTeX math. Every answer is also callable as raw JSON at /api.php?q={keyword}.
Put CFSGPT to work — your AI copilot for chip design, architecture, process, manufacturing, and machine intelligence. Ask a question and start building.
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