更低 幻觉 曝光
严重 engineering results are parsed, validated and 已关联 to evidence outside the AI model.
硅 工程 命令 居中
A 受治理的 semiconductor intelligence architecture that routes each engineering task to the safest, most repeatable and most compute-efficient processing layer.
确定性 where possible. AI only where necessary. 经人工审批 where critical.
工程-assisted, human-controlled. 最终审批仍由合格工程师负责。
严重 engineering results are parsed, validated and 已关联 to evidence outside the AI model.
设计 revisions, PDK 版本s, tool environments, scripts, seeds and evidence remain traceable.
结构d tasks use parsers, rules, cached results and detached services instead of unnecessary large-model execution.
机密 PDK、IP 和半导体项目数据可保留在客户控制的基础设施内。
保护专有 IC 数据、PDK 信息、凭据和工程上下文。
分离需求、DRC、LVS、时序、覆盖率、PVT、Monte Carlo 和豁免。
使用针对工件的工程解析器,而非通用模型。
检查 values, units, versions, completeness and specification 限制s deterministically.
根据风险与复杂度选择规则、数据库、本地模型或高级推理。
仅将 AI 用于解释、比较、综合和工程说明。
比较 AI-assisted findings against independent evidence and validators.
将架构、豁免、验证和流片权限保留给工程师。
AINNA does not replace 节奏, Synopsys, Siemens EDA, Ansys, COMSOL or other semiconductor engineering 工具. It connects requirements, workflows, results, decisions and engineering evidence across the IC development lifecycle.
6 inputs → silicon-ready artifacts
Low-计算 半导体 智能
半导体 engineering cannot rely on a single probabilistic model. NeuralOps separates deterministic processing, specialised parsing, AI reasoning and human authority into controlled 层.
计时 限制s · unit checks · PVT 完整性
要求s · validated baselines · prior evidence
DRC/LVS 解析 · 清单 · 校验和 · 统计
轻量分类 · 元数据 · 文档标记
复杂比较 · 歧义 · 综合 · 说明
半导体-Specific 控制 Map
选择领域以查看工程工作流中使用的受限 NeuralOps 控制。
要求 extraction
要求-ID validation
歧义检测
设计-block mapping
测试映射
变更影响分析
缺失-evidence detection
模拟 manifest
PVT 解析
Monte Carlo 聚合
单元验证
规格余量检查
布局后比较
证据 linking
Lint 报告解析
CDC 与 RDC 解析
综合结果导入
计时 extraction
回归 tracking
覆盖范围 aggregation
ECO 可追溯性
版图修订跟踪
拥堵-result parsing
DRC 解析
LVS 解析
PEX 结果关联
IR-drop 状态
Electromigration 状态
签核证据完整性
测试计划映射
回归 segmentation
故障聚类
覆盖范围 closure
豁免验证
未复现故障检测
证据-bound summaries
测试数据导入
晶圆图生成
分箱良率分析
测试机相关性
参数分布
模拟-to-silicon comparison
故障-analysis 可追溯性
ESG-Aware 硅 运营
模拟 logs, regressions, verification data, DRC/LVS output, timing reports, wafer-test records, yield data, characterisation results and engineering documents are often structured. They should not automatically be sent to a large AI model.
NeuralOps is 设计ed to reduce unnecessary model execution through 智能路由, 分离式处理 and specialised parsers.
NeuralOps is 设计ed to reduce unnecessary compute consumption by routing work to the lightest validated processing layer capable of completing the task.
碳声明就绪
估算结果绝不能自动呈现为已测量或经独立验证的结果。
NeuralOps is 设计ed to reduce unnecessary model execution through 智能路由, 分离式处理 and specialised parsers.
已发布的架构声明来源: AINNA NeuralOps architecture specification · Last reviewed: 2026-07-29基于配置的工作负载、硬件、路由和电网假设的估算减排量。
估算 预测 - Not a Certified 碳 审计来源: AINNA 碳足迹 模拟器 configuration · Last reviewed: 2026-07-29打开 碳足迹 模拟器在此证据级别通过其治理和审批要求之前,发布将被阻止。
不可用于发布来源: 生产 telemetry - not yet connected · Last reviewed: 2026-07-29在此证据级别通过其治理和审批要求之前,发布将被阻止。
不可用于发布来源: 独立 assurance - not yet available · Last reviewed: 2026-07-29运行中 证据 Schema
运行中 metadata is stored separately from proprietary 设计 artefacts. ESG 测量ment does not require collecting schematics, RTL or confidential report content.
event_idproject_idcustomer_environmenttimestamp_starttimestamp_endtask_typeengineering_domainrisk_levelprocessing_routeparser_nameparser_versionmodel_namemodel_sizemodel_locationinput_tokensoutput_tokenscached_tokensgpu_runtime_secondscpu_runtime_secondsmemory_usageestimated_energy_kwhmeasured_energy_kwhenergy_sourcemeter_idmeasurement_sourceevidence_checksumdata_transfer_mbcache_hitdetached_system_usedadvanced_model_usedvalidation_statusresult_statushardware_typehardware_regiondata_centre_puegrid_emission_factorcalculation_methodmethodology_versionexcluded_workloadsconfidence_rangeuncertainty_range半导体 ESG and 计算 仪表盘
模式l the 架构
配置ure workload, hardware, routing, PUE and grid assumptions before making an 估算预测.
