{"id":221,"date":"2025-10-21T18:56:00","date_gmt":"2025-10-21T18:56:00","guid":{"rendered":"https:\/\/ykim.synology.me\/wordpress\/?p=221"},"modified":"2025-10-22T16:18:51","modified_gmt":"2025-10-22T16:18:51","slug":"practical-example-workflow-using-python-ml-to-replace-monte-carlo-for-sram-snm-variability","status":"publish","type":"post","link":"https:\/\/ykim.synology.me\/wordpress\/practical-example-workflow-using-python-ml-to-replace-monte-carlo-for-sram-snm-variability-221\/","title":{"rendered":"Practical example workflow, using Python ML to replace Monte Carlo for SRAM SNM variability"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">\ud83d\udd39 Problem Setup<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Goal:<\/strong> Predict SRAM cell <strong>Read SNM<\/strong> under process variation.<\/li>\n\n\n\n<li><strong>Traditional approach:<\/strong> Monte Carlo \u2192 run 100k SPICE simulations \u2192 slow.<\/li>\n\n\n\n<li><strong>AI-aided approach:<\/strong> Train ML model to learn the mapping from <strong>process parameters \u2192 SNM<\/strong>.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\">\ud83d\udd39 1. Collect Data<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Generate data using SPICE\/TCAD simulations (smaller Monte Carlo sample, e.g., 5k runs).<\/li>\n\n\n\n<li>Features (inputs):\n<ul class=\"wp-block-list\">\n<li>Vth of NMOS (\u0394Vtn)<\/li>\n\n\n\n<li>Vth of PMOS (\u0394Vtp)<\/li>\n\n\n\n<li>Channel length variation (\u0394L)<\/li>\n\n\n\n<li>Supply voltage (Vdd)<\/li>\n\n\n\n<li>Temperature (T)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Target (output):\n<ul class=\"wp-block-list\">\n<li>Read SNM (in mV).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h1 class=\"wp-block-heading\">\ud83d\udd39 2. Example Dataset Format<\/h1>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>\u0394Vtn (mV)<\/th><th>\u0394Vtp (mV)<\/th><th>\u0394L (nm)<\/th><th>Vdd (V)<\/th><th>Temp (\u00b0C)<\/th><th>SNM (mV)<\/th><\/tr><\/thead><tbody><tr><td>-30<\/td><td>+20<\/td><td>1.2<\/td><td>0.9<\/td><td>25<\/td><td>180<\/td><\/tr><tr><td>+15<\/td><td>-25<\/td><td>0.8<\/td><td>1.0<\/td><td>85<\/td><td>120<\/td><\/tr><tr><td>\u2026<\/td><td>\u2026<\/td><td>\u2026<\/td><td>\u2026<\/td><td>\u2026<\/td><td>\u2026<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h1 class=\"wp-block-heading\">\ud83d\udd39 3. Train ML Model (Python Example)<\/h1>\n\n\n\n<p class=\"wp-block-paragraph\">Here\u2019s a <strong>simplified Python workflow<\/strong> using <em>scikit-learn<\/em>:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.metrics import mean_absolute_error\n\n# Load dataset (collected from SPICE\/TCAD or silicon)\ndata = pd.read_csv(&quot;sram_variability.csv&quot;)\n\n# Features and target\nX = data&#x5B;&#x5B;&quot;DeltaVtn&quot;, &quot;DeltaVtp&quot;, &quot;DeltaL&quot;, &quot;Vdd&quot;, &quot;Temp&quot;]]\ny = data&#x5B;&quot;SNM&quot;]\n\n# Split into train\/test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Train ML model (Gradient Boosting works well for tabular data)\nmodel = GradientBoostingRegressor(n_estimators=300, max_depth=5)\nmodel.fit(X_train, y_train)\n\n# Evaluate\ny_pred = model.predict(X_test)\nmae = mean_absolute_error(y_test, y_pred)\nprint(f&quot;Mean Absolute Error: {mae:.2f} mV&quot;)\n\n# Example prediction\nexample = &#x5B;&#x5B;-20, 15, 1.0, 0.95, 25]]\nprint(&quot;Predicted SNM:&quot;, model.predict(example))\n<\/pre><\/div>\n\n\n<h1 class=\"wp-block-heading\">\ud83d\udd39 4. Deploy the AI Model<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Instead of running <strong>100k SPICE Monte Carlo simulations<\/strong>, you run <strong>only 5k to train<\/strong> the ML model.<\/li>\n\n\n\n<li>Then use the ML model to predict SNM for <strong>millions of variation samples instantly<\/strong>.<\/li>\n\n\n\n<li>You can even estimate <strong>yield<\/strong> by counting the % of predictions below a SNM failure threshold.<\/li>\n<\/ul>\n\n\n\n<h1 class=\"wp-block-heading\">\ud83d\udd39 5. Benefits<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Speed:<\/strong> Predictions in seconds instead of weeks of SPICE.<\/li>\n\n\n\n<li><strong>Coverage:<\/strong> Explore much larger variation space.<\/li>\n\n\n\n<li><strong>Accuracy:<\/strong> Within a few mV error compared to Monte Carlo SPICE (if trained properly).<\/li>\n\n\n\n<li><strong>Scalability:<\/strong> Same workflow applies to delay, leakage, reliability, etc.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\">\u2705 <strong>Summary:<\/strong><br>This is how <strong>AI replaces brute-force Monte Carlo<\/strong> in variability modeling. You simulate a smaller dataset, train an ML surrogate model, and then predict across the full variation space for <strong>fast yield estimation<\/strong>.<\/p>\n\n\n\n<div class=\"wp-block-jetpack-contact-form\">\n<div>\n\n<\/div>\n\n\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n<div style='text-align:center' class='yasr-auto-insert-overall'><\/div><div style='text-align:center' class='yasr-auto-insert-visitor'><\/div>","protected":false},"excerpt":{"rendered":"<p>\ud83d\udd39 Problem Setup \ud83d\udd39 1. Collect Data \ud83d\udd39 2. Example Dataset Format \u0394Vtn (mV) \u0394Vtp (mV) \u0394L (nm) Vdd (V) Temp (\u00b0C) SNM (mV) -30 +20 1.2 0.9 25 180 +15 -25 0.8 1.0 85 120 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \ud83d\udd39 3. Train ML Model (Python Example) Here\u2019s a simplified Python workflow using&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"_kadence_starter_templates_imported_post":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","fifu_image_url":"","fifu_image_alt":"","iawp_total_views":0,"footnotes":""},"categories":[18,4,6],"tags":[],"class_list":["post-221","post","type-post","status-publish","format-standard","hentry","category-ai-powered-slug","category-semiconductor-slug","category-sram-slug"],"yasr_visitor_votes":{"stars_attributes":{"read_only":false,"span_bottom":false},"number_of_votes":0,"sum_votes":0},"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts\/221","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/comments?post=221"}],"version-history":[{"count":23,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts\/221\/revisions"}],"predecessor-version":[{"id":330,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts\/221\/revisions\/330"}],"wp:attachment":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/media?parent=221"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/categories?post=221"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/tags?post=221"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}