mpw-007 / VIM2 Via 2-PLM

the VIM2 shapes of all of mpw-007

40 of 40 dies rendered.

40,825,665 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (34.2 MB in total, largest 1.4 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-007_VIM2_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-007_VIM2_reticle.png
the stitched reticle, full resolution
23.0 MB 8440a9f98e28aea6215b3f05bfb17c33a410be6a38013c5f61fb87f3e67aaf0d86c461d0 0e54bb7fe73ed15d937611811bfc8c8570f53f1649cc93c1e103e843f6134bd2
mpw-007_VIM2_reticle_overview.png
the same image scaled to fit 4096 px
861.7 kB 8695dd1535a06a88ad76e3bc595ecd37501bfcdc1f15366f3186c19ddf00946b26554fc9 6f6960126f58f815837e52aaf140668ec6135f11051c8bbbfd6349e86ea9daa7
mpw-007_VIM2_tiles.json
where every tile belongs, and its checksum
13.5 kB 5438f4fd6263dff71deb298aa310b9f31349f8ad57db923fd595e14fc1dc2f0e1b42cadd d55aae298c5b3c392ca2dd9011723ff7d7adfccb4de1b1b05006ca479a98bf1b
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 tinytapeout 7576×107760, 0 830.3 kB0ac60daba915fa9dbda24ede8bf89e9571456a02b6abfe79b0b72921930202ac
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 1.0 MBbddd1fae851953f62c84d44df2461cf2a93e7f84f3a0993f95329c4fc2a6bff4
A3 microwatt_mpw7 7576×1077615152, 0 1.4 MB912447280868a676b00aed2e2c2164395352de7dbbc1757a6c818ca8b5a74f12
A4 yonga-can_controller 7576×1077622728, 0 703.4 kBd8661e3f16fe2a2450c8e42690fa0225bd47b262ff9bac634a6cfa7278d9656a
A5 rocketalpha 7576×1077630304, 0 1.0 MBe65d1f6227501406d7a84721436e90c637fa68daeabd1dde70dc7d47e150fb7d
A6 riscduino-dcore_d3_3 7576×1077637880, 0 1.2 MBf720426336639b0f0dff73066330f097ba5210ffec5183ca9a8b3078bd8d89b1
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 1.2 MB834ce2663905d9f36e5dac5f9e33d081b438431f7c9ddd67b39d7cf1d4cc8733
B2 riscduino-score-s4-3 7576×107767576, 10776 1.1 MB7a281054da4a652f22129b2ff6b8e13ba696c3836c139da60e43558b3e8a31dc
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 533.6 kBf52bedef219eeb936c6bd210f801abaf5f81f9955ee2679737ec87d6e9530c67
B4 sramtestvehicleg3 7576×1077622728, 10776 657.3 kB2a78e19d1f1b99944b29107aba44912b87dfc4add3242b68f824ee77ad2219c8
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 666.0 kB2653064adc216d3bb88033bbe85981df5becdffc4fd55257a9a9eb9d78d3d6ff
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 729.7 kB13afd039cb0670e54b210fe078d4d9e5b4c7c5bb5eef774eff8a33e2db634e01
B7 reram_crossbar 7976×1077645456, 10776 879.7 kB8197d88c89583fe98e1a0a2a2191aed97de855a4aa545e039acfa37a138e71b2
C1 trainable-nn 7576×107760, 21552 955.9 kB97202054c7ccccc02fe32540e1ea0aba06fb685fe0137d503ee9083a2855eaab
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 664.5 kB3364cd020887ecdf8af747240279d39abf5d55d0835562ea22eeaf1d18552b46
C3 bitcoin_mining_asics 7576×1077615152, 21552 712.2 kB6fa5e96d39011d88e69b27c72274657c622fbf40aebe4e2c11f2344d4850296f
C4 yonga-mcu 7576×1077622728, 21552 956.9 kBaa666f07eb0a527a569b87849414c9fa26ab55bf02812fa60129448542b35dab
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 1.2 MB0989678c36789c2f213bc745cfef825e4930afff2cbc97e784c5c798a65690e6