碳指标免责声明: 碳 and energy figures shown on this website may be estimates based on configured workload, hardware, PUE and grid-emission assumptions. They are not certified carbon-audit results unless explicitly identified as independently 已验证. 审核 assumptions in the 碳足迹 模拟器.
工程 Friction
运行中 problems that slow 设计 closure, weaken evidence and make knowledge difficult to reuse.
规格、脚本、仿真设置和依据分散各处。
PDKs, 工具, models, scripts, seeds and environments drift.
要求s are not consistently connected to blocks, tests and evidence.
故障、覆盖缺口、豁免和缺陷需要人工关联。
状态 reports are manually assembled and quickly become stale.
已解决 issues and 设计 rationale are not retained as reusable knowledge.
IC 开发ment Lifecycle
从规格出发,沿一条受控线索贯穿设计、仿真、验证和人工签核。
输入-已转介 噪音 Density ≤ 12 nV/√Hz
经营 色温: -40°C to 125°C
验证 必需信号 intent and interface assumptions remain 已关联 to the requirement baseline.
要求 coverage means requirements with at least one 已关联 verification test and evidence.
控制led 工程 系统
模块 connect engineering context without replacing qualified engineers or licensed EDA 系统s.
独立式 · 分段ed · 证据-Bound
半导体 engineering requires repeatability, 可追溯性 and evidence. AINNA NeuralOps separates deterministic engineering processes from probabilistic AI reasoning so that an AI-generated response cannot silently become a 设计, verification or tape-out decision.
仅在确实需要高级智能时才使用高级 AI。
Most semiconductor workloads do not require a large language model. 结构d and repetitive work belongs in controlled processing 层.
高级模型s are reserved for work where interpretation and synthesis add genuine engineering value.
This boundary is 设计ed to reduce unnecessary 模型调用, token usage, compute demand, latency, cost, power consumption, hallucination exposure and uncontrolled data movement.
The 96% demonstration share means advanced reasoning models are by通过ed; it includes deterministic, cached and customer-controlled local processing and does not claim that every by通过ed task is deterministic.
独立 工程 服务
分离式系统 are engineering services that operate independently from the language model. They execute controlled, repeatable and auditable tasks without depending on AI-generated reasoning.
AI模型不负责验证自身输出。先分解再推理
A request such as “审核 this post-layout verification package and determine tape-out readiness” is never sent as one uncontrolled prompt. It becomes bounded tasks with defined inputs, output schemas, allowed 工具, risk, classification, validation, evidence and human 关卡s.
文件 validation, numeric calculation, checksum, lookup
不允许或不必要使用 AI分类, metadata, formatting, basic summary
解析器 or small local model preferred歧义、故障聚类、跨报告比较
证据-grounded model permitted豁免、签核、安全或可靠性分析
独立验证 and human approval mandatory流片、PDK 更改、生产发布、破坏性操作
自动nomous execution prohibited已定义输入正常ised timing metrics + REQ-TIM baseline
输出 schemarequirement_id · value · unit · 限制 · 状态
允许ed 工具规则引擎 + 已批准限制数据库
风险 / data classL0 确定性 · 机密 IC
验证 / evidence单元检查 + 基线校验和
自动nomy / approval比较 only · engineer approves disposition
默认不信任
工程 data, user instructions, embedded text, external content and 系统 instructions remain separate. 说明s inside uploaded files can never override security, validation or approval rules.
PASS 原始与净化后的校验和、转换及策略决策均写入审计记录。
BLOCK / QUARANTINE 无效、恶意或与策略冲突的内容无法进入处理区域。
PRIVATE ROUTE 敏感内容保留在已批准的私有环境中,标识符最小化并强制执行访问权限。
工件特定提取
A single parser creates a single point of 失败ure. Separate parsers handle each engineering artefact and emit defined schemas before outputs can be accepted.
UNRESOLVED Both outputs preserved. Dependent high-risk actions 已停止. 引擎er review required.
解析器 disagreement is an engineering signal, not an inconvenience to be hidden.
独立 检查s
Schema, units, ranges, IDs, PVT 完整性, mathematics, statistics, versions, duplicate detection, evidence completeness, approved 限制s and policy are checked independently.
“All PVT 角s 通过ed.”
流畅的解释不是工程证据。
计算感知编排
Every task is classified by type, complexity, format, confidentiality, risk, required accuracy, latency, reproducibility, compute and energy impact.
Bounded 输出 状态s
准确度 prompting alone is insufficient. 已批准 retrieval, citations, schemas, numerical checks, parser comparison, confidence 阈值s, contradictions, no-answer states, restricted permissions and immutable audit context reduce exposure.