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 1.1 MBfffd42d4f4c11dac8ee1b6a5bf13ed70bdaa55878d398f15405eed6481f5a656
D1 mixed_signal_circuits-jun13 7576×107760, 32328 654.2 kBb4f0c4ebdc49c24174a6780b82b26e7364117ecf7e8dd2f7c5ac46ccbfd843ce
D2 fpga_programming_management_unit 7576×107767576, 32328 765.0 kB23f3d2af0b41c41cb1be8e6bc59f5a2ea17f82409b7d475c6d570b8a08e7f2b9
D3 warp-v 7576×1077615152, 32328 748.5 kBb391f53656dcde36ccbc611036e4e85044ce709ac5b46f7ad3c36894298649f7
D4 leaf__mpw7_ 7576×1077622728, 32328 764.8 kBc1aa064125911d1f6c7b620984b86a9826e894151595f7cf9d7096ce97ac9835
D5 in_memory_computing_sram 7576×1077630304, 32328 701.0 kB8f034f93adae5e7c0ef28028713d382e0d68046119796b47bccd3727617a77aa
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 690.6 kB997b2a1cd88a618af69cde3432703d5f055f39601645670ec7653e85b5d313f4
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 694.2 kBcbd9ecec85262a672911ea949dda956c7d313d4a0723a99da38d3b47136bc439
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 706.0 kB1e604fa0e37ff7d8ce1e8d2767271ec5bf63c7e2481dc95a77888191f3150a5f
E2 in_memory_computing_rram 7576×107767576, 43104 756.0 kBc635100d5d0b8aba5d2bbd2f8489ab1ab9f40c792d46c050d2a4d8b99a49c5ea
E3 rram_imc_v2_flat 7576×1077615152, 43104 756.0 kBc635100d5d0b8aba5d2bbd2f8489ab1ab9f40c792d46c050d2a4d8b99a49c5ea
E4 topmetalse-dps 7576×1077622728, 43104 662.5 kBf5ae0bfa4d3066bfcdb95ee968f17b9934e3786f206aa5961d515729d2cdaa79
E5 chaos_automaton 7576×1077630304, 43104 1.2 MB41ed90b1782ce0a29cc981987df42cd9ec24209abe9f57ceb328ac4e7366a90a
E6 hehecore_resubmission 7576×1077637880, 43104 948.7 kBd6136feb1679a93d12bd3581e6ed98c104c9775a9e9d7ad5f4d96b84a80946e6
E7 waveform_generator 7976×1077645456, 43104 735.3 kB0bcd8c0952ce77eca692e6fa4a38104a49eb302444f353681625744d7792072b
F1 rift2core 7576×111760, 53880 721.3 kBbccca72d9cf18d1d2189ce5c68c4f0c5f39cf65afa0e3a9d4883f57d3f647c78
F2 rift2go_2300 7576×111767576, 53880 1.1 MBba4efad27177eec3481669094f59dd2cea77ea44c7f1a940885c9a3546865e41
F3 sloci_resubmission 7576×1117615152, 53880 665.3 kB8e954732d3356e68ebb8a2a0e2501b7d0ed3fa735a7d50a5bb989a86b79d01f3
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 695.4 kB4681988bd6e8c76e0bac8371996c5331084db761e03809493c3e0f63a0905b28
F5 crypto_aes128 7576×1117630304, 53880 754.6 kB1e4e809dafdb6697630930f32714e640bb55bfb8f202ae21b9d68ab303db6434
F6 graphics_controller 7576×1117637880, 53880 792.7 kB50c471e8a2be5d74a21e5878ea113fb742e4392da7f8f35617772d1618cd7bf5
F7 prga-test 7976×1117645456, 53880 894.5 kB5ad18a5310548b4ba0e9a72f5e45db1c562d1fc777ee7c25d1091b63ad3155b9

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About VIM2 on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask VIM2

Mask layer
VIM2 — Via 2-PLM
Process step
129 — Via2 mask
Drawn layer
69:44
Layer on the mask-layer GDS
44:0
Variants
Via 2-TNV, Via 2-S8TM, Via 2-PLM
Also
Via 2 (M2→M3)
Used in SKY130
yes

mpw-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.