来源-Level 可追溯性
来源 file / locationSTA_报告_R42.txt · line 1842
检查sum7F3A…
解析器STA 文字 解析器 v2.4
Tool / PDKPrimeTime 2026.03 · PDK R19.2
设计 revisionR42
WNS-0.083 ns
限制≥ 0 ns
角SS / 0.81 V / 125°C
验证确定性 parser confirmed
置信度已验证 extraction
审计 contextRUN-1142 · 2026-07-29 03:42 UTC
限制豁免状态待定
可问责签署
AINNA supports engineering judgment. It does not replace engineering accountability.
ESG 感知计算
半导体 innovation should not depend on sending every task to a large model. 独立式 services, local models, caching, task segmentation and 智能路由 reduce unnecessary AI execution.
Public figures use a 10,000-task illustrative baseline, classify deterministic and cached handling before model routing, and do not assume a production model, hardware platform, grid carbon factor or 测量d period. 实际 telemetry must record model type, hardware, duration, energy source, cache policy, data transfer and confidence range.
ESG metrics must be calculated from actual deployment telemetry. Public website values are demonstration data unless explicitly supported by 测量d operational records.
Low-电源 本地 执行
本地 execution can reduce external transfer and latency while improving control over PDK, IP and compute usage.
外部 routing requires explicit customer approval, appropriate sanitisation, policy compliance, audit logging and an approved destination.
架构 运营
Run 上下文 完整性
可重复性 requires more than storing the final simulation report. A reproducible run must preserve the complete 设计, tool, model, script, configuration and computing context.
仅视觉演示。不执行 EDA 仿真。
验证 智能
交互式演示nstration data connects requirement intent to technical evidence and 失败ure context.
Top 4 regression rows account for 664 失败ures; 259 remaining 失败ures are aggre关卡d separately.
| 特征 | 测试 | 首次 / 末次 | 疑似来源 | 所有者 | 状态 |
|---|---|---|---|---|---|
| SIG-CLK-07 | 312 | 02:11 / 06:42 | 环境 | CAD-02 | 已解决 |
| SIG-RST-03 | 184 | 02:14 / 06:39 | RTL | RTL-04 | 审核 |
| SIG-TB-11 | 127 | 02:18 / 06:37 | 测试平台 | VER-07 | 修复已排队 |
| SIG-X-19 | 41 | 03:22 / 06:12 | 未解决 | VER-03 | 打开 |
设计 for Six Sigma
DMAIC 改进现有工程或制造流程。 DMADV structures 新 product and IC development.
定义 · 测量 · 分析 · Improve · 控制
定义 · 测量 · 分析 · 设计 · 校验
能力指数仅针对此已验证的工艺窗口显示,不替代发布标准。
角色-Aware Programme 智能
证据-backed views for executives, engineering disciplines, repeatability, verification and silicon learning.
工程 角色s and Programmes
选择角色以查看与该团队最相关的工作流程、仪表盘和运营效益。
Connect architecture, block 状态, verification closure and technical risk to current engineering evidence.
数据主权
Public demonstrations and 企业 deployment are deliberately separated.
Public demonstrations use sanitised data. 生产 PDKs, proprietary IP, 设计 databases and customer engineering information remain inside the customer-controlled environment. 部署 controls and air-gap suitability are validated per customer architecture, security policy and contract.
打开 Authenticated 企业版 演示工具无关的连接
设计ed to connect through approved APIs, scripts, reports, schedulers, version-control 系统s and engineering data exports.
集成 availability depends on tool licensing, customer environment, API 访问 and approved security policies. Vendor names identify eco系统 context and do not imply certified integration.
保留 工程 内容
原始半导体简报和提示材料现作为实用的工程输入模板支持该平台。
结构d prompts for architecture, power, verification and review.
六个受控输入:设计类型、实现策略、接口、功耗、已验证的工具上下文和所需工件。
规格、PVT、噪声、器件模型、仿真和证据字段。
RTL 层次结构、约束、提交、所有者、验证和签核上下文。
规划, tests, seeds, environment, coverage, waivers and defect context.
传感器 mechanism, multiphysics context, interface, characterisation and calibration.
证据 baseline, run manifests, DRC/LVS/PEX, waivers and human approvals.
签名、受影响的测试、首次出现、来源假设、所有者和解决方案证据。
Do not place NDA-restricted PDK content, proprietary schematics, export-controlled data, customer requirements or unreleased IP into a public AI service. AINNA outputs are engineering drafts and evidence aids. PDK-aware operation requires validated, authorised inputs. 合格 engineers retain 设计, verification and tape-out authority.
范围评估
从一个有边界的工程工作流、商定的证据边界和人工控制的成功标准开始。
实时 工程 上下文
Semicon-scoped taxonomy only: selected developments in semiconductor technology, IC 设计, EDA, verification, advanced packaging, 智能体 AI, CI and 边缘 AI, with concise explanations of their relevance to engineering teams.
